Combined Yamamoto approach for simultaneous estimation of adsorption isotherm and kinetic parameters in ion-exchange chromatography.

PubMed

Rüdt, Matthias; Gillet, Florian; Heege, Stefanie; Hitzler, Julian; Kalbfuss, Bernd; Guélat, Bertrand

2015-09-25

Application of model-based design is appealing to support the development of protein chromatography in the biopharmaceutical industry. However, the required efforts for parameter estimation are frequently perceived as time-consuming and expensive. In order to speed-up this work, a new parameter estimation approach for modelling ion-exchange chromatography in linear conditions was developed. It aims at reducing the time and protein demand for the model calibration. The method combines the estimation of kinetic and thermodynamic parameters based on the simultaneous variation of the gradient slope and the residence time in a set of five linear gradient elutions. The parameters are estimated from a Yamamoto plot and a gradient-adjusted Van Deemter plot. The combined approach increases the information extracted per experiment compared to the individual methods. As a proof of concept, the combined approach was successfully applied for a monoclonal antibody on a cation-exchanger and for a Fc-fusion protein on an anion-exchange resin. The individual parameter estimations for the mAb confirmed that the new approach maintained the accuracy of the usual Yamamoto and Van Deemter plots. In the second case, offline size-exclusion chromatography was performed in order to estimate the thermodynamic parameters of an impurity (high molecular weight species) simultaneously with the main product. Finally, the parameters obtained from the combined approach were used in a lumped kinetic model to simulate the chromatography runs. The simulated chromatograms obtained for a wide range of gradient lengths and residence times showed only small deviations compared to the experimental data. Copyright © 2015 Elsevier B.V. All rights reserved.

Summary of tracking and identification methods

NASA Astrophysics Data System (ADS)

Blasch, Erik; Yang, Chun; Kadar, Ivan

2014-06-01

Over the last two decades, many solutions have arisen to combine target tracking estimation with classification methods. Target tracking includes developments from linear to non-linear and Gaussian to non-Gaussian processing. Pattern recognition includes detection, classification, recognition, and identification methods. Integrating tracking and pattern recognition has resulted in numerous approaches and this paper seeks to organize the various approaches. We discuss the terminology so as to have a common framework for various standards such as the NATO STANAG 4162 - Identification Data Combining Process. In a use case, we provide a comparative example highlighting that location information (as an example) with additional mission objectives from geographical, human, social, cultural, and behavioral modeling is needed to determine identification as classification alone does not allow determining identification or intent.

Protein fold recognition using geometric kernel data fusion.

PubMed

Zakeri, Pooya; Jeuris, Ben; Vandebril, Raf; Moreau, Yves

2014-07-01

Various approaches based on features extracted from protein sequences and often machine learning methods have been used in the prediction of protein folds. Finding an efficient technique for integrating these different protein features has received increasing attention. In particular, kernel methods are an interesting class of techniques for integrating heterogeneous data. Various methods have been proposed to fuse multiple kernels. Most techniques for multiple kernel learning focus on learning a convex linear combination of base kernels. In addition to the limitation of linear combinations, working with such approaches could cause a loss of potentially useful information. We design several techniques to combine kernel matrices by taking more involved, geometry inspired means of these matrices instead of convex linear combinations. We consider various sequence-based protein features including information extracted directly from position-specific scoring matrices and local sequence alignment. We evaluate our methods for classification on the SCOP PDB-40D benchmark dataset for protein fold recognition. The best overall accuracy on the protein fold recognition test set obtained by our methods is ∼ 86.7%. This is an improvement over the results of the best existing approach. Moreover, our computational model has been developed by incorporating the functional domain composition of proteins through a hybridization model. It is observed that by using our proposed hybridization model, the protein fold recognition accuracy is further improved to 89.30%. Furthermore, we investigate the performance of our approach on the protein remote homology detection problem by fusing multiple string kernels. The MATLAB code used for our proposed geometric kernel fusion frameworks are publicly available at http://people.cs.kuleuven.be/∼raf.vandebril/homepage/software/geomean.php?menu=5/. © The Author 2014. Published by Oxford University Press.

A penalized linear and nonlinear combined conjugate gradient method for the reconstruction of fluorescence molecular tomography.

PubMed

Shang, Shang; Bai, Jing; Song, Xiaolei; Wang, Hongkai; Lau, Jaclyn

2007-01-01

Conjugate gradient method is verified to be efficient for nonlinear optimization problems of large-dimension data. In this paper, a penalized linear and nonlinear combined conjugate gradient method for the reconstruction of fluorescence molecular tomography (FMT) is presented. The algorithm combines the linear conjugate gradient method and the nonlinear conjugate gradient method together based on a restart strategy, in order to take advantage of the two kinds of conjugate gradient methods and compensate for the disadvantages. A quadratic penalty method is adopted to gain a nonnegative constraint and reduce the illposedness of the problem. Simulation studies show that the presented algorithm is accurate, stable, and fast. It has a better performance than the conventional conjugate gradient-based reconstruction algorithms. It offers an effective approach to reconstruct fluorochrome information for FMT.

A class of stochastic optimization problems with one quadratic & several linear objective functions and extended portfolio selection model

NASA Astrophysics Data System (ADS)

Xu, Jiuping; Li, Jun

2002-09-01

In this paper a class of stochastic multiple-objective programming problems with one quadratic, several linear objective functions and linear constraints has been introduced. The former model is transformed into a deterministic multiple-objective nonlinear programming model by means of the introduction of random variables' expectation. The reference direction approach is used to deal with linear objectives and results in a linear parametric optimization formula with a single linear objective function. This objective function is combined with the quadratic function using the weighted sums. The quadratic problem is transformed into a linear (parametric) complementary problem, the basic formula for the proposed approach. The sufficient and necessary conditions for (properly, weakly) efficient solutions and some construction characteristics of (weakly) efficient solution sets are obtained. An interactive algorithm is proposed based on reference direction and weighted sums. Varying the parameter vector on the right-hand side of the model, the DM can freely search the efficient frontier with the model. An extended portfolio selection model is formed when liquidity is considered as another objective to be optimized besides expectation and risk. The interactive approach is illustrated with a practical example.

Robust stability of bidirectional associative memory neural networks with time delays

NASA Astrophysics Data System (ADS)

Park, Ju H.

2006-01-01

Based on the Lyapunov Krasovskii functionals combined with linear matrix inequality approach, a novel stability criterion is proposed for asymptotic stability of bidirectional associative memory neural networks with time delays. A novel delay-dependent stability criterion is given in terms of linear matrix inequalities, which can be solved easily by various optimization algorithms.

A General Linear Model Approach to Adjusting the Cumulative GPA.

ERIC Educational Resources Information Center

Young, John W.

A general linear model (GLM), using least-squares techniques, was used to develop a criterion measure to replace freshman year grade point average (GPA) in college admission predictive validity studies. Problems with the use of GPA include those associated with the combination of grades from different courses and disciplines into a single measure,…

Is 3D true non linear traveltime tomography reasonable ?

NASA Astrophysics Data System (ADS)

Herrero, A.; Virieux, J.

2003-04-01

The data sets requiring 3D analysis tools in the context of seismic exploration (both onshore and offshore experiments) or natural seismicity (micro seismicity surveys or post event measurements) are more and more numerous. Classical linearized tomographies and also earthquake localisation codes need an accurate 3D background velocity model. However, if the medium is complex and a priori information not available, a 1D analysis is not able to provide an adequate background velocity image. Moreover, the design of the acquisition layouts is often intrinsically 3D and renders difficult even 2D approaches, especially in natural seismicity cases. Thus, the solution relies on the use of a 3D true non linear approach, which allows to explore the model space and to identify an optimal velocity image. The problem becomes then practical and its feasibility depends on the available computing resources (memory and time). In this presentation, we show that facing a 3D traveltime tomography problem with an extensive non-linear approach combining fast travel time estimators based on level set methods and optimisation techniques such as multiscale strategy is feasible. Moreover, because management of inhomogeneous inversion parameters is more friendly in a non linear approach, we describe how to perform a jointly non-linear inversion for the seismic velocities and the sources locations.

Linear Power-Flow Models in Multiphase Distribution Networks: Preprint

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

Bernstein, Andrey; Dall'Anese, Emiliano

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

Combined analysis of magnetic and gravity anomalies using normalized source strength (NSS)

NASA Astrophysics Data System (ADS)

Li, L.; Wu, Y.

2017-12-01

Gravity field and magnetic field belong to potential fields which lead inherent multi-solution. Combined analysis of magnetic and gravity anomalies based on Poisson's relation is used to determinate homology gravity and magnetic anomalies and decrease the ambiguity. The traditional combined analysis uses the linear regression of the reduction to pole (RTP) magnetic anomaly to the first order vertical derivative of the gravity anomaly, and provides the quantitative or semi-quantitative interpretation by calculating the correlation coefficient, slope and intercept. In the calculation process, due to the effect of remanent magnetization, the RTP anomaly still contains the effect of oblique magnetization. In this case the homology gravity and magnetic anomalies display irrelevant results in the linear regression calculation. The normalized source strength (NSS) can be transformed from the magnetic tensor matrix, which is insensitive to the remanence. Here we present a new combined analysis using NSS. Based on the Poisson's relation, the gravity tensor matrix can be transformed into the pseudomagnetic tensor matrix of the direction of geomagnetic field magnetization under the homologous condition. The NSS of pseudomagnetic tensor matrix and original magnetic tensor matrix are calculated and linear regression analysis is carried out. The calculated correlation coefficient, slope and intercept indicate the homology level, Poisson's ratio and the distribution of remanent respectively. We test the approach using synthetic model under complex magnetization, the results show that it can still distinguish the same source under the condition of strong remanence, and establish the Poisson's ratio. Finally, this approach is applied in China. The results demonstrated that our approach is feasible.

Combining large number of weak biomarkers based on AUC.

PubMed

Yan, Li; Tian, Lili; Liu, Song

2015-12-20

Combining multiple biomarkers to improve diagnosis and/or prognosis accuracy is a common practice in clinical medicine. Both parametric and non-parametric methods have been developed for finding the optimal linear combination of biomarkers to maximize the area under the receiver operating characteristic curve (AUC), primarily focusing on the setting with a small number of well-defined biomarkers. This problem becomes more challenging when the number of observations is not order of magnitude greater than the number of variables, especially when the involved biomarkers are relatively weak. Such settings are not uncommon in certain applied fields. The first aim of this paper is to empirically evaluate the performance of existing linear combination methods under such settings. The second aim is to propose a new combination method, namely, the pairwise approach, to maximize AUC. Our simulation studies demonstrated that the performance of several existing methods can become unsatisfactory as the number of markers becomes large, while the newly proposed pairwise method performs reasonably well. Furthermore, we apply all the combination methods to real datasets used for the development and validation of MammaPrint. The implication of our study for the design of optimal linear combination methods is discussed. Copyright © 2015 John Wiley & Sons, Ltd.

Combining large number of weak biomarkers based on AUC

PubMed Central

Yan, Li; Tian, Lili; Liu, Song

2018-01-01

Combining multiple biomarkers to improve diagnosis and/or prognosis accuracy is a common practice in clinical medicine. Both parametric and non-parametric methods have been developed for finding the optimal linear combination of biomarkers to maximize the area under the receiver operating characteristic curve (AUC), primarily focusing on the setting with a small number of well-defined biomarkers. This problem becomes more challenging when the number of observations is not order of magnitude greater than the number of variables, especially when the involved biomarkers are relatively weak. Such settings are not uncommon in certain applied fields. The first aim of this paper is to empirically evaluate the performance of existing linear combination methods under such settings. The second aim is to propose a new combination method, namely, the pairwise approach, to maximize AUC. Our simulation studies demonstrated that the performance of several existing methods can become unsatisfactory as the number of markers becomes large, while the newly proposed pairwise method performs reasonably well. Furthermore, we apply all the combination methods to real datasets used for the development and validation of MammaPrint. The implication of our study for the design of optimal linear combination methods is discussed. PMID:26227901

A Learning Combination: Coaching with CLASS and the Project Approach

ERIC Educational Resources Information Center

Vartuli, Sue; Bolz, Carol; Wilson, Catherine

2014-01-01

The focus of this ongoing research is the effectiveness of coaching in improving the quality of teacher-child instructional interactions in Head Start classrooms. This study examines the relationship between two measures: Classroom Assessment Scoring System (CLASS) and a Project Approach Fidelity form developed by the authors. Linear regressions…

A linear circuit analysis program with stiff systems capability

NASA Technical Reports Server (NTRS)

Cook, C. H.; Bavuso, S. J.

1973-01-01

Several existing network analysis programs have been modified and combined to employ a variable topological approach to circuit translation. Efficient numerical integration techniques are used for transient analysis.

Adaptive rival penalized competitive learning and combined linear predictor model for financial forecast and investment.

PubMed

Cheung, Y M; Leung, W M; Xu, L

1997-01-01

We propose a prediction model called Rival Penalized Competitive Learning (RPCL) and Combined Linear Predictor method (CLP), which involves a set of local linear predictors such that a prediction is made by the combination of some activated predictors through a gating network (Xu et al., 1994). Furthermore, we present its improved variant named Adaptive RPCL-CLP that includes an adaptive learning mechanism as well as a data pre-and-post processing scheme. We compare them with some existing models by demonstrating their performance on two real-world financial time series--a China stock price and an exchange-rate series of US Dollar (USD) versus Deutschmark (DEM). Experiments have shown that Adaptive RPCL-CLP not only outperforms the other approaches with the smallest prediction error and training costs, but also brings in considerable high profits in the trading simulation of foreign exchange market.

A distributed lag approach to fitting non-linear dose-response models in particulate matter air pollution time series investigations.

PubMed

Roberts, Steven; Martin, Michael A

2007-06-01

The majority of studies that have investigated the relationship between particulate matter (PM) air pollution and mortality have assumed a linear dose-response relationship and have used either a single-day's PM or a 2- or 3-day moving average of PM as the measure of PM exposure. Both of these modeling choices have come under scrutiny in the literature, the linear assumption because it does not allow for non-linearities in the dose-response relationship, and the use of the single- or multi-day moving average PM measure because it does not allow for differential PM-mortality effects spread over time. These two problems have been dealt with on a piecemeal basis with non-linear dose-response models used in some studies and distributed lag models (DLMs) used in others. In this paper, we propose a method for investigating the shape of the PM-mortality dose-response relationship that combines a non-linear dose-response model with a DLM. This combined model will be shown to produce satisfactory estimates of the PM-mortality dose-response relationship in situations where non-linear dose response models and DLMs alone do not; that is, the combined model did not systemically underestimate or overestimate the effect of PM on mortality. The combined model is applied to ten cities in the US and a pooled dose-response model formed. When fitted with a change-point value of 60 microg/m(3), the pooled model provides evidence for a positive association between PM and mortality. The combined model produced larger estimates for the effect of PM on mortality than when using a non-linear dose-response model or a DLM in isolation. For the combined model, the estimated percentage increase in mortality for PM concentrations of 25 and 75 microg/m(3) were 3.3% and 5.4%, respectively. In contrast, the corresponding values from a DLM used in isolation were 1.2% and 3.5%, respectively.

A Hybrid Method for Opinion Finding Task (KUNLP at TREC 2008 Blog Track)

DTIC Science & Technology

2008-11-01

retrieve relevant documents. For the Opinion Retrieval subtask, we propose a hybrid model of lexicon-based approach and machine learning approach for...estimating and ranking the opinionated documents. For the Polarized Opinion Retrieval subtask, we employ machine learning for predicting the polarity...and linear combination technique for ranking polar documents. The hybrid model which utilize both lexicon-based approach and machine learning approach

Implementation of an approximate self-energy correction scheme in the orthogonalized linear combination of atomic orbitals method of band-structure calculations

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

Gu, Z.; Ching, W.Y.

Based on the Sterne-Inkson model for the self-energy correction to the single-particle energy in the local-density approximation (LDA), we have implemented an approximate energy-dependent and [bold k]-dependent [ital GW] correction scheme to the orthogonalized linear combination of atomic orbital-based local-density calculation for insulators. In contrast to the approach of Jenkins, Srivastava, and Inkson, we evaluate the on-site exchange integrals using the LDA Bloch functions throughout the Brillouin zone. By using a [bold k]-weighted band gap [ital E][sub [ital g

Interactive optimization approach for optimal impulsive rendezvous using primer vector and evolutionary algorithms

NASA Astrophysics Data System (ADS)

Luo, Ya-Zhong; Zhang, Jin; Li, Hai-yang; Tang, Guo-Jin

2010-08-01

In this paper, a new optimization approach combining primer vector theory and evolutionary algorithms for fuel-optimal non-linear impulsive rendezvous is proposed. The optimization approach is designed to seek the optimal number of impulses as well as the optimal impulse vectors. In this optimization approach, adding a midcourse impulse is determined by an interactive method, i.e. observing the primer-magnitude time history. An improved version of simulated annealing is employed to optimize the rendezvous trajectory with the fixed-number of impulses. This interactive approach is evaluated by three test cases: coplanar circle-to-circle rendezvous, same-circle rendezvous and non-coplanar rendezvous. The results show that the interactive approach is effective and efficient in fuel-optimal non-linear rendezvous design. It can guarantee solutions, which satisfy the Lawden's necessary optimality conditions.

Factorization-based texture segmentation

DOE PAGES

Yuan, Jiangye; Wang, Deliang; Cheriyadat, Anil M.

2015-06-17

This study introduces a factorization-based approach that efficiently segments textured images. We use local spectral histograms as features, and construct an M × N feature matrix using M-dimensional feature vectors in an N-pixel image. Based on the observation that each feature can be approximated by a linear combination of several representative features, we factor the feature matrix into two matrices-one consisting of the representative features and the other containing the weights of representative features at each pixel used for linear combination. The factorization method is based on singular value decomposition and nonnegative matrix factorization. The method uses local spectral histogramsmore » to discriminate region appearances in a computationally efficient way and at the same time accurately localizes region boundaries. Finally, the experiments conducted on public segmentation data sets show the promise of this simple yet powerful approach.« less

Intelligent Distributed Systems

DTIC Science & Technology

2015-10-23

periodic gossiping algorithms by using convex combination rules rather than standard averaging rules. On a ring graph, we have discovered how to sequence...the gossips within a period to achieve the best possible convergence rate and we have related this optimal value to the classic edge coloring problem...consensus. There are three different approaches to distributed averaging: linear iterations, gossiping , and dou- ble linear iterations which are also known as

Nonlinear flap-lag axial equations of a rotating beam

NASA Technical Reports Server (NTRS)

Kaza, K. R. V.; Kvaternik, R. G.

1977-01-01

It is possible to identify essentially four approaches by which analysts have established either the linear or nonlinear governing equations of motion for a particular problem related to the dynamics of rotating elastic bodies. The approaches include the effective applied load artifice in combination with a variational principle and the use of Newton's second law, written as D'Alembert's principle, applied to the deformed configuration. A third approach is a variational method in which nonlinear strain-displacement relations and a first-degree displacement field are used. The method introduced by Vigneron (1975) for deriving the linear flap-lag equations of a rotating beam constitutes the fourth approach. The reported investigation shows that all four approaches make use of the geometric nonlinear theory of elasticity. An alternative method for deriving the nonlinear coupled flap-lag-axial equations of motion is also discussed.

[Research on the methods for multi-class kernel CSP-based feature extraction].

PubMed

Wang, Jinjia; Zhang, Lingzhi; Hu, Bei

2012-04-01

To relax the presumption of strictly linear patterns in the common spatial patterns (CSP), we studied the kernel CSP (KCSP). A new multi-class KCSP (MKCSP) approach was proposed in this paper, which combines the kernel approach with multi-class CSP technique. In this approach, we used kernel spatial patterns for each class against all others, and extracted signal components specific to one condition from EEG data sets of multiple conditions. Then we performed classification using the Logistic linear classifier. Brain computer interface (BCI) competition III_3a was used in the experiment. Through the experiment, it can be proved that this approach could decompose the raw EEG singles into spatial patterns extracted from multi-class of single trial EEG, and could obtain good classification results.

On the stability and instantaneous velocity of grasped frictionless objects

NASA Technical Reports Server (NTRS)

Trinkle, Jeffrey C.

1992-01-01

A quantitative test for form closure valid for any number of contact points is formulated as a linear program, the optimal objective value of which provides a measure of how far a grasp is from losing form closure. Another contribution of the study is the formulation of a linear program whose solution yields the same information as the classical approach. The benefit of the formulation is that explicit testing of all possible combinations of contact interactions can be avoided by the algorithm used to solve the linear program.

Frequency assignments for HFDF receivers in a search and rescue network

NASA Astrophysics Data System (ADS)

Johnson, Krista E.

1990-03-01

This thesis applies a multiobjective linear programming approach to the problem of assigning frequencies to high frequency direction finding (HFDF) receivers in a search-and-rescue network in order to maximize the expected number of geolocations of vessels in distress. The problem is formulated as a multiobjective integer linear programming problem. The integrality of the solutions is guaranteed by the totally unimodularity of the A-matrix. Two approaches are taken to solve the multiobjective linear programming problem: (1) the multiobjective simplex method as implemented in ADBASE; and (2) an iterative approach. In this approach, the individual objective functions are weighted and combined in a single additive objective function. The resulting single objective problem is expressed as a network programming problem and solved using SAS NETFLOW. The process is then repeated with different weightings for the objective functions. The solutions obtained from the multiobjective linear programs are evaluated using a FORTRAN program to determine which solution provides the greatest expected number of geolocations. This solution is then compared to the sample mean and standard deviation for the expected number of geolocations resulting from 10,000 random frequency assignments for the network.

[Study on the early detection of Sclerotinia of Brassica napus based on combinational-stimulated bands].

PubMed

Liu, Fei; Feng, Lei; Lou, Bing-gan; Sun, Guang-ming; Wang, Lian-ping; He, Yong

2010-07-01

The combinational-stimulated bands were used to develop linear and nonlinear calibrations for the early detection of sclerotinia of oilseed rape (Brassica napus L.). Eighty healthy and 100 Sclerotinia leaf samples were scanned, and different preprocessing methods combined with successive projections algorithm (SPA) were applied to develop partial least squares (PLS) discriminant models, multiple linear regression (MLR) and least squares-support vector machine (LS-SVM) models. The results indicated that the optimal full-spectrum PLS model was achieved by direct orthogonal signal correction (DOSC), then De-trending and Raw spectra with correct recognition ratio of 100%, 95.7% and 95.7%, respectively. When using combinational-stimulated bands, the optimal linear models were SPA-MLR (DOSC) and SPA-PLS (DOSC) with correct recognition ratio of 100%. All SPA-LSSVM models using DOSC, De-trending and Raw spectra achieved perfect results with recognition of 100%. The overall results demonstrated that it was feasible to use combinational-stimulated bands for the early detection of Sclerotinia of oilseed rape, and DOSC-SPA was a powerful way for informative wavelength selection. This method supplied a new approach to the early detection and portable monitoring instrument of sclerotinia.

Range and azimuth resolution enhancement for 94 GHz real-beam radar

NASA Astrophysics Data System (ADS)

Liu, Guoqing; Yang, Ken; Sykora, Brian; Salha, Imad

2008-04-01

In this paper, two-dimensional (2D) (range and azimuth) resolution enhancement is investigated for millimeter wave (mmW) real-beam radar (RBR) with linear or non-linear antenna scan in the azimuth dimension. We design a new architecture of super resolution processing, in which a dual-mode approach is used for defining region of interest for 2D resolution enhancement and a combined approach is deployed for obtaining accurate location and amplitude estimations of targets within the region of interest. To achieve 2D resolution enhancement, we first adopt the Capon Beamformer (CB) approach (also known as the minimum variance method (MVM)) to enhance range resolution. A generalized CB (GCB) approach is then applied to azimuth dimension for azimuth resolution enhancement. The GCB approach does not rely on whether the azimuth sampling is even or not and thus can be used in both linear and non-linear antenna scanning modes. The effectiveness of the resolution enhancement is demonstrated by using both simulation and test data. The results of using a 94 GHz real-beam frequency modulation continuous wave (FMCW) radar data show that the overall image quality is significantly improved per visual evaluation and comparison with respect to the original real-beam radar image.

Locality-preserving sparse representation-based classification in hyperspectral imagery

NASA Astrophysics Data System (ADS)

Gao, Lianru; Yu, Haoyang; Zhang, Bing; Li, Qingting

2016-10-01

This paper proposes to combine locality-preserving projections (LPP) and sparse representation (SR) for hyperspectral image classification. The LPP is first used to reduce the dimensionality of all the training and testing data by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold, where the high-dimensional data lies. Then, SR codes the projected testing pixels as sparse linear combinations of all the training samples to classify the testing pixels by evaluating which class leads to the minimum approximation error. The integration of LPP and SR represents an innovative contribution to the literature. The proposed approach, called locality-preserving SR-based classification, addresses the imbalance between high dimensionality of hyperspectral data and the limited number of training samples. Experimental results on three real hyperspectral data sets demonstrate that the proposed approach outperforms the original counterpart, i.e., SR-based classification.

Efficient Transition State Optimization of Periodic Structures through Automated Relaxed Potential Energy Surface Scans.

PubMed

Plessow, Philipp N

2018-02-13

This work explores how constrained linear combinations of bond lengths can be used to optimize transition states in periodic structures. Scanning of constrained coordinates is a standard approach for molecular codes with localized basis functions, where a full set of internal coordinates is used for optimization. Common plane wave-codes for periodic boundary conditions almost exlusively rely on Cartesian coordinates. An implementation of constrained linear combinations of bond lengths with Cartesian coordinates is described. Along with an optimization of the value of the constrained coordinate toward the transition states, this allows transition optimization within a single calculation. The approach is suitable for transition states that can be well described in terms of broken and formed bonds. In particular, the implementation is shown to be effective and efficient in the optimization of transition states in zeolite-catalyzed reactions, which have high relevance in industrial processes.

Cosmological constraints from the CFHTLenS shear measurements using a new, accurate, and flexible way of predicting non-linear mass clustering

NASA Astrophysics Data System (ADS)

Angulo, Raul E.; Hilbert, Stefan

2015-03-01

We explore the cosmological constraints from cosmic shear using a new way of modelling the non-linear matter correlation functions. The new formalism extends the method of Angulo & White, which manipulates outputs of N-body simulations to represent the 3D non-linear mass distribution in different cosmological scenarios. We show that predictions from our approach for shear two-point correlations at 1-300 arcmin separations are accurate at the ˜10 per cent level, even for extreme changes in cosmology. For moderate changes, with target cosmologies similar to that preferred by analyses of recent Planck data, the accuracy is close to ˜5 per cent. We combine this approach with a Monte Carlo Markov chain sampler to explore constraints on a Λ cold dark matter model from the shear correlation functions measured in the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS). We obtain constraints on the parameter combination σ8(Ωm/0.27)0.6 = 0.801 ± 0.028. Combined with results from cosmic microwave background data, we obtain marginalized constraints on σ8 = 0.81 ± 0.01 and Ωm = 0.29 ± 0.01. These results are statistically compatible with previous analyses, which supports the validity of our approach. We discuss the advantages of our method and the potential it offers, including a path to model in detail (i) the effects of baryons, (ii) high-order shear correlation functions, and (iii) galaxy-galaxy lensing, among others, in future high-precision cosmological analyses.

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

PubMed

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

2016-01-01

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

Combined non-parametric and parametric approach for identification of time-variant systems

NASA Astrophysics Data System (ADS)

Dziedziech, Kajetan; Czop, Piotr; Staszewski, Wieslaw J.; Uhl, Tadeusz

2018-03-01

Identification of systems, structures and machines with variable physical parameters is a challenging task especially when time-varying vibration modes are involved. The paper proposes a new combined, two-step - i.e. non-parametric and parametric - modelling approach in order to determine time-varying vibration modes based on input-output measurements. Single-degree-of-freedom (SDOF) vibration modes from multi-degree-of-freedom (MDOF) non-parametric system representation are extracted in the first step with the use of time-frequency wavelet-based filters. The second step involves time-varying parametric representation of extracted modes with the use of recursive linear autoregressive-moving-average with exogenous inputs (ARMAX) models. The combined approach is demonstrated using system identification analysis based on the experimental mass-varying MDOF frame-like structure subjected to random excitation. The results show that the proposed combined method correctly captures the dynamics of the analysed structure, using minimum a priori information on the model.

Combining global and local approximations

NASA Technical Reports Server (NTRS)

Haftka, Raphael T.

1991-01-01

A method based on a linear approximation to a scaling factor, designated the 'global-local approximation' (GLA) method, is presented and shown capable of extending the range of usefulness of derivative-based approximations to a more refined model. The GLA approach refines the conventional scaling factor by means of a linearly varying, rather than constant, scaling factor. The capabilities of the method are demonstrated for a simple beam example with a crude and more refined FEM model.

Is the linear modeling technique good enough for optimal form design? A comparison of quantitative analysis models.

PubMed

Lin, Yang-Cheng; Yeh, Chung-Hsing; Wang, Chen-Cheng; Wei, Chun-Chun

2012-01-01

How to design highly reputable and hot-selling products is an essential issue in product design. Whether consumers choose a product depends largely on their perception of the product image. A consumer-oriented design approach presented in this paper helps product designers incorporate consumers' perceptions of product forms in the design process. The consumer-oriented design approach uses quantification theory type I, grey prediction (the linear modeling technique), and neural networks (the nonlinear modeling technique) to determine the optimal form combination of product design for matching a given product image. An experimental study based on the concept of Kansei Engineering is conducted to collect numerical data for examining the relationship between consumers' perception of product image and product form elements of personal digital assistants (PDAs). The result of performance comparison shows that the QTTI model is good enough to help product designers determine the optimal form combination of product design. Although the PDA form design is used as a case study, the approach is applicable to other consumer products with various design elements and product images. The approach provides an effective mechanism for facilitating the consumer-oriented product design process.

Is the Linear Modeling Technique Good Enough for Optimal Form Design? A Comparison of Quantitative Analysis Models

PubMed Central

Lin, Yang-Cheng; Yeh, Chung-Hsing; Wang, Chen-Cheng; Wei, Chun-Chun

2012-01-01

How to design highly reputable and hot-selling products is an essential issue in product design. Whether consumers choose a product depends largely on their perception of the product image. A consumer-oriented design approach presented in this paper helps product designers incorporate consumers' perceptions of product forms in the design process. The consumer-oriented design approach uses quantification theory type I, grey prediction (the linear modeling technique), and neural networks (the nonlinear modeling technique) to determine the optimal form combination of product design for matching a given product image. An experimental study based on the concept of Kansei Engineering is conducted to collect numerical data for examining the relationship between consumers' perception of product image and product form elements of personal digital assistants (PDAs). The result of performance comparison shows that the QTTI model is good enough to help product designers determine the optimal form combination of product design. Although the PDA form design is used as a case study, the approach is applicable to other consumer products with various design elements and product images. The approach provides an effective mechanism for facilitating the consumer-oriented product design process. PMID:23258961

Linear Combination of Heuristics Approach to Spatial Sampling Hyperspectral Data for Target Tracking

DTIC Science & Technology

2010-12-01

Figure 37 - Illustration of the tunable spectral polarimeter. ........................................... 154 Figure 38 - Illustration of micromirrors ...polarimeter. 9.2 Multiobject Tracking Spectrometer The idea of combining an array of MEMS micromirrors with an imager and a spectrometer array is the... micromirror array is located at an intermediate focal plane of the optical system. If all the individual mirrors are turned in the same direction

Cross-validation analysis for genetic evaluation models for ranking in endurance horses.

PubMed

García-Ballesteros, S; Varona, L; Valera, M; Gutiérrez, J P; Cervantes, I

2018-01-01

Ranking trait was used as a selection criterion for competition horses to estimate racing performance. In the literature the most common approaches to estimate breeding values are the linear or threshold statistical models. However, recent studies have shown that a Thurstonian approach was able to fix the race effect (competitive level of the horses that participate in the same race), thus suggesting a better prediction accuracy of breeding values for ranking trait. The aim of this study was to compare the predictability of linear, threshold and Thurstonian approaches for genetic evaluation of ranking in endurance horses. For this purpose, eight genetic models were used for each approach with different combinations of random effects: rider, rider-horse interaction and environmental permanent effect. All genetic models included gender, age and race as systematic effects. The database that was used contained 4065 ranking records from 966 horses and that for the pedigree contained 8733 animals (47% Arabian horses), with an estimated heritability around 0.10 for the ranking trait. The prediction ability of the models for racing performance was evaluated using a cross-validation approach. The average correlation between real and predicted performances across genetic models was around 0.25 for threshold, 0.58 for linear and 0.60 for Thurstonian approaches. Although no significant differences were found between models within approaches, the best genetic model included: the rider and rider-horse random effects for threshold, only rider and environmental permanent effects for linear approach and all random effects for Thurstonian approach. The absolute correlations of predicted breeding values among models were higher between threshold and Thurstonian: 0.90, 0.91 and 0.88 for all animals, top 20% and top 5% best animals. For rank correlations these figures were 0.85, 0.84 and 0.86. The lower values were those between linear and threshold approaches (0.65, 0.62 and 0.51). In conclusion, the Thurstonian approach is recommended for the routine genetic evaluations for ranking in endurance horses.

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

PubMed

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

2017-08-24

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

Quasi- and pseudo-maximum likelihood estimators for discretely observed continuous-time Markov branching processes

PubMed Central

Chen, Rui; Hyrien, Ollivier

2011-01-01

This article deals with quasi- and pseudo-likelihood estimation in a class of continuous-time multi-type Markov branching processes observed at discrete points in time. “Conventional” and conditional estimation are discussed for both approaches. We compare their properties and identify situations where they lead to asymptotically equivalent estimators. Both approaches possess robustness properties, and coincide with maximum likelihood estimation in some cases. Quasi-likelihood functions involving only linear combinations of the data may be unable to estimate all model parameters. Remedial measures exist, including the resort either to non-linear functions of the data or to conditioning the moments on appropriate sigma-algebras. The method of pseudo-likelihood may also resolve this issue. We investigate the properties of these approaches in three examples: the pure birth process, the linear birth-and-death process, and a two-type process that generalizes the previous two examples. Simulations studies are conducted to evaluate performance in finite samples. PMID:21552356

A Thermodynamic Theory Of Solid Viscoelasticity. Part 1: Linear Viscoelasticity.

NASA Technical Reports Server (NTRS)

Freed, Alan D.; Leonov, Arkady I.

2002-01-01

The present series of three consecutive papers develops a general theory for linear and finite solid viscoelasticity. Because the most important object for nonlinear studies are rubber-like materials, the general approach is specified in a form convenient for solving problems important for many industries that involve rubber-like materials. General linear and nonlinear theories for non-isothermal deformations of viscoelastic solids are developed based on the quasi-linear approach of non-equilibrium thermodynamics. In this, the first paper of the series, we analyze non-isothermal linear viscoelasticity, which is applicable in a range of small strains not only to all synthetic polymers and bio-polymers but also to some non-polymeric materials. Although the linear case seems to be well developed, there still are some reasons to implement a thermodynamic derivation of constitutive equations for solid-like, non-isothermal, linear viscoelasticity. The most important is the thermodynamic modeling of thermo-rheological complexity , i.e. different temperature dependences of relaxation parameters in various parts of relaxation spectrum. A special structure of interaction matrices is established for different physical mechanisms contributed to the normal relaxation modes. This structure seems to be in accord with observations, and creates a simple mathematical framework for both continuum and molecular theories of the thermo-rheological complex relaxation phenomena. Finally, a unified approach is briefly discussed that, in principle, allows combining both the long time (discrete) and short time (continuous) descriptions of relaxation behaviors for polymers in the rubbery and glassy regions.

General linear methods and friends: Toward efficient solutions of multiphysics problems

NASA Astrophysics Data System (ADS)

Sandu, Adrian

2017-07-01

Time dependent multiphysics partial differential equations are of great practical importance as they model diverse phenomena that appear in mechanical and chemical engineering, aeronautics, astrophysics, meteorology and oceanography, financial modeling, environmental sciences, etc. There is no single best time discretization for the complex multiphysics systems of practical interest. We discuss "multimethod" approaches that combine different time steps and discretizations using the rigourous frameworks provided by Partitioned General Linear Methods and Generalize-structure Additive Runge Kutta Methods..

Credibility analysis of risk classes by generalized linear model

NASA Astrophysics Data System (ADS)

Erdemir, Ovgucan Karadag; Sucu, Meral

2016-06-01

In this paper generalized linear model (GLM) and credibility theory which are frequently used in nonlife insurance pricing are combined for reliability analysis. Using full credibility standard, GLM is associated with limited fluctuation credibility approach. Comparison criteria such as asymptotic variance and credibility probability are used to analyze the credibility of risk classes. An application is performed by using one-year claim frequency data of a Turkish insurance company and results of credible risk classes are interpreted.

Biomarker selection for medical diagnosis using the partial area under the ROC curve

PubMed Central

2014-01-01

Background A biomarker is usually used as a diagnostic or assessment tool in medical research. Finding an ideal biomarker is not easy and combining multiple biomarkers provides a promising alternative. Moreover, some biomarkers based on the optimal linear combination do not have enough discriminatory power. As a result, the aim of this study was to find the significant biomarkers based on the optimal linear combination maximizing the pAUC for assessment of the biomarkers. Methods Under the binormality assumption we obtain the optimal linear combination of biomarkers maximizing the partial area under the receiver operating characteristic curve (pAUC). Related statistical tests are developed for assessment of a biomarker set and of an individual biomarker. Stepwise biomarker selections are introduced to identify those biomarkers of statistical significance. Results The results of simulation study and three real examples, Duchenne Muscular Dystrophy disease, heart disease, and breast tissue example are used to show that our methods are most suitable biomarker selection for the data sets of a moderate number of biomarkers. Conclusions Our proposed biomarker selection approaches can be used to find the significant biomarkers based on hypothesis testing. PMID:24410929

Fast correction approach for wavefront sensorless adaptive optics based on a linear phase diversity technique.

PubMed

Yue, Dan; Nie, Haitao; Li, Ye; Ying, Changsheng

2018-03-01

Wavefront sensorless (WFSless) adaptive optics (AO) systems have been widely studied in recent years. To reach optimum results, such systems require an efficient correction method. This paper presents a fast wavefront correction approach for a WFSless AO system mainly based on the linear phase diversity (PD) technique. The fast closed-loop control algorithm is set up based on the linear relationship between the drive voltage of the deformable mirror (DM) and the far-field images of the system, which is obtained through the linear PD algorithm combined with the influence function of the DM. A large number of phase screens under different turbulence strengths are simulated to test the performance of the proposed method. The numerical simulation results show that the method has fast convergence rate and strong correction ability, a few correction times can achieve good correction results, and can effectively improve the imaging quality of the system while needing fewer measurements of CCD data.

A new line-of-sight approach to the non-linear Cosmic Microwave Background

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

Fidler, Christian; Koyama, Kazuya; Pettinari, Guido W., E-mail: christian.fidler@port.ac.uk, E-mail: kazuya.koyama@port.ac.uk, E-mail: guido.pettinari@gmail.com

2015-04-01

We develop the transport operator formalism, a new line-of-sight integration framework to calculate the anisotropies of the Cosmic Microwave Background (CMB) at the linear and non-linear level. This formalism utilises a transformation operator that removes all inhomogeneous propagation effects acting on the photon distribution function, thus achieving a split between perturbative collisional effects at recombination and non-perturbative line-of-sight effects at later times. The former can be computed in the framework of standard cosmological perturbation theory with a second-order Boltzmann code such as SONG, while the latter can be treated within a separate perturbative scheme allowing the use of non-linear Newtonianmore » potentials. We thus provide a consistent framework to compute all physical effects contained in the Boltzmann equation and to combine the standard remapping approach with Boltzmann codes at any order in perturbation theory, without assuming that all sources are localised at recombination.« less

Analytical optimal pulse shapes obtained with the aid of genetic algorithms

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

Guerrero, Rubén D., E-mail: rdguerrerom@unal.edu.co; Arango, Carlos A.; Reyes, Andrés

2015-09-28

We propose a methodology to design optimal pulses for achieving quantum optimal control on molecular systems. Our approach constrains pulse shapes to linear combinations of a fixed number of experimentally relevant pulse functions. Quantum optimal control is obtained by maximizing a multi-target fitness function using genetic algorithms. As a first application of the methodology, we generated an optimal pulse that successfully maximized the yield on a selected dissociation channel of a diatomic molecule. Our pulse is obtained as a linear combination of linearly chirped pulse functions. Data recorded along the evolution of the genetic algorithm contained important information regarding themore » interplay between radiative and diabatic processes. We performed a principal component analysis on these data to retrieve the most relevant processes along the optimal path. Our proposed methodology could be useful for performing quantum optimal control on more complex systems by employing a wider variety of pulse shape functions.« less

How linear response shaped models of neural circuits and the quest for alternatives.

PubMed

Herfurth, Tim; Tchumatchenko, Tatjana

2017-10-01

In the past decades, many mathematical approaches to solve complex nonlinear systems in physics have been successfully applied to neuroscience. One of these tools is the concept of linear response functions. However, phenomena observed in the brain emerge from fundamentally nonlinear interactions and feedback loops rather than from a composition of linear filters. Here, we review the successes achieved by applying the linear response formalism to topics, such as rhythm generation and synchrony and by incorporating it into models that combine linear and nonlinear transformations. We also discuss the challenges encountered in the linear response applications and argue that new theoretical concepts are needed to tackle feedback loops and non-equilibrium dynamics which are experimentally observed in neural networks but are outside of the validity regime of the linear response formalism. Copyright © 2017 Elsevier Ltd. All rights reserved.

Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate

NASA Astrophysics Data System (ADS)

Hobeichi, Sanaa; Abramowitz, Gab; Evans, Jason; Ukkola, Anna

2018-02-01

Accurate global gridded estimates of evapotranspiration (ET) are key to understanding water and energy budgets, in addition to being required for model evaluation. Several gridded ET products have already been developed which differ in their data requirements, the approaches used to derive them and their estimates, yet it is not clear which provides the most reliable estimates. This paper presents a new global ET dataset and associated uncertainty with monthly temporal resolution for 2000-2009. Six existing gridded ET products are combined using a weighting approach trained by observational datasets from 159 FLUXNET sites. The weighting method is based on a technique that provides an analytically optimal linear combination of ET products compared to site data and accounts for both the performance differences and error covariance between the participating ET products. We examine the performance of the weighting approach in several in-sample and out-of-sample tests that confirm that point-based estimates of flux towers provide information on the grid scale of these products. We also provide evidence that the weighted product performs better than its six constituent ET product members in four common metrics. Uncertainty in the ET estimate is derived by rescaling the spread of participating ET products so that their spread reflects the ability of the weighted mean estimate to match flux tower data. While issues in observational data and any common biases in participating ET datasets are limitations to the success of this approach, future datasets can easily be incorporated and enhance the derived product.

Assessing the impact of non-tidal atmospheric loading on a Kalman filter-based terrestrial reference frame

NASA Astrophysics Data System (ADS)

Abbondanza, Claudio; Altamimi, Zuheir; Chin, Toshio; Collilieux, Xavier; Dach, Rolf; Gross, Richard; Heflin, Michael; König, Rolf; Lemoine, Frank; Macmillan, Dan; Parker, Jay; van Dam, Tonie; Wu, Xiaoping

2014-05-01

The International Terrestrial Reference Frame (ITRF) adopts a piece-wise linear model to parameterize regularized station positions and velocities. The space-geodetic (SG) solutions from VLBI, SLR, GPS and DORIS used as input in the ITRF combination process account for tidal loading deformations, but ignore the non-tidal part. As a result, the non-linear signal observed in the time series of SG-derived station positions in part reflects non-tidal loading displacements not introduced in the SG data reduction. In this analysis, we assess the impact of non-tidal atmospheric loading (NTAL) corrections on the TRF computation. Focusing on the a-posteriori approach, (i) the NTAL model derived from the National Centre for Environmental Prediction (NCEP) surface pressure is removed from the SINEX files of the SG solutions used as inputs to the TRF determinations; (ii) adopting a Kalman-filter based approach, two distinct linear TRFs are estimated combining the 4 SG solutions with (corrected TRF solution) and without the NTAL displacements (standard TRF solution). Linear fits (offset and atmospheric velocity) of the NTAL displacements removed during step (i) are estimated accounting for the station position discontinuities introduced in the SG solutions and adopting different weighting strategies. The NTAL-derived (atmospheric) velocity fields are compared to those obtained from the TRF reductions during step (ii). The consistency between the atmospheric and the TRF-derived velocity fields is examined. We show how the presence of station position discontinuities in SG solutions degrades the agreement between the velocity fields and compare the effect of different weighting structure adopted while estimating the linear fits to the NTAL displacements. Finally, we evaluate the effect of restoring the atmospheric velocities determined through the linear fits of the NTAL displacements to the single-technique linear reference frames obtained by stacking the standard SG SINEX files. Differences between the velocity fields obtained restoring the NTAL displacements and the standard stacked linear reference frames are discussed.

A geometric approach to non-linear correlations with intrinsic scatter

NASA Astrophysics Data System (ADS)

Pihajoki, Pauli

2017-12-01

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

Free and Forced Vibrations of Thick-Walled Anisotropic Cylindrical Shells

NASA Astrophysics Data System (ADS)

Marchuk, A. V.; Gnedash, S. V.; Levkovskii, S. A.

2017-03-01

Two approaches to studying the free and forced axisymmetric vibrations of cylindrical shell are proposed. They are based on the three-dimensional theory of elasticity and division of the original cylindrical shell with concentric cross-sectional circles into several coaxial cylindrical shells. One approach uses linear polynomials to approximate functions defined in plan and across the thickness. The other approach also uses linear polynomials to approximate functions defined in plan, but their variation with thickness is described by the analytical solution of a system of differential equations. Both approaches have approximation and arithmetic errors. When determining the natural frequencies by the semi-analytical finite-element method in combination with the divide and conqure method, it is convenient to find the initial frequencies by the finite-element method. The behavior of the shell during free and forced vibrations is analyzed in the case where the loading area is half the shell thickness

Study on static and dynamic characteristics of moving magnet linear compressors

NASA Astrophysics Data System (ADS)

Chen, N.; Tang, Y. J.; Wu, Y. N.; Chen, X.; Xu, L.

2007-09-01

With the development of high-strength NdFeB magnetic material, moving magnet linear compressors have been gradually introduced in the fields of refrigeration and cryogenic engineering, especially in Stirling and pulse tube cryocoolers. This paper presents simulation and experimental investigations on the static and dynamic characteristics of a moving magnet linear motor and a moving magnet linear compressor. Both equivalent magnetic circuits and finite element approaches have been used to model the moving magnet linear motor. Subsequently, the force and equilibrium characteristics of the linear motor have been predicted and verified by detailed static experimental analyses. In combination with a harmonic analysis, experimental investigations were conducted on a prototype of a moving magnet linear compressor. A voltage-stroke relationship, the effect of charging pressure on the performance and dynamic frequency response characteristics are investigated. Finally, the method to identify optimal points of the linear compressor has been described, which is indispensable to the design and operation of moving magnet linear compressors.

AN EFFICIENT HIGHER-ORDER FAST MULTIPOLE BOUNDARY ELEMENT SOLUTION FOR POISSON-BOLTZMANN BASED MOLECULAR ELECTROSTATICS

PubMed Central

Bajaj, Chandrajit; Chen, Shun-Chuan; Rand, Alexander

2011-01-01

In order to compute polarization energy of biomolecules, we describe a boundary element approach to solving the linearized Poisson-Boltzmann equation. Our approach combines several important features including the derivative boundary formulation of the problem and a smooth approximation of the molecular surface based on the algebraic spline molecular surface. State of the art software for numerical linear algebra and the kernel independent fast multipole method is used for both simplicity and efficiency of our implementation. We perform a variety of computational experiments, testing our method on a number of actual proteins involved in molecular docking and demonstrating the effectiveness of our solver for computing molecular polarization energy. PMID:21660123

KALREF—A Kalman filter and time series approach to the International Terrestrial Reference Frame realization

NASA Astrophysics Data System (ADS)

Wu, Xiaoping; Abbondanza, Claudio; Altamimi, Zuheir; Chin, T. Mike; Collilieux, Xavier; Gross, Richard S.; Heflin, Michael B.; Jiang, Yan; Parker, Jay W.

2015-05-01

The current International Terrestrial Reference Frame is based on a piecewise linear site motion model and realized by reference epoch coordinates and velocities for a global set of stations. Although linear motions due to tectonic plates and glacial isostatic adjustment dominate geodetic signals, at today's millimeter precisions, nonlinear motions due to earthquakes, volcanic activities, ice mass losses, sea level rise, hydrological changes, and other processes become significant. Monitoring these (sometimes rapid) changes desires consistent and precise realization of the terrestrial reference frame (TRF) quasi-instantaneously. Here, we use a Kalman filter and smoother approach to combine time series from four space geodetic techniques to realize an experimental TRF through weekly time series of geocentric coordinates. In addition to secular, periodic, and stochastic components for station coordinates, the Kalman filter state variables also include daily Earth orientation parameters and transformation parameters from input data frames to the combined TRF. Local tie measurements among colocated stations are used at their known or nominal epochs of observation, with comotion constraints applied to almost all colocated stations. The filter/smoother approach unifies different geodetic time series in a single geocentric frame. Fragmented and multitechnique tracking records at colocation sites are bridged together to form longer and coherent motion time series. While the time series approach to TRF reflects the reality of a changing Earth more closely than the linear approximation model, the filter/smoother is computationally powerful and flexible to facilitate incorporation of other data types and more advanced characterization of stochastic behavior of geodetic time series.

Linear combination of atomic orbitals calculation of the Auger neutralization rate of He{sup +} on Al(111) (100), and (110) surfaces

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

Valdes, Diego; Blanco, J.M.; Monreal, R.C.

2005-06-15

We develop a theory of the Auger neutralization rate of ions on solid surfaces in which the matrix elements for the transition are calculated by means of a linear combination of atomic orbitals technique. We apply the theory to the calculation of the Auger rate of He{sup +} on unreconstructed Al(111) (100), and (110) surfaces, assuming He{sup +} to approach these surfaces on high symmetry positions and compare them with the results of the jellium model. Although there are substantial differences between the Auger rates calculated with both kinds of approaches, those differences tend to compensate when evaluating the integralmore » along the ion trajectory and, consequently, are of minor influence in some physical magnitudes like the ion survival probability for perpendicular energies larger than 100 eV. We find that many atoms contribute to the Auger process and small effects of lateral corrugation are registered.« less

Combined control-structure optimization

NASA Technical Reports Server (NTRS)

Salama, M.; Milman, M.; Bruno, R.; Scheid, R.; Gibson, S.

1989-01-01

An approach for combined control-structure optimization keyed to enhancing early design trade-offs is outlined and illustrated by numerical examples. The approach employs a homotopic strategy and appears to be effective for generating families of designs that can be used in these early trade studies. Analytical results were obtained for classes of structure/control objectives with linear quadratic Gaussian (LQG) and linear quadratic regulator (LQR) costs. For these, researchers demonstrated that global optima can be computed for small values of the homotopy parameter. Conditions for local optima along the homotopy path were also given. Details of two numerical examples employing the LQR control cost were given showing variations of the optimal design variables along the homotopy path. The results of the second example suggest that introducing a second homotopy parameter relating the two parts of the control index in the LQG/LQR formulation might serve to enlarge the family of Pareto optima, but its effect on modifying the optimal structural shapes may be analogous to the original parameter lambda.

Optimization of composite box-beam structures including effects of subcomponent interactions

NASA Technical Reports Server (NTRS)

Ragon, Scott A.; Guerdal, Zafer; Starnes, James H., Jr.

1995-01-01

Minimum mass designs are obtained for a simple box beam structure subject to bending, torque and combined bending/torque load cases. These designs are obtained subject to point strain and linear buckling constraints. The present work differs from previous efforts in that special attention is payed to including the effects of subcomponent panel interaction in the optimal design process. Two different approaches are used to impose the buckling constraints. When the global approach is used, buckling constraints are imposed on the global structure via a linear eigenvalue analysis. This approach allows the subcomponent panels to interact in a realistic manner. The results obtained using this approach are compared to results obtained using a traditional, less expensive approach, called the local approach. When the local approach is used, in-plane loads are extracted from the global model and used to impose buckling constraints on each subcomponent panel individually. In the global cases, it is found that there can be significant interaction between skin, spar, and rib design variables. This coupling is weak or nonexistent in the local designs. It is determined that weight savings of up to 7% may be obtained by using the global approach instead of the local approach to design these structures. Several of the designs obtained using the linear buckling analysis are subjected to a geometrically nonlinear analysis. For the designs which were subjected to bending loads, the innermost rib panel begins to collapse at less than half the intended design load and in a mode different from that predicted by linear analysis. The discrepancy between the predicted linear and nonlinear responses is attributed to the effects of the nonlinear rib crushing load, and the parameter which controls this rib collapse failure mode is shown to be the rib thickness. The rib collapse failure mode may be avoided by increasing the rib thickness above the value obtained from the (linear analysis based) optimizer. It is concluded that it would be necessary to include geometric nonlinearities in the design optimization process if the true optimum in this case were to be found.

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Linear-sweep voltammetry of a soluble redox couple in a cylindrical electrode

NASA Technical Reports Server (NTRS)

Weidner, John W.

1991-01-01

An approach is described for using the linear sweep voltammetry (LSV) technique to study the kinetics of flooded porous electrodes by assuming a porous electrode as a collection of identical noninterconnected cylindrical pores that are filled with electrolyte. This assumption makes possible to study the behavior of this ideal electrode as that of a single pore. Alternatively, for an electrode of a given pore-size distribution, it is possible to predict the performance of different pore sizes and then combine the performance values.

Hippocampus Segmentation Based on Local Linear Mapping

PubMed Central

Pang, Shumao; Jiang, Jun; Lu, Zhentai; Li, Xueli; Yang, Wei; Huang, Meiyan; Zhang, Yu; Feng, Yanqiu; Huang, Wenhua; Feng, Qianjin

2017-01-01

We propose local linear mapping (LLM), a novel fusion framework for distance field (DF) to perform automatic hippocampus segmentation. A k-means cluster method is propose for constructing magnetic resonance (MR) and DF dictionaries. In LLM, we assume that the MR and DF samples are located on two nonlinear manifolds and the mapping from the MR manifold to the DF manifold is differentiable and locally linear. We combine the MR dictionary using local linear representation to present the test sample, and combine the DF dictionary using the corresponding coefficients derived from local linear representation procedure to predict the DF of the test sample. We then merge the overlapped predicted DF patch to obtain the DF value of each point in the test image via a confidence-based weighted average method. This approach enabled us to estimate the label of the test image according to the predicted DF. The proposed method was evaluated on brain images of 35 subjects obtained from SATA dataset. Results indicate the effectiveness of the proposed method, which yields mean Dice similarity coefficients of 0.8697, 0.8770 and 0.8734 for the left, right and bi-lateral hippocampus, respectively. PMID:28368016

Hippocampus Segmentation Based on Local Linear Mapping.

PubMed

Pang, Shumao; Jiang, Jun; Lu, Zhentai; Li, Xueli; Yang, Wei; Huang, Meiyan; Zhang, Yu; Feng, Yanqiu; Huang, Wenhua; Feng, Qianjin

2017-04-03

We propose local linear mapping (LLM), a novel fusion framework for distance field (DF) to perform automatic hippocampus segmentation. A k-means cluster method is propose for constructing magnetic resonance (MR) and DF dictionaries. In LLM, we assume that the MR and DF samples are located on two nonlinear manifolds and the mapping from the MR manifold to the DF manifold is differentiable and locally linear. We combine the MR dictionary using local linear representation to present the test sample, and combine the DF dictionary using the corresponding coefficients derived from local linear representation procedure to predict the DF of the test sample. We then merge the overlapped predicted DF patch to obtain the DF value of each point in the test image via a confidence-based weighted average method. This approach enabled us to estimate the label of the test image according to the predicted DF. The proposed method was evaluated on brain images of 35 subjects obtained from SATA dataset. Results indicate the effectiveness of the proposed method, which yields mean Dice similarity coefficients of 0.8697, 0.8770 and 0.8734 for the left, right and bi-lateral hippocampus, respectively.

Hippocampus Segmentation Based on Local Linear Mapping

NASA Astrophysics Data System (ADS)

Pang, Shumao; Jiang, Jun; Lu, Zhentai; Li, Xueli; Yang, Wei; Huang, Meiyan; Zhang, Yu; Feng, Yanqiu; Huang, Wenhua; Feng, Qianjin

2017-04-01

We propose local linear mapping (LLM), a novel fusion framework for distance field (DF) to perform automatic hippocampus segmentation. A k-means cluster method is propose for constructing magnetic resonance (MR) and DF dictionaries. In LLM, we assume that the MR and DF samples are located on two nonlinear manifolds and the mapping from the MR manifold to the DF manifold is differentiable and locally linear. We combine the MR dictionary using local linear representation to present the test sample, and combine the DF dictionary using the corresponding coefficients derived from local linear representation procedure to predict the DF of the test sample. We then merge the overlapped predicted DF patch to obtain the DF value of each point in the test image via a confidence-based weighted average method. This approach enabled us to estimate the label of the test image according to the predicted DF. The proposed method was evaluated on brain images of 35 subjects obtained from SATA dataset. Results indicate the effectiveness of the proposed method, which yields mean Dice similarity coefficients of 0.8697, 0.8770 and 0.8734 for the left, right and bi-lateral hippocampus, respectively.

Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem

NASA Astrophysics Data System (ADS)

Rahmalia, Dinita

2017-08-01

Linear Transportation Problem (LTP) is the case of constrained optimization where we want to minimize cost subject to the balance of the number of supply and the number of demand. The exact method such as northwest corner, vogel, russel, minimal cost have been applied at approaching optimal solution. In this paper, we use heurisitic like Particle Swarm Optimization (PSO) for solving linear transportation problem at any size of decision variable. In addition, we combine mutation operator of Genetic Algorithm (GA) at PSO to improve optimal solution. This method is called Particle Swarm Optimization - Genetic Algorithm (PSOGA). The simulations show that PSOGA can improve optimal solution resulted by PSO.

Graphical overview and navigation of electronic health records in a prototyping environment using Google Earth and openEHR archetypes.

PubMed

Sundvall, Erik; Nyström, Mikael; Forss, Mattias; Chen, Rong; Petersson, Håkan; Ahlfeldt, Hans

2007-01-01

This paper describes selected earlier approaches to graphically relating events to each other and to time; some new combinations are also suggested. These are then combined into a unified prototyping environment for visualization and navigation of electronic health records. Google Earth (GE) is used for handling display and interaction of clinical information stored using openEHR data structures and 'archetypes'. The strength of the approach comes from GE's sophisticated handling of detail levels, from coarse overviews to fine-grained details that has been combined with linear, polar and region-based views of clinical events related to time. The system should be easy to learn since all the visualization styles can use the same navigation. The structured and multifaceted approach to handling time that is possible with archetyped openEHR data lends itself well to visualizing and integration with openEHR components is provided in the environment.

Bounded Linear Stability Analysis - A Time Delay Margin Estimation Approach for Adaptive Control

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.; Ishihara, Abraham K.; Krishnakumar, Kalmanje Srinlvas; Bakhtiari-Nejad, Maryam

2009-01-01

This paper presents a method for estimating time delay margin for model-reference adaptive control of systems with almost linear structured uncertainty. The bounded linear stability analysis method seeks to represent the conventional model-reference adaptive law by a locally bounded linear approximation within a small time window using the comparison lemma. The locally bounded linear approximation of the combined adaptive system is cast in a form of an input-time-delay differential equation over a small time window. The time delay margin of this system represents a local stability measure and is computed analytically by a matrix measure method, which provides a simple analytical technique for estimating an upper bound of time delay margin. Based on simulation results for a scalar model-reference adaptive control system, both the bounded linear stability method and the matrix measure method are seen to provide a reasonably accurate and yet not too conservative time delay margin estimation.

ATOM TRANSFER RADICAL POLYMERIZATION OF N-BUTYL METHACRYLATE IN AQUEOUS DISPERSED SYSTEMS: A MINIEMULSION APPROACH. (R826735)

EPA Science Inventory

Ultrasonication was applied in combination with a hydrophobe for the copper-mediated atom transfer radical polymerization of n-butyl methacrylate in an aqueous dispersed system. A controlled polymerization was successfully achieved, as demonstrated by a linear correlation between...

Machine learning-based methods for prediction of linear B-cell epitopes.

PubMed

Wang, Hsin-Wei; Pai, Tun-Wen

2014-01-01

B-cell epitope prediction facilitates immunologists in designing peptide-based vaccine, diagnostic test, disease prevention, treatment, and antibody production. In comparison with T-cell epitope prediction, the performance of variable length B-cell epitope prediction is still yet to be satisfied. Fortunately, due to increasingly available verified epitope databases, bioinformaticians could adopt machine learning-based algorithms on all curated data to design an improved prediction tool for biomedical researchers. Here, we have reviewed related epitope prediction papers, especially those for linear B-cell epitope prediction. It should be noticed that a combination of selected propensity scales and statistics of epitope residues with machine learning-based tools formulated a general way for constructing linear B-cell epitope prediction systems. It is also observed from most of the comparison results that the kernel method of support vector machine (SVM) classifier outperformed other machine learning-based approaches. Hence, in this chapter, except reviewing recently published papers, we have introduced the fundamentals of B-cell epitope and SVM techniques. In addition, an example of linear B-cell prediction system based on physicochemical features and amino acid combinations is illustrated in details.

Improved determination of particulate absorption from combined filter pad and PSICAM measurements.

PubMed

Lefering, Ina; Röttgers, Rüdiger; Weeks, Rebecca; Connor, Derek; Utschig, Christian; Heymann, Kerstin; McKee, David

2016-10-31

Filter pad light absorption measurements are subject to two major sources of experimental uncertainty: the so-called pathlength amplification factor, β, and scattering offsets, o, for which previous null-correction approaches are limited by recent observations of non-zero absorption in the near infrared (NIR). A new filter pad absorption correction method is presented here which uses linear regression against point-source integrating cavity absorption meter (PSICAM) absorption data to simultaneously resolve both β and the scattering offset. The PSICAM has previously been shown to provide accurate absorption data, even in highly scattering waters. Comparisons of PSICAM and filter pad particulate absorption data reveal linear relationships that vary on a sample by sample basis. This regression approach provides significantly improved agreement with PSICAM data (3.2% RMS%E) than previously published filter pad absorption corrections. Results show that direct transmittance (T-method) filter pad absorption measurements perform effectively at the same level as more complex geometrical configurations based on integrating cavity measurements (IS-method and QFT-ICAM) because the linear regression correction compensates for the sensitivity to scattering errors in the T-method. This approach produces accurate filter pad particulate absorption data for wavelengths in the blue/UV and in the NIR where sensitivity issues with PSICAM measurements limit performance. The combination of the filter pad absorption and PSICAM is therefore recommended for generating full spectral, best quality particulate absorption data as it enables correction of multiple errors sources across both measurements.

Combining module based on coherent polarization beam combining.

PubMed

Yang, Yan; Geng, Chao; Li, Feng; Li, Xinyang

2017-03-01

A multiaperture receiver with a phased array is an effective approach to overcome the effect of the random optical disturbance in coherent free-space laser communications, in which one of the key technologies is how to efficiently combine the multiple laser beams received by the phased array antenna. A combining module based on coherent polarization beam combining (CPBC), which can combine multiple laser beams to one laser beam with high combining efficiency and output a linearly polarized beam, is proposed in this paper. The principle of the combining module is introduced, the coherent polarization combining efficiency of CPBC is analyzed, and the performance of the combining module is evaluated. Moreover, the feasibility and the expansibility of the proposed combining module are validated in experiments of CPBC based on active phase-locking.

Interpreting linear support vector machine models with heat map molecule coloring

PubMed Central

2011-01-01

Background Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity. Results We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor. Conclusions In combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor. PMID:21439031

Weighted hybrid technique for recommender system

NASA Astrophysics Data System (ADS)

Suriati, S.; Dwiastuti, Meisyarah; Tulus, T.

2017-12-01

Recommender system becomes very popular and has important role in an information system or webpages nowadays. A recommender system tries to make a prediction of which item a user may like based on his activity on the system. There are some familiar techniques to build a recommender system, such as content-based filtering and collaborative filtering. Content-based filtering does not involve opinions from human to make the prediction, while collaborative filtering does, so collaborative filtering can predict more accurately. However, collaborative filtering cannot give prediction to items which have never been rated by any user. In order to cover the drawbacks of each approach with the advantages of other approach, both approaches can be combined with an approach known as hybrid technique. Hybrid technique used in this work is weighted technique in which the prediction score is combination linear of scores gained by techniques that are combined.The purpose of this work is to show how an approach of weighted hybrid technique combining content-based filtering and item-based collaborative filtering can work in a movie recommender system and to show the performance comparison when both approachare combined and when each approach works alone. There are three experiments done in this work, combining both techniques with different parameters. The result shows that the weighted hybrid technique that is done in this work does not really boost the performance up, but it helps to give prediction score for unrated movies that are impossible to be recommended by only using collaborative filtering.

School Climate, Principal Support and Collaboration among Portuguese Teachers

ERIC Educational Resources Information Center

Castro Silva, José; Amante, Lúcia; Morgado, José

2017-01-01

This article analyses the relationship between school principal support and teacher collaboration among Portuguese teachers. Data were collected from a random sample of 234 teachers in middle and secondary schools. The use of a combined approach using linear and multiple regression tests concluded that the school principal support, through the…

A new preconditioner update strategy for the solution of sequences of linear systems in structural mechanics: application to saddle point problems in elasticity

NASA Astrophysics Data System (ADS)

Mercier, Sylvain; Gratton, Serge; Tardieu, Nicolas; Vasseur, Xavier

2017-12-01

Many applications in structural mechanics require the numerical solution of sequences of linear systems typically issued from a finite element discretization of the governing equations on fine meshes. The method of Lagrange multipliers is often used to take into account mechanical constraints. The resulting matrices then exhibit a saddle point structure and the iterative solution of such preconditioned linear systems is considered as challenging. A popular strategy is then to combine preconditioning and deflation to yield an efficient method. We propose an alternative that is applicable to the general case and not only to matrices with a saddle point structure. In this approach, we consider to update an existing algebraic or application-based preconditioner, using specific available information exploiting the knowledge of an approximate invariant subspace or of matrix-vector products. The resulting preconditioner has the form of a limited memory quasi-Newton matrix and requires a small number of linearly independent vectors. Numerical experiments performed on three large-scale applications in elasticity highlight the relevance of the new approach. We show that the proposed method outperforms the deflation method when considering sequences of linear systems with varying matrices.

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

PubMed

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

2011-07-01

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

Modeling workplace bullying using catastrophe theory.

PubMed

Escartin, J; Ceja, L; Navarro, J; Zapf, D

2013-10-01

Workplace bullying is defined as negative behaviors directed at organizational members or their work context that occur regularly and repeatedly over a period of time. Employees' perceptions of psychosocial safety climate, workplace bullying victimization, and workplace bullying perpetration were assessed within a sample of nearly 5,000 workers. Linear and nonlinear approaches were applied in order to model both continuous and sudden changes in workplace bullying. More specifically, the present study examines whether a nonlinear dynamical systems model (i.e., a cusp catastrophe model) is superior to the linear combination of variables for predicting the effect of psychosocial safety climate and workplace bullying victimization on workplace bullying perpetration. According to the AICc, and BIC indices, the linear regression model fits the data better than the cusp catastrophe model. The study concludes that some phenomena, especially unhealthy behaviors at work (like workplace bullying), may be better studied using linear approaches as opposed to nonlinear dynamical systems models. This can be explained through the healthy variability hypothesis, which argues that positive organizational behavior is likely to present nonlinear behavior, while a decrease in such variability may indicate the occurrence of negative behaviors at work.

Mixed H∞ and passive control for linear switched systems via hybrid control approach

NASA Astrophysics Data System (ADS)

Zheng, Qunxian; Ling, Youzhu; Wei, Lisheng; Zhang, Hongbin

2018-03-01

This paper investigates the mixed H∞ and passive control problem for linear switched systems based on a hybrid control strategy. To solve this problem, first, a new performance index is proposed. This performance index can be viewed as the mixed weighted H∞ and passivity performance. Then, the hybrid controllers are used to stabilise the switched systems. The hybrid controllers consist of dynamic output-feedback controllers for every subsystem and state updating controllers at the switching instant. The design of state updating controllers not only depends on the pre-switching subsystem and the post-switching subsystem, but also depends on the measurable output signal. The hybrid controllers proposed in this paper can include some existing ones as special cases. Combine the multiple Lyapunov functions approach with the average dwell time technique, new sufficient conditions are obtained. Under the new conditions, the closed-loop linear switched systems are globally uniformly asymptotically stable with a mixed H∞ and passivity performance index. Moreover, the desired hybrid controllers can be constructed by solving a set of linear matrix inequalities. Finally, a numerical example and a practical example are given.

Assessment of combined antiandrogenic effects of binary parabens mixtures in a yeast-based reporter assay.

PubMed

Ma, Dehua; Chen, Lujun; Zhu, Xiaobiao; Li, Feifei; Liu, Cong; Liu, Rui

2014-05-01

To date, toxicological studies of endocrine disrupting chemicals (EDCs) have typically focused on single chemical exposures and associated effects. However, exposure to EDCs mixtures in the environment is common. Antiandrogens represent a group of EDCs, which draw increasing attention due to their resultant demasculinization and sexual disruption of aquatic organisms. Although there are a number of in vivo and in vitro studies investigating the combined effects of antiandrogen mixtures, these studies are mainly on selected model compounds such as flutamide, procymidone, and vinclozolin. The aim of the present study is to investigate the combined antiandrogenic effects of parabens, which are widely used antiandrogens in industrial and domestic commodities. A yeast-based human androgen receptor (hAR) assay (YAS) was applied to assess the antiandrogenic activities of n-propylparaben (nPrP), iso-propylparaben (iPrP), methylparaben (MeP), and 4-n-pentylphenol (PeP), as well as the binary mixtures of nPrP with each of the other three antiandrogens. All of the four compounds could exhibit antiandrogenic activity via the hAR. A linear interaction model was applied to quantitatively analyze the interaction between nPrP and each of the other three antiandrogens. The isoboles method was modified to show the variation of combined effects as the concentrations of mixed antiandrogens were changed. Graphs were constructed to show isoeffective curves of three binary mixtures based on the fitted linear interaction model and to evaluate the interaction of the mixed antiandrogens (synergism or antagonism). The combined effect of equimolar combinations of the three mixtures was also considered with the nonlinear isoboles method. The main effect parameters and interaction effect parameters in the linear interaction models of the three mixtures were different from zero. The results showed that any two antiandrogens in their binary mixtures tended to exert equal antiandrogenic activity in the linear concentration ranges. The antiandrogenicity of the binary mixture and the concentration of nPrP were fitted to a sigmoidal model if the concentrations of the other antiandrogens (iPrP, MeP, and PeP) in the mixture were lower than the AR saturation concentrations. Some concave isoboles above the additivity line appeared in all the three mixtures. There were some synergistic effects of the binary mixture of nPrP and MeP at low concentrations in the linear concentration ranges. Interesting, when the antiandrogens concentrations approached the saturation, the interaction between chemicals were antagonistic for all the three mixtures tested. When the toxicity of the three mixtures was assessed using nonlinear isoboles, only antagonism was observed for equimolar combinations of nPrP and iPrP as the concentrations were increased from the no-observed-effect-concentration (NOEC) to effective concentration of 80%. In addition, the interactions were changed from synergistic to antagonistic as effective concentrations were increased in the equimolar combinations of nPrP and MeP, as well as nPrP and PeP. The combined effects of three binary antiandrogens mixtures in the linear ranges were successfully evaluated by curve fitting and isoboles. The combined effects of specific binary mixtures varied depending on the concentrations of the chemicals in the mixtures. At low concentrations in the linear concentration ranges, there was synergistic interaction existing in the binary mixture of nPrP and MeP. The interaction tended to be antagonistic as the antiandrogens approached saturation concentrations in mixtures of nPrP with each of the other three antiandrogens. The synergistic interaction was also found in the equimolar combinations of nPrP and MeP, as well as nPrP and PeP, at low concentrations with another method of nonlinear isoboles. The mixture activities of binary antiandrogens had a tendency towards antagonism at high concentrations and synergism at low concentrations.

Analysis of the quality of image data acquired by the LANDSAT-4 thematic mapper and multispectral scanners

NASA Technical Reports Server (NTRS)

Colwell, R. N. (Principal Investigator)

1983-01-01

The geometric quality of the TM and MSS film products were evaluated by making selective photo measurements such as scale, linear and area determinations; and by measuring the coordinates of known features on both the film products and map products and then relating these paired observations using a standard linear least squares regression approach. Quantitative interpretation tests are described which evaluate the quality and utility of the TM film products and various band combinations for detecting and identifying important forest and agricultural features.

Canonical-ensemble extended Lagrangian Born-Oppenheimer molecular dynamics for the linear scaling density functional theory.

PubMed

Hirakawa, Teruo; Suzuki, Teppei; Bowler, David R; Miyazaki, Tsuyoshi

2017-10-11

We discuss the development and implementation of a constant temperature (NVT) molecular dynamics scheme that combines the Nosé-Hoover chain thermostat with the extended Lagrangian Born-Oppenheimer molecular dynamics (BOMD) scheme, using a linear scaling density functional theory (DFT) approach. An integration scheme for this canonical-ensemble extended Lagrangian BOMD is developed and discussed in the context of the Liouville operator formulation. Linear scaling DFT canonical-ensemble extended Lagrangian BOMD simulations are tested on bulk silicon and silicon carbide systems to evaluate our integration scheme. The results show that the conserved quantity remains stable with no systematic drift even in the presence of the thermostat.

A Review on Dimension Reduction

PubMed Central

Ma, Yanyuan; Zhu, Liping

2013-01-01

Summary Summarizing the effect of many covariates through a few linear combinations is an effective way of reducing covariate dimension and is the backbone of (sufficient) dimension reduction. Because the replacement of high-dimensional covariates by low-dimensional linear combinations is performed with a minimum assumption on the specific regression form, it enjoys attractive advantages as well as encounters unique challenges in comparison with the variable selection approach. We review the current literature of dimension reduction with an emphasis on the two most popular models, where the dimension reduction affects the conditional distribution and the conditional mean, respectively. We discuss various estimation and inference procedures in different levels of detail, with the intention of focusing on their underneath idea instead of technicalities. We also discuss some unsolved problems in this area for potential future research. PMID:23794782

Prediction of HDR quality by combining perceptually transformed display measurements with machine learning

NASA Astrophysics Data System (ADS)

Choudhury, Anustup; Farrell, Suzanne; Atkins, Robin; Daly, Scott

2017-09-01

We present an approach to predict overall HDR display quality as a function of key HDR display parameters. We first performed subjective experiments on a high quality HDR display that explored five key HDR display parameters: maximum luminance, minimum luminance, color gamut, bit-depth and local contrast. Subjects rated overall quality for different combinations of these display parameters. We explored two models | a physical model solely based on physically measured display characteristics and a perceptual model that transforms physical parameters using human vision system models. For the perceptual model, we use a family of metrics based on a recently published color volume model (ICT-CP), which consists of the PQ luminance non-linearity (ST2084) and LMS-based opponent color, as well as an estimate of the display point spread function. To predict overall visual quality, we apply linear regression and machine learning techniques such as Multilayer Perceptron, RBF and SVM networks. We use RMSE and Pearson/Spearman correlation coefficients to quantify performance. We found that the perceptual model is better at predicting subjective quality than the physical model and that SVM is better at prediction than linear regression. The significance and contribution of each display parameter was investigated. In addition, we found that combined parameters such as contrast do not improve prediction. Traditional perceptual models were also evaluated and we found that models based on the PQ non-linearity performed better.

Numerical Technology for Large-Scale Computational Electromagnetics

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

Sharpe, R; Champagne, N; White, D

The key bottleneck of implicit computational electromagnetics tools for large complex geometries is the solution of the resulting linear system of equations. The goal of this effort was to research and develop critical numerical technology that alleviates this bottleneck for large-scale computational electromagnetics (CEM). The mathematical operators and numerical formulations used in this arena of CEM yield linear equations that are complex valued, unstructured, and indefinite. Also, simultaneously applying multiple mathematical modeling formulations to different portions of a complex problem (hybrid formulations) results in a mixed structure linear system, further increasing the computational difficulty. Typically, these hybrid linear systems aremore » solved using a direct solution method, which was acceptable for Cray-class machines but does not scale adequately for ASCI-class machines. Additionally, LLNL's previously existing linear solvers were not well suited for the linear systems that are created by hybrid implicit CEM codes. Hence, a new approach was required to make effective use of ASCI-class computing platforms and to enable the next generation design capabilities. Multiple approaches were investigated, including the latest sparse-direct methods developed by our ASCI collaborators. In addition, approaches that combine domain decomposition (or matrix partitioning) with general-purpose iterative methods and special purpose pre-conditioners were investigated. Special-purpose pre-conditioners that take advantage of the structure of the matrix were adapted and developed based on intimate knowledge of the matrix properties. Finally, new operator formulations were developed that radically improve the conditioning of the resulting linear systems thus greatly reducing solution time. The goal was to enable the solution of CEM problems that are 10 to 100 times larger than our previous capability.« less

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

PubMed

Li, Pu; Vu, Quoc Dong

2015-12-14

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

Simplified methods for real-time prediction of storm surge uncertainty: The city of Venice case study

NASA Astrophysics Data System (ADS)

Mel, Riccardo; Viero, Daniele Pietro; Carniello, Luca; Defina, Andrea; D'Alpaos, Luigi

2014-09-01

Providing reliable and accurate storm surge forecasts is important for a wide range of problems related to coastal environments. In order to adequately support decision-making processes, it also become increasingly important to be able to estimate the uncertainty associated with the storm surge forecast. The procedure commonly adopted to do this uses the results of a hydrodynamic model forced by a set of different meteorological forecasts; however, this approach requires a considerable, if not prohibitive, computational cost for real-time application. In the present paper we present two simplified methods for estimating the uncertainty affecting storm surge prediction with moderate computational effort. In the first approach we use a computationally fast, statistical tidal model instead of a hydrodynamic numerical model to estimate storm surge uncertainty. The second approach is based on the observation that the uncertainty in the sea level forecast mainly stems from the uncertainty affecting the meteorological fields; this has led to the idea to estimate forecast uncertainty via a linear combination of suitable meteorological variances, directly extracted from the meteorological fields. The proposed methods were applied to estimate the uncertainty in the storm surge forecast in the Venice Lagoon. The results clearly show that the uncertainty estimated through a linear combination of suitable meteorological variances nicely matches the one obtained using the deterministic approach and overcomes some intrinsic limitations in the use of a statistical tidal model.

Testing the Intervention Effect in Single-Case Experiments: A Monte Carlo Simulation Study

ERIC Educational Resources Information Center

Heyvaert, Mieke; Moeyaert, Mariola; Verkempynck, Paul; Van den Noortgate, Wim; Vervloet, Marlies; Ugille, Maaike; Onghena, Patrick

2017-01-01

This article reports on a Monte Carlo simulation study, evaluating two approaches for testing the intervention effect in replicated randomized AB designs: two-level hierarchical linear modeling (HLM) and using the additive method to combine randomization test "p" values (RTcombiP). Four factors were manipulated: mean intervention effect,…

Monitoring Human Development Goals: A Straightforward (Bayesian) Methodology for Cross-National Indices

ERIC Educational Resources Information Center

Abayomi, Kobi; Pizarro, Gonzalo

2013-01-01

We offer a straightforward framework for measurement of progress, across many dimensions, using cross-national social indices, which we classify as linear combinations of multivariate country level data onto a univariate score. We suggest a Bayesian approach which yields probabilistic (confidence type) intervals for the point estimates of country…

A comparative analysis of alternative approaches for quantifying nonlinear dynamics in cardiovascular system.

PubMed

Chen, Yun; Yang, Hui

2013-01-01

Heart rate variability (HRV) analysis has emerged as an important research topic to evaluate autonomic cardiac function. However, traditional time and frequency-domain analysis characterizes and quantify only linear and stationary phenomena. In the present investigation, we made a comparative analysis of three alternative approaches (i.e., wavelet multifractal analysis, Lyapunov exponents and multiscale entropy analysis) for quantifying nonlinear dynamics in heart rate time series. Note that these extracted nonlinear features provide information about nonlinear scaling behaviors and the complexity of cardiac systems. To evaluate the performance, we used 24-hour HRV recordings from 54 healthy subjects and 29 heart failure patients, available in PhysioNet. Three nonlinear methods are evaluated not only individually but also in combination using three classification algorithms, i.e., linear discriminate analysis, quadratic discriminate analysis and k-nearest neighbors. Experimental results show that three nonlinear methods capture nonlinear dynamics from different perspectives and the combined feature set achieves the best performance, i.e., sensitivity 97.7% and specificity 91.5%. Collectively, nonlinear HRV features are shown to have the promise to identify the disorders in autonomic cardiovascular function.

Data analytics using canonical correlation analysis and Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Rickman, Jeffrey M.; Wang, Yan; Rollett, Anthony D.; Harmer, Martin P.; Compson, Charles

2017-07-01

A canonical correlation analysis is a generic parametric model used in the statistical analysis of data involving interrelated or interdependent input and output variables. It is especially useful in data analytics as a dimensional reduction strategy that simplifies a complex, multidimensional parameter space by identifying a relatively few combinations of variables that are maximally correlated. One shortcoming of the canonical correlation analysis, however, is that it provides only a linear combination of variables that maximizes these correlations. With this in mind, we describe here a versatile, Monte-Carlo based methodology that is useful in identifying non-linear functions of the variables that lead to strong input/output correlations. We demonstrate that our approach leads to a substantial enhancement of correlations, as illustrated by two experimental applications of substantial interest to the materials science community, namely: (1) determining the interdependence of processing and microstructural variables associated with doped polycrystalline aluminas, and (2) relating microstructural decriptors to the electrical and optoelectronic properties of thin-film solar cells based on CuInSe2 absorbers. Finally, we describe how this approach facilitates experimental planning and process control.

Global GNSS processing based on the raw observation approach

NASA Astrophysics Data System (ADS)

Strasser, Sebastian; Zehentner, Norbert; Mayer-Gürr, Torsten

2017-04-01

Many global navigation satellite system (GNSS) applications, e.g. Precise Point Positioning (PPP), require high-quality GNSS products, such as precise GNSS satellite orbits and clocks. These products are routinely determined by analysis centers of the International GNSS Service (IGS). The current processing methods of the analysis centers make use of the ionosphere-free linear combination to reduce the ionospheric influence. Some of the analysis centers also form observation differences, in general double-differences, to eliminate several additional error sources. The raw observation approach is a new GNSS processing approach that was developed at Graz University of Technology for kinematic orbit determination of low Earth orbit (LEO) satellites and subsequently adapted to global GNSS processing in general. This new approach offers some benefits compared to well-established approaches, such as a straightforward incorporation of new observables due to the avoidance of observation differences and linear combinations. This becomes especially important in view of the changing GNSS landscape with two new systems, the European system Galileo and the Chinese system BeiDou, currently in deployment. GNSS products generated at Graz University of Technology using the raw observation approach currently comprise precise GNSS satellite orbits and clocks, station positions and clocks, code and phase biases, and Earth rotation parameters. To evaluate the new approach, products generated using the Global Positioning System (GPS) constellation and observations from the global IGS station network are compared to those of the IGS analysis centers. The comparisons show that the products generated at Graz University of Technology are on a similar level of quality to the products determined by the IGS analysis centers. This confirms that the raw observation approach is applicable to global GNSS processing. Some areas requiring further work have been identified, enabling future improvements of the method.

Towards lexicographic multi-objective linear programming using grossone methodology

NASA Astrophysics Data System (ADS)

Cococcioni, Marco; Pappalardo, Massimo; Sergeyev, Yaroslav D.

2016-10-01

Lexicographic Multi-Objective Linear Programming (LMOLP) problems can be solved in two ways: preemptive and nonpreemptive. The preemptive approach requires the solution of a series of LP problems, with changing constraints (each time the next objective is added, a new constraint appears). The nonpreemptive approach is based on a scalarization of the multiple objectives into a single-objective linear function by a weighted combination of the given objectives. It requires the specification of a set of weights, which is not straightforward and can be time consuming. In this work we present both mathematical and software ingredients necessary to solve LMOLP problems using a recently introduced computational methodology (allowing one to work numerically with infinities and infinitesimals) based on the concept of grossone. The ultimate goal of such an attempt is an implementation of a simplex-like algorithm, able to solve the original LMOLP problem by solving only one single-objective problem and without the need to specify finite weights. The expected advantages are therefore obvious.

Linear and nonlinear interpretation of the direct strike lightning response of the NASA F106B thunderstorm research aircraft

NASA Technical Reports Server (NTRS)

Rudolph, T. H.; Perala, R. A.

1983-01-01

The objective of the work reported here is to develop a methodology by which electromagnetic measurements of inflight lightning strike data can be understood and extended to other aircraft. A linear and time invariant approach based on a combination of Fourier transform and three dimensional finite difference techniques is demonstrated. This approach can obtain the lightning channel current in the absence of the aircraft for given channel characteristic impedance and resistive loading. The model is applied to several measurements from the NASA F106B lightning research program. A non-linear three dimensional finite difference code has also been developed to study the response of the F106B to a lightning leader attachment. This model includes three species air chemistry and fluid continuity equations and can incorporate an experimentally based streamer formulation. Calculated responses are presented for various attachment locations and leader parameters. The results are compared qualitatively with measured inflight data.

Novel hybrid linear stochastic with non-linear extreme learning machine methods for forecasting monthly rainfall a tropical climate.

PubMed

Zeynoddin, Mohammad; Bonakdari, Hossein; Azari, Arash; Ebtehaj, Isa; Gharabaghi, Bahram; Riahi Madavar, Hossein

2018-09-15

A novel hybrid approach is presented that can more accurately predict monthly rainfall in a tropical climate by integrating a linear stochastic model with a powerful non-linear extreme learning machine method. This new hybrid method was then evaluated by considering four general scenarios. In the first scenario, the modeling process is initiated without preprocessing input data as a base case. While in other three scenarios, the one-step and two-step procedures are utilized to make the model predictions more precise. The mentioned scenarios are based on a combination of stationarization techniques (i.e., differencing, seasonal and non-seasonal standardization and spectral analysis), and normality transforms (i.e., Box-Cox, John and Draper, Yeo and Johnson, Johnson, Box-Cox-Mod, log, log standard, and Manly). In scenario 2, which is a one-step scenario, the stationarization methods are employed as preprocessing approaches. In scenario 3 and 4, different combinations of normality transform, and stationarization methods are considered as preprocessing techniques. In total, 61 sub-scenarios are evaluated resulting 11013 models (10785 linear methods, 4 nonlinear models, and 224 hybrid models are evaluated). The uncertainty of the linear, nonlinear and hybrid models are examined by Monte Carlo technique. The best preprocessing technique is the utilization of Johnson normality transform and seasonal standardization (respectively) (R 2  = 0.99; RMSE = 0.6; MAE = 0.38; RMSRE = 0.1, MARE = 0.06, UI = 0.03 &UII = 0.05). The results of uncertainty analysis indicated the good performance of proposed technique (d-factor = 0.27; 95PPU = 83.57). Moreover, the results of the proposed methodology in this study were compared with an evolutionary hybrid of adaptive neuro fuzzy inference system (ANFIS) with firefly algorithm (ANFIS-FFA) demonstrating that the new hybrid methods outperformed ANFIS-FFA method. Copyright © 2018 Elsevier Ltd. All rights reserved.

Time-Frequency Analysis of Non-Stationary Biological Signals with Sparse Linear Regression Based Fourier Linear Combiner.

PubMed

Wang, Yubo; Veluvolu, Kalyana C

2017-06-14

It is often difficult to analyze biological signals because of their nonlinear and non-stationary characteristics. This necessitates the usage of time-frequency decomposition methods for analyzing the subtle changes in these signals that are often connected to an underlying phenomena. This paper presents a new approach to analyze the time-varying characteristics of such signals by employing a simple truncated Fourier series model, namely the band-limited multiple Fourier linear combiner (BMFLC). In contrast to the earlier designs, we first identified the sparsity imposed on the signal model in order to reformulate the model to a sparse linear regression model. The coefficients of the proposed model are then estimated by a convex optimization algorithm. The performance of the proposed method was analyzed with benchmark test signals. An energy ratio metric is employed to quantify the spectral performance and results show that the proposed method Sparse-BMFLC has high mean energy (0.9976) ratio and outperforms existing methods such as short-time Fourier transfrom (STFT), continuous Wavelet transform (CWT) and BMFLC Kalman Smoother. Furthermore, the proposed method provides an overall 6.22% in reconstruction error.

Linear and nonlinear spectroscopy from quantum master equations.

PubMed

Fetherolf, Jonathan H; Berkelbach, Timothy C

2017-12-28

We investigate the accuracy of the second-order time-convolutionless (TCL2) quantum master equation for the calculation of linear and nonlinear spectroscopies of multichromophore systems. We show that even for systems with non-adiabatic coupling, the TCL2 master equation predicts linear absorption spectra that are accurate over an extremely broad range of parameters and well beyond what would be expected based on the perturbative nature of the approach; non-equilibrium population dynamics calculated with TCL2 for identical parameters are significantly less accurate. For third-order (two-dimensional) spectroscopy, the importance of population dynamics and the violation of the so-called quantum regression theorem degrade the accuracy of TCL2 dynamics. To correct these failures, we combine the TCL2 approach with a classical ensemble sampling of slow microscopic bath degrees of freedom, leading to an efficient hybrid quantum-classical scheme that displays excellent accuracy over a wide range of parameters. In the spectroscopic setting, the success of such a hybrid scheme can be understood through its separate treatment of homogeneous and inhomogeneous broadening. Importantly, the presented approach has the computational scaling of TCL2, with the modest addition of an embarrassingly parallel prefactor associated with ensemble sampling. The presented approach can be understood as a generalized inhomogeneous cumulant expansion technique, capable of treating multilevel systems with non-adiabatic dynamics.

Linear and nonlinear spectroscopy from quantum master equations

NASA Astrophysics Data System (ADS)

Fetherolf, Jonathan H.; Berkelbach, Timothy C.

2017-12-01

We investigate the accuracy of the second-order time-convolutionless (TCL2) quantum master equation for the calculation of linear and nonlinear spectroscopies of multichromophore systems. We show that even for systems with non-adiabatic coupling, the TCL2 master equation predicts linear absorption spectra that are accurate over an extremely broad range of parameters and well beyond what would be expected based on the perturbative nature of the approach; non-equilibrium population dynamics calculated with TCL2 for identical parameters are significantly less accurate. For third-order (two-dimensional) spectroscopy, the importance of population dynamics and the violation of the so-called quantum regression theorem degrade the accuracy of TCL2 dynamics. To correct these failures, we combine the TCL2 approach with a classical ensemble sampling of slow microscopic bath degrees of freedom, leading to an efficient hybrid quantum-classical scheme that displays excellent accuracy over a wide range of parameters. In the spectroscopic setting, the success of such a hybrid scheme can be understood through its separate treatment of homogeneous and inhomogeneous broadening. Importantly, the presented approach has the computational scaling of TCL2, with the modest addition of an embarrassingly parallel prefactor associated with ensemble sampling. The presented approach can be understood as a generalized inhomogeneous cumulant expansion technique, capable of treating multilevel systems with non-adiabatic dynamics.

A variational approach to dynamics of flexible multibody systems

NASA Technical Reports Server (NTRS)

Wu, Shih-Chin; Haug, Edward J.; Kim, Sung-Soo

1989-01-01

This paper presents a variational formulation of constrained dynamics of flexible multibody systems, using a vector-variational calculus approach. Body reference frames are used to define global position and orientation of individual bodies in the system, located and oriented by position of its origin and Euler parameters, respectively. Small strain linear elastic deformation of individual components, relative to their body references frames, is defined by linear combinations of deformation modes that are induced by constraint reaction forces and normal modes of vibration. A library of kinematic couplings between flexible and/or rigid bodies is defined and analyzed. Variational equations of motion for multibody systems are obtained and reduced to mixed differential-algebraic equations of motion. A space structure that must deform during deployment is analyzed, to illustrate use of the methods developed.

Quasi-model free control for the post-capture operation of a non-cooperative target

NASA Astrophysics Data System (ADS)

She, Yuchen; Sun, Jun; Li, Shuang; Li, Wendan; Song, Ting

2018-06-01

This paper investigates a quasi-model free control (QMFC) approach for the post-capture control of a non-cooperative space object. The innovation of this paper lies in the following three aspects, which correspond to the three challenges presented in the mission scenario. First, an excitation-response mapping search strategy is developed based on the linearization of the system in terms of a set of parameters, which is efficient in handling the combined spacecraft with a high coupling effect on the inertia matrix. Second, a virtual coordinate system is proposed to efficiently compute the center of mass (COM) of the combined system, which improves the COM tracking efficiency for time-varying COM positions. Third, a linear online corrector is built to reduce the control error to further improve the control accuracy, which helps control the tracking mode within the combined system's time-varying inertia matrix. Finally, simulation analyses show that the proposed control framework is able to realize combined spacecraft post-capture control in extremely unfavorable conditions with high control accuracy.

Sparse principal component analysis in medical shape modeling

NASA Astrophysics Data System (ADS)

Sjöstrand, Karl; Stegmann, Mikkel B.; Larsen, Rasmus

2006-03-01

Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims at producing easily interpreted models through sparse loadings, i.e. each new variable is a linear combination of a subset of the original variables. One of the aims of using SPCA is the possible separation of the results into isolated and easily identifiable effects. This article introduces SPCA for shape analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA algorithm has been implemented using Matlab and is available for download. The general behavior of the algorithm is investigated, and strengths and weaknesses are discussed. The original report on the SPCA algorithm argues that the ordering of modes is not an issue. We disagree on this point and propose several approaches to establish sensible orderings. A method that orders modes by decreasing variance and maximizes the sum of variances for all modes is presented and investigated in detail.

Improved Shaping Approach to the Preliminary Design of Low-Thrust Trajectories

NASA Astrophysics Data System (ADS)

Novak, D. M.; Vasile, M.

2011-01-01

This paper presents a general framework for the development of shape-based approaches to low-thrust trajectory design. A novel shaping method, based on a three-dimensional description of the trajectory in spherical coordinates, is developed within this general framework. Both the exponential sinusoid and the inverse polynomial shaping are demonstrated to be particular two-dimensional cases of the spherical one. The pseudoequinoctial shaping is revisited within the new framework, and the nonosculating nature of the pseudoequinoctial elements is analyzed. A two-step approach is introduced to solve the time of flight constraint, related to the design of low-thrust arcs with boundary constraints for both spherical and pseudoequinoctial shaping. The solution derived from the shaping approach is improved with a feedback linear-quadratic controller and compared against a direct collocation method based on finite elements in time. The new shaping approach and the combination of shaping and linear-quadratic controller are tested on three case studies: a mission to Mars, a mission to asteroid 1989ML, a mission to comet Tempel-1, and a mission to Neptune.

CHAMP: a locally adaptive unmixing-based hyperspectral anomaly detection algorithm

NASA Astrophysics Data System (ADS)

Crist, Eric P.; Thelen, Brian J.; Carrara, David A.

1998-10-01

Anomaly detection offers a means by which to identify potentially important objects in a scene without prior knowledge of their spectral signatures. As such, this approach is less sensitive to variations in target class composition, atmospheric and illumination conditions, and sensor gain settings than would be a spectral matched filter or similar algorithm. The best existing anomaly detectors generally fall into one of two categories: those based on local Gaussian statistics, and those based on linear mixing moles. Unmixing-based approaches better represent the real distribution of data in a scene, but are typically derived and applied on a global or scene-wide basis. Locally adaptive approaches allow detection of more subtle anomalies by accommodating the spatial non-homogeneity of background classes in a typical scene, but provide a poorer representation of the true underlying background distribution. The CHAMP algorithm combines the best attributes of both approaches, applying a linear-mixing model approach in a spatially adaptive manner. The algorithm itself, and teste results on simulated and actual hyperspectral image data, are presented in this paper.

Stochastic modeling of macrodispersion in unsaturated heterogeneous porous media. Final report

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

Yeh, T.C.J.

1995-02-01

Spatial heterogeneity of geologic media leads to uncertainty in predicting both flow and transport in the vadose zone. In this work an efficient and flexible, combined analytical-numerical Monte Carlo approach is developed for the analysis of steady-state flow and transient transport processes in highly heterogeneous, variably saturated porous media. The approach is also used for the investigation of the validity of linear, first order analytical stochastic models. With the Monte Carlo analysis accurate estimates of the ensemble conductivity, head, velocity, and concentration mean and covariance are obtained; the statistical moments describing displacement of solute plumes, solute breakthrough at a compliancemore » surface, and time of first exceedance of a given solute flux level are analyzed; and the cumulative probability density functions for solute flux across a compliance surface are investigated. The results of the Monte Carlo analysis show that for very heterogeneous flow fields, and particularly in anisotropic soils, the linearized, analytical predictions of soil water tension and soil moisture flux become erroneous. Analytical, linearized Lagrangian transport models also overestimate both the longitudinal and the transverse spreading of the mean solute plume in very heterogeneous soils and in dry soils. A combined analytical-numerical conditional simulation algorithm is also developed to estimate the impact of in-situ soil hydraulic measurements on reducing the uncertainty of concentration and solute flux predictions.« less

Tail mean and related robust solution concepts

NASA Astrophysics Data System (ADS)

Ogryczak, Włodzimierz

2014-01-01

Robust optimisation might be viewed as a multicriteria optimisation problem where objectives correspond to the scenarios although their probabilities are unknown or imprecise. The simplest robust solution concept represents a conservative approach focused on the worst-case scenario results optimisation. A softer concept allows one to optimise the tail mean thus combining performances under multiple worst scenarios. We show that while considering robust models allowing the probabilities to vary only within given intervals, the tail mean represents the robust solution for only upper bounded probabilities. For any arbitrary intervals of probabilities the corresponding robust solution may be expressed by the optimisation of appropriately combined mean and tail mean criteria thus remaining easily implementable with auxiliary linear inequalities. Moreover, we use the tail mean concept to develope linear programming implementable robust solution concepts related to risk averse optimisation criteria.

AucPR: an AUC-based approach using penalized regression for disease prediction with high-dimensional omics data.

PubMed

Yu, Wenbao; Park, Taesung

2014-01-01

It is common to get an optimal combination of markers for disease classification and prediction when multiple markers are available. Many approaches based on the area under the receiver operating characteristic curve (AUC) have been proposed. Existing works based on AUC in a high-dimensional context depend mainly on a non-parametric, smooth approximation of AUC, with no work using a parametric AUC-based approach, for high-dimensional data. We propose an AUC-based approach using penalized regression (AucPR), which is a parametric method used for obtaining a linear combination for maximizing the AUC. To obtain the AUC maximizer in a high-dimensional context, we transform a classical parametric AUC maximizer, which is used in a low-dimensional context, into a regression framework and thus, apply the penalization regression approach directly. Two kinds of penalization, lasso and elastic net, are considered. The parametric approach can avoid some of the difficulties of a conventional non-parametric AUC-based approach, such as the lack of an appropriate concave objective function and a prudent choice of the smoothing parameter. We apply the proposed AucPR for gene selection and classification using four real microarray and synthetic data. Through numerical studies, AucPR is shown to perform better than the penalized logistic regression and the nonparametric AUC-based method, in the sense of AUC and sensitivity for a given specificity, particularly when there are many correlated genes. We propose a powerful parametric and easily-implementable linear classifier AucPR, for gene selection and disease prediction for high-dimensional data. AucPR is recommended for its good prediction performance. Beside gene expression microarray data, AucPR can be applied to other types of high-dimensional omics data, such as miRNA and protein data.

Understanding Individual-Level Change through the Basis Functions of a Latent Curve Model

ERIC Educational Resources Information Center

Blozis, Shelley A.; Harring, Jeffrey R.

2017-01-01

Latent curve models have become a popular approach to the analysis of longitudinal data. At the individual level, the model expresses an individual's response as a linear combination of what are called "basis functions" that are common to all members of a population and weights that may vary among individuals. This article uses…

Teaching to Emerge: Toward a Bottom-Up Pedagogy

ERIC Educational Resources Information Center

Brailas, Alexios; Koskinas, Konstantinos; Alexias, George

2017-01-01

This paper focuses on the conceptual model of an academic course inspired by complexity theory. In the proposed conceptual model, the aim of teaching is to form a learning organization: a knowledge community with emergent properties that cannot be reduced to any linear combination of the properties of its parts. In this approach, the learning of…

Hybrid Grid and Basis Set Approach to Quantum Chemistry DMRG

NASA Astrophysics Data System (ADS)

Stoudenmire, Edwin Miles; White, Steven

We present a new approach for using DMRG for quantum chemistry that combines the advantages of a basis set with that of a grid approximation. Because DMRG scales linearly for quasi-one-dimensional systems, it is feasible to approximate the continuum with a fine grid in one direction while using a standard basis set approach for the transverse directions. Compared to standard basis set methods, we reach larger systems and achieve better scaling when approaching the basis set limit. The flexibility and reduced costs of our approach even make it feasible to incoporate advanced DMRG techniques such as simulating real-time dynamics. Supported by the Simons Collaboration on the Many-Electron Problem.

Higher symmetries and exact solutions of linear and nonlinear Schr{umlt o}dinger equation

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

Fushchych, W.I.; Nikitin, A.G.

1997-11-01

A new approach for the analysis of partial differential equations is developed which is characterized by a simultaneous use of higher and conditional symmetries. Higher symmetries of the Schr{umlt o}dinger equation with an arbitrary potential are investigated. Nonlinear determining equations for potentials are solved using reductions to Weierstrass, Painlev{acute e}, and Riccati forms. Algebraic properties of higher order symmetry operators are analyzed. Combinations of higher and conditional symmetries are used to generate families of exact solutions of linear and nonlinear Schr{umlt o}dinger equations. {copyright} {ital 1997 American Institute of Physics.}

Theoretical linear approach to the combined man-manipulator system in manual control of an aircraft

NASA Technical Reports Server (NTRS)

Brauser, K.

1981-01-01

An approach to the calculation of the dynamic characteristics of the combined man manipulator system in manual aircraft control was derived from a model of the neuromuscular system. This model combines the neuromuscular properties of man with the physical properties of the manipulator system which is introduced as pilot manipulator model into the manual aircraft control. The assumption of man as a quasilinear and time invariant control operator adapted to operating states, depending on the flight phases, of the control system gives rise to interesting solutions of the frequency domain transfer functions of both the man manipulator system and the closed loop pilot aircraft control system. It is shown that it is necessary to introduce the complete precision pilot manipulator model into the closed loop pilot aircraft transfer function in order to understand the well known handling quality criteria, and to derive these criteria directly from human operator properties.

Future mission studies: Preliminary comparisons of solar flux models

NASA Technical Reports Server (NTRS)

Ashrafi, S.

1991-01-01

The results of comparisons of the solar flux models are presented. (The wavelength lambda = 10.7 cm radio flux is the best indicator of the strength of the ionizing radiations such as solar ultraviolet and x-ray emissions that directly affect the atmospheric density thereby changing the orbit lifetime of satellites. Thus, accurate forecasting of solar flux F sub 10.7 is crucial for orbit determination of spacecrafts.) The measured solar flux recorded by National Oceanic and Atmospheric Administration (NOAA) is compared against the forecasts made by Schatten, MSFC, and NOAA itself. The possibility of a combined linear, unbiased minimum-variance estimation that properly combines all three models into one that minimizes the variance is also discussed. All the physics inherent in each model are combined. This is considered to be the dead-end statistical approach to solar flux forecasting before any nonlinear chaotic approach.

Fast solution of elliptic partial differential equations using linear combinations of plane waves.

PubMed

Pérez-Jordá, José M

2016-02-01

Given an arbitrary elliptic partial differential equation (PDE), a procedure for obtaining its solution is proposed based on the method of Ritz: the solution is written as a linear combination of plane waves and the coefficients are obtained by variational minimization. The PDE to be solved is cast as a system of linear equations Ax=b, where the matrix A is not sparse, which prevents the straightforward application of standard iterative methods in order to solve it. This sparseness problem can be circumvented by means of a recursive bisection approach based on the fast Fourier transform, which makes it possible to implement fast versions of some stationary iterative methods (such as Gauss-Seidel) consuming O(NlogN) memory and executing an iteration in O(Nlog(2)N) time, N being the number of plane waves used. In a similar way, fast versions of Krylov subspace methods and multigrid methods can also be implemented. These procedures are tested on Poisson's equation expressed in adaptive coordinates. It is found that the best results are obtained with the GMRES method using a multigrid preconditioner with Gauss-Seidel relaxation steps.

Multi-Scale Long-Range Magnitude and Sign Correlations in Vertical Upward Oil-Gas-Water Three-Phase Flow

NASA Astrophysics Data System (ADS)

Zhao, An; Jin, Ning-de; Ren, Ying-yu; Zhu, Lei; Yang, Xia

2016-01-01

In this article we apply an approach to identify the oil-gas-water three-phase flow patterns in vertical upwards 20 mm inner-diameter pipe based on the conductance fluctuating signals. We use the approach to analyse the signals with long-range correlations by decomposing the signal increment series into magnitude and sign series and extracting their scaling properties. We find that the magnitude series relates to nonlinear properties of the original time series, whereas the sign series relates to the linear properties. The research shows that the oil-gas-water three-phase flows (slug flow, churn flow, bubble flow) can be classified by a combination of scaling exponents of magnitude and sign series. This study provides a new way of characterising linear and nonlinear properties embedded in oil-gas-water three-phase flows.

Nonlinear aeroservoelastic analysis of a controlled multiple-actuated-wing model with free-play

NASA Astrophysics Data System (ADS)

Huang, Rui; Hu, Haiyan; Zhao, Yonghui

2013-10-01

In this paper, the effects of structural nonlinearity due to free-play in both leading-edge and trailing-edge outboard control surfaces on the linear flutter control system are analyzed for an aeroelastic model of three-dimensional multiple-actuated-wing. The free-play nonlinearities in the control surfaces are modeled theoretically by using the fictitious mass approach. The nonlinear aeroelastic equations of the presented model can be divided into nine sub-linear modal-based aeroelastic equations according to the different combinations of deflections of the leading-edge and trailing-edge outboard control surfaces. The nonlinear aeroelastic responses can be computed based on these sub-linear aeroelastic systems. To demonstrate the effects of nonlinearity on the linear flutter control system, a single-input and single-output controller and a multi-input and multi-output controller are designed based on the unconstrained optimization techniques. The numerical results indicate that the free-play nonlinearity can lead to either limit cycle oscillations or divergent motions when the linear control system is implemented.

Robust model predictive control of nonlinear systems with unmodeled dynamics and bounded uncertainties based on neural networks.

PubMed

Yan, Zheng; Wang, Jun

2014-03-01

This paper presents a neural network approach to robust model predictive control (MPC) for constrained discrete-time nonlinear systems with unmodeled dynamics affected by bounded uncertainties. The exact nonlinear model of underlying process is not precisely known, but a partially known nominal model is available. This partially known nonlinear model is first decomposed to an affine term plus an unknown high-order term via Jacobian linearization. The linearization residue combined with unmodeled dynamics is then modeled using an extreme learning machine via supervised learning. The minimax methodology is exploited to deal with bounded uncertainties. The minimax optimization problem is reformulated as a convex minimization problem and is iteratively solved by a two-layer recurrent neural network. The proposed neurodynamic approach to nonlinear MPC improves the computational efficiency and sheds a light for real-time implementability of MPC technology. Simulation results are provided to substantiate the effectiveness and characteristics of the proposed approach.

Linear-scaling generation of potential energy surfaces using a double incremental expansion

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

König, Carolin, E-mail: carolink@kth.se; Christiansen, Ove, E-mail: ove@chem.au.dk

We present a combination of the incremental expansion of potential energy surfaces (PESs), known as n-mode expansion, with the incremental evaluation of the electronic energy in a many-body approach. The application of semi-local coordinates in this context allows the generation of PESs in a very cost-efficient way. For this, we employ the recently introduced flexible adaptation of local coordinates of nuclei (FALCON) coordinates. By introducing an additional transformation step, concerning only a fraction of the vibrational degrees of freedom, we can achieve linear scaling of the accumulated cost of the single point calculations required in the PES generation. Numerical examplesmore » of these double incremental approaches for oligo-phenyl examples show fast convergence with respect to the maximum number of simultaneously treated fragments and only a modest error introduced by the additional transformation step. The approach, presented here, represents a major step towards the applicability of vibrational wave function methods to sizable, covalently bound systems.« less

An Approach for Automatic Generation of Adaptive Hypermedia in Education with Multilingual Knowledge Discovery Techniques

ERIC Educational Resources Information Center

Alfonseca, Enrique; Rodriguez, Pilar; Perez, Diana

2007-01-01

This work describes a framework that combines techniques from Adaptive Hypermedia and Natural Language processing in order to create, in a fully automated way, on-line information systems from linear texts in electronic format, such as textbooks. The process is divided into two steps: an "off-line" processing step, which analyses the source text,…

Seeking Effectiveness and Equity in a Large College Chemistry Course: An HLM Investigation of Peer-Led Guided Inquiry

ERIC Educational Resources Information Center

Lewis, Scott E.; Lewis, Jennifer E.

2008-01-01

This study employed hierarchical linear models (HLM) to investigate Peer-Led Guided Inquiry (PLGI), a teaching practice combining cooperative learning and inquiry and tailored for a large class. Ultimately, the study provided an example of the effective introduction of a reform pedagogical approach in a large class setting. In the narrative, the…

Construction of Ligand Group Orbitals for Polyatomics and Transition-Metal Complexes Using an Intuitive Symmetry-Based Approach

ERIC Educational Resources Information Center

Johnson, Adam R.

2013-01-01

A molecular orbital (MO) diagram, especially its frontier orbitals, explains the bonding and reactivity for a chemical compound. It is therefore important for students to learn how to construct one. The traditional methods used to derive these diagrams rely on linear algebra techniques to combine ligand orbitals into symmetry-adapted linear…

Nonlinear Fusion of Multispectral Citrus Fruit Image Data with Information Contents.

PubMed

Li, Peilin; Lee, Sang-Heon; Hsu, Hung-Yao; Park, Jae-Sam

2017-01-13

The main issue of vison-based automatic harvesting manipulators is the difficulty in the correct fruit identification in the images under natural lighting conditions. Mostly, the solution has been based on a linear combination of color components in the multispectral images. However, the results have not reached a satisfactory level. To overcome this issue, this paper proposes a robust nonlinear fusion method to augment the original color image with the synchronized near infrared image. The two images are fused with Daubechies wavelet transform (DWT) in a multiscale decomposition approach. With DWT, the background noises are reduced and the necessary image features are enhanced by fusing the color contrast of the color components and the homogeneity of the near infrared (NIR) component. The resulting fused color image is classified with a C-means algorithm for reconstruction. The performance of the proposed approach is evaluated with the statistical F measure in comparison to some existing methods using linear combinations of color components. The results show that the fusion of information in different spectral components has the advantage of enhancing the image quality, therefore improving the classification accuracy in citrus fruit identification in natural lighting conditions.

Nonlinear Fusion of Multispectral Citrus Fruit Image Data with Information Contents

PubMed Central

Li, Peilin; Lee, Sang-Heon; Hsu, Hung-Yao; Park, Jae-Sam

2017-01-01

The main issue of vison-based automatic harvesting manipulators is the difficulty in the correct fruit identification in the images under natural lighting conditions. Mostly, the solution has been based on a linear combination of color components in the multispectral images. However, the results have not reached a satisfactory level. To overcome this issue, this paper proposes a robust nonlinear fusion method to augment the original color image with the synchronized near infrared image. The two images are fused with Daubechies wavelet transform (DWT) in a multiscale decomposition approach. With DWT, the background noises are reduced and the necessary image features are enhanced by fusing the color contrast of the color components and the homogeneity of the near infrared (NIR) component. The resulting fused color image is classified with a C-means algorithm for reconstruction. The performance of the proposed approach is evaluated with the statistical F measure in comparison to some existing methods using linear combinations of color components. The results show that the fusion of information in different spectral components has the advantage of enhancing the image quality, therefore improving the classification accuracy in citrus fruit identification in natural lighting conditions. PMID:28098797

A Least-Squares Commutator in the Iterative Subspace Method for Accelerating Self-Consistent Field Convergence.

PubMed

Li, Haichen; Yaron, David J

2016-11-08

A least-squares commutator in the iterative subspace (LCIIS) approach is explored for accelerating self-consistent field (SCF) calculations. LCIIS is similar to direct inversion of the iterative subspace (DIIS) methods in that the next iterate of the density matrix is obtained as a linear combination of past iterates. However, whereas DIIS methods find the linear combination by minimizing a sum of error vectors, LCIIS minimizes the Frobenius norm of the commutator between the density matrix and the Fock matrix. This minimization leads to a quartic problem that can be solved iteratively through a constrained Newton's method. The relationship between LCIIS and DIIS is discussed. Numerical experiments suggest that LCIIS leads to faster convergence than other SCF convergence accelerating methods in a statistically significant sense, and in a number of cases LCIIS leads to stable SCF solutions that are not found by other methods. The computational cost involved in solving the quartic minimization problem is small compared to the typical cost of SCF iterations and the approach is easily integrated into existing codes. LCIIS can therefore serve as a powerful addition to SCF convergence accelerating methods in computational quantum chemistry packages.

A combined QSAR and partial order ranking approach to risk assessment.

PubMed

Carlsen, L

2006-04-01

QSAR generated data appear as an attractive alternative to experimental data as foreseen in the proposed new chemicals legislation REACH. A preliminary risk assessment for the aquatic environment can be based on few factors, i.e. the octanol-water partition coefficient (Kow), the vapour pressure (VP) and the potential biodegradability of the compound in combination with the predicted no-effect concentration (PNEC) and the actual tonnage in which the substance is produced. Application of partial order ranking, allowing simultaneous inclusion of several parameters leads to a mutual prioritisation of the investigated substances, the prioritisation possibly being further analysed through the concept of linear extensions and average ranks. The ranking uses endpoint values (log Kow and log VP) derived from strictly linear 'noise-deficient' QSAR models as input parameters. Biodegradation estimates were adopted from the BioWin module of the EPI Suite. The population growth impairment of Tetrahymena pyriformis was used as a surrogate for fish lethality.

Robust, nonlinear, high angle-of-attack control design for a supermaneuverable vehicle

NASA Technical Reports Server (NTRS)

Adams, Richard J.

1993-01-01

High angle-of-attack flight control laws are developed for a supermaneuverable fighter aircraft. The methods of dynamic inversion and structured singular value synthesis are combined into an approach which addresses both the nonlinearity and robustness problems of flight at extreme operating conditions. The primary purpose of the dynamic inversion control elements is to linearize the vehicle response across the flight envelope. Structured singular value synthesis is used to design a dynamic controller which provides robust tracking to pilot commands. The resulting control system achieves desired flying qualities and guarantees a large margin of robustness to uncertainties for high angle-of-attack flight conditions. The results of linear simulation and structured singular value stability analysis are presented to demonstrate satisfaction of the design criteria. High fidelity nonlinear simulation results show that the combined dynamics inversion/structured singular value synthesis control law achieves a high level of performance in a realistic environment.

Sparse Coding and Counting for Robust Visual Tracking

PubMed Central

Liu, Risheng; Wang, Jing; Shang, Xiaoke; Wang, Yiyang; Su, Zhixun; Cai, Yu

2016-01-01

In this paper, we propose a novel sparse coding and counting method under Bayesian framework for visual tracking. In contrast to existing methods, the proposed method employs the combination of L0 and L1 norm to regularize the linear coefficients of incrementally updated linear basis. The sparsity constraint enables the tracker to effectively handle difficult challenges, such as occlusion or image corruption. To achieve real-time processing, we propose a fast and efficient numerical algorithm for solving the proposed model. Although it is an NP-hard problem, the proposed accelerated proximal gradient (APG) approach is guaranteed to converge to a solution quickly. Besides, we provide a closed solution of combining L0 and L1 regularized representation to obtain better sparsity. Experimental results on challenging video sequences demonstrate that the proposed method achieves state-of-the-art results both in accuracy and speed. PMID:27992474

Blocked Force and Loading Calculations for LaRC THUNDER Actuators

NASA Technical Reports Server (NTRS)

Campbell, Joel F.

2007-01-01

An analytic approach is developed to predict the performance of LaRC Thunder actuators under load and under blocked conditions. The problem is treated with the Von Karman non-linear analysis combined with a simple Raleigh-Ritz calculation. From this, shape and displacement under load combined with voltage are calculated. A method is found to calculate the blocked force vs voltage and spring force vs distance. It is found that under certain conditions, the blocked force and displacement is almost linear with voltage. It is also found that the spring force is multivalued and has at least one bifurcation point. This bifurcation point is where the device collapses under load and locks to a different bending solution. This occurs at a particular critical load. It is shown this other bending solution has a reduced amplitude and is proportional to the original amplitude times the square of the aspect ratio.

Cyclic Multiblock Copolymers via Combination of Iterative Cu(0)-Mediated Radical Polymerization and Cu(I)-Catalyzed Azide-Alkyne Cycloaddition Reaction.

PubMed

Xiao, Lifen; Zhu, Wen; Chen, Jiqiang; Zhang, Ke

2017-02-01

Cyclic multiblock polymers with high-order blocks are synthesized via the combination of single-electron transfer living radical polymerization (SET-LRP) and copper-catalyzed azide-alkyne cycloaddition (CuAAC). The linear α,ω-telechelic multiblock copolymer is prepared via SET-LRP by sequential addition of different monomers. The SET-LRP approach allows well control of the block length and sequence as A-B-C-D-E, etc. The CuAAC is then performed to intramolecularly couple the azide and alkyne end groups of the linear copolymer and produce the corresponding cyclic copolymer. The block sequence and the cyclic topology of the resultant cyclic copolymer are confirmed by the characterization of 1 H nuclear magnetic resonance spectroscopy, gel permeation chromatography, Fourier transform infrared spectroscopy, and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Estimation of annual energy production using dynamic wake meandering in combination with ambient CFD solutions

NASA Astrophysics Data System (ADS)

Hahn, S.; Machefaux, E.; Hristov, Y. V.; Albano, M.; Threadgill, R.

2016-09-01

In the present study, combination of the standalone dynamic wake meandering (DWM) model with Reynolds-averaged Navier-Stokes (RANS) CFD solutions for ambient ABL flows is introduced, and its predictive performance for annual energy production (AEP) is evaluated against Vestas’ SCADA data for six operating wind farms over semi-complex terrains under neutral conditions. The performances of conventional linear and quadratic wake superposition techniques are also compared, together with the in-house implemention of successive hierarchical merging approaches. As compared to our standard procedure based on the Jensen model in WindPRO, the overall results are promising, leading to a significant improvement in AEP accuracy for four of the six sites. While the conventional linear superposition shows the best performance for the improved four sites, the hierarchical square superposition shows the least deteriorated result for the other two sites.

Probabilistic estimation of splitting coefficients of normal modes of the Earth, and their uncertainties, using an autoregressive technique

NASA Astrophysics Data System (ADS)

Pachhai, S.; Masters, G.; Laske, G.

2017-12-01

Earth's normal-mode spectra are crucial to studying the long wavelength structure of the Earth. Such observations have been used extensively to estimate "splitting coefficients" which, in turn, can be used to determine the three-dimensional velocity and density structure. Most past studies apply a non-linear iterative inversion to estimate the splitting coefficients which requires that the earthquake source is known. However, it is challenging to know the source details, particularly for big events as used in normal-mode analyses. Additionally, the final solution of the non-linear inversion can depend on the choice of damping parameter and starting model. To circumvent the need to know the source, a two-step linear inversion has been developed and successfully applied to many mantle and core sensitive modes. The first step takes combinations of the data from a single event to produce spectra known as "receiver strips". The autoregressive nature of the receiver strips can then be used to estimate the structure coefficients without the need to know the source. Based on this approach, we recently employed a neighborhood algorithm to measure the splitting coefficients for an isolated inner-core sensitive mode (13S2). This approach explores the parameter space efficiently without any need of regularization and finds the structure coefficients which best fit the observed strips. Here, we implement a Bayesian approach to data collected for earthquakes from early 2000 and more recent. This approach combines the data (through likelihood) and prior information to provide rigorous parameter values and their uncertainties for both isolated and coupled modes. The likelihood function is derived from the inferred errors of the receiver strips which allows us to retrieve proper uncertainties. Finally, we apply model selection criteria that balance the trade-offs between fit (likelihood) and model complexity to investigate the degree and type of structure (elastic and anelastic) required to explain the data.

Low-Rank Correction Methods for Algebraic Domain Decomposition Preconditioners

DOE PAGES

Li, Ruipeng; Saad, Yousef

2017-08-01

This study presents a parallel preconditioning method for distributed sparse linear systems, based on an approximate inverse of the original matrix, that adopts a general framework of distributed sparse matrices and exploits domain decomposition (DD) and low-rank corrections. The DD approach decouples the matrix and, once inverted, a low-rank approximation is applied by exploiting the Sherman--Morrison--Woodbury formula, which yields two variants of the preconditioning methods. The low-rank expansion is computed by the Lanczos procedure with reorthogonalizations. Numerical experiments indicate that, when combined with Krylov subspace accelerators, this preconditioner can be efficient and robust for solving symmetric sparse linear systems. Comparisonsmore » with pARMS, a DD-based parallel incomplete LU (ILU) preconditioning method, are presented for solving Poisson's equation and linear elasticity problems.« less

Low-Rank Correction Methods for Algebraic Domain Decomposition Preconditioners

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

Li, Ruipeng; Saad, Yousef

This study presents a parallel preconditioning method for distributed sparse linear systems, based on an approximate inverse of the original matrix, that adopts a general framework of distributed sparse matrices and exploits domain decomposition (DD) and low-rank corrections. The DD approach decouples the matrix and, once inverted, a low-rank approximation is applied by exploiting the Sherman--Morrison--Woodbury formula, which yields two variants of the preconditioning methods. The low-rank expansion is computed by the Lanczos procedure with reorthogonalizations. Numerical experiments indicate that, when combined with Krylov subspace accelerators, this preconditioner can be efficient and robust for solving symmetric sparse linear systems. Comparisonsmore » with pARMS, a DD-based parallel incomplete LU (ILU) preconditioning method, are presented for solving Poisson's equation and linear elasticity problems.« less

An SVM model with hybrid kernels for hydrological time series

NASA Astrophysics Data System (ADS)

Wang, C.; Wang, H.; Zhao, X.; Xie, Q.

2017-12-01

Support Vector Machine (SVM) models have been widely applied to the forecast of climate/weather and its impact on other environmental variables such as hydrologic response to climate/weather. When using SVM, the choice of the kernel function plays the key role. Conventional SVM models mostly use one single type of kernel function, e.g., radial basis kernel function. Provided that there are several featured kernel functions available, each having its own advantages and drawbacks, a combination of these kernel functions may give more flexibility and robustness to SVM approach, making it suitable for a wide range of application scenarios. This paper presents such a linear combination of radial basis kernel and polynomial kernel for the forecast of monthly flowrate in two gaging stations using SVM approach. The results indicate significant improvement in the accuracy of predicted series compared to the approach with either individual kernel function, thus demonstrating the feasibility and advantages of such hybrid kernel approach for SVM applications.

Power calculations for likelihood ratio tests for offspring genotype risks, maternal effects, and parent-of-origin (POO) effects in the presence of missing parental genotypes when unaffected siblings are available.

PubMed

Rampersaud, E; Morris, R W; Weinberg, C R; Speer, M C; Martin, E R

2007-01-01

Genotype-based likelihood-ratio tests (LRT) of association that examine maternal and parent-of-origin effects have been previously developed in the framework of log-linear and conditional logistic regression models. In the situation where parental genotypes are missing, the expectation-maximization (EM) algorithm has been incorporated in the log-linear approach to allow incomplete triads to contribute to the LRT. We present an extension to this model which we call the Combined_LRT that incorporates additional information from the genotypes of unaffected siblings to improve assignment of incompletely typed families to mating type categories, thereby improving inference of missing parental data. Using simulations involving a realistic array of family structures, we demonstrate the validity of the Combined_LRT under the null hypothesis of no association and provide power comparisons under varying levels of missing data and using sibling genotype data. We demonstrate the improved power of the Combined_LRT compared with the family-based association test (FBAT), another widely used association test. Lastly, we apply the Combined_LRT to a candidate gene analysis in Autism families, some of which have missing parental genotypes. We conclude that the proposed log-linear model will be an important tool for future candidate gene studies, for many complex diseases where unaffected siblings can often be ascertained and where epigenetic factors such as imprinting may play a role in disease etiology.

Geometric mean for subspace selection.

PubMed

Tao, Dacheng; Li, Xuelong; Wu, Xindong; Maybank, Stephen J

2009-02-01

Subspace selection approaches are powerful tools in pattern classification and data visualization. One of the most important subspace approaches is the linear dimensionality reduction step in the Fisher's linear discriminant analysis (FLDA), which has been successfully employed in many fields such as biometrics, bioinformatics, and multimedia information management. However, the linear dimensionality reduction step in FLDA has a critical drawback: for a classification task with c classes, if the dimension of the projected subspace is strictly lower than c - 1, the projection to a subspace tends to merge those classes, which are close together in the original feature space. If separate classes are sampled from Gaussian distributions, all with identical covariance matrices, then the linear dimensionality reduction step in FLDA maximizes the mean value of the Kullback-Leibler (KL) divergences between different classes. Based on this viewpoint, the geometric mean for subspace selection is studied in this paper. Three criteria are analyzed: 1) maximization of the geometric mean of the KL divergences, 2) maximization of the geometric mean of the normalized KL divergences, and 3) the combination of 1 and 2. Preliminary experimental results based on synthetic data, UCI Machine Learning Repository, and handwriting digits show that the third criterion is a potential discriminative subspace selection method, which significantly reduces the class separation problem in comparing with the linear dimensionality reduction step in FLDA and its several representative extensions.

Determining polarizable force fields with electrostatic potentials from quantum mechanical linear response theory

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

Wang, Hao; Yang, Weitao, E-mail: weitao.yang@duke.edu; Department of Physics, Duke University, Durham, North Carolina 27708

We developed a new method to calculate the atomic polarizabilities by fitting to the electrostatic potentials (ESPs) obtained from quantum mechanical (QM) calculations within the linear response theory. This parallels the conventional approach of fitting atomic charges based on electrostatic potentials from the electron density. Our ESP fitting is combined with the induced dipole model under the perturbation of uniform external electric fields of all orientations. QM calculations for the linear response to the external electric fields are used as input, fully consistent with the induced dipole model, which itself is a linear response model. The orientation of the uniformmore » external electric fields is integrated in all directions. The integration of orientation and QM linear response calculations together makes the fitting results independent of the orientations and magnitudes of the uniform external electric fields applied. Another advantage of our method is that QM calculation is only needed once, in contrast to the conventional approach, where many QM calculations are needed for many different applied electric fields. The molecular polarizabilities obtained from our method show comparable accuracy with those from fitting directly to the experimental or theoretical molecular polarizabilities. Since ESP is directly fitted, atomic polarizabilities obtained from our method are expected to reproduce the electrostatic interactions better. Our method was used to calculate both transferable atomic polarizabilities for polarizable molecular mechanics’ force fields and nontransferable molecule-specific atomic polarizabilities.« less

Linear Estimation of Particle Bulk Parameters from Multi-Wavelength Lidar Measurements

NASA Technical Reports Server (NTRS)

Veselovskii, Igor; Dubovik, Oleg; Kolgotin, A.; Korenskiy, M.; Whiteman, D. N.; Allakhverdiev, K.; Huseyinoglu, F.

2012-01-01

An algorithm for linear estimation of aerosol bulk properties such as particle volume, effective radius and complex refractive index from multiwavelength lidar measurements is presented. The approach uses the fact that the total aerosol concentration can well be approximated as a linear combination of aerosol characteristics measured by multiwavelength lidar. Therefore, the aerosol concentration can be estimated from lidar measurements without the need to derive the size distribution, which entails more sophisticated procedures. The definition of the coefficients required for the linear estimates is based on an expansion of the particle size distribution in terms of the measurement kernels. Once the coefficients are established, the approach permits fast retrieval of aerosol bulk properties when compared with the full regularization technique. In addition, the straightforward estimation of bulk properties stabilizes the inversion making it more resistant to noise in the optical data. Numerical tests demonstrate that for data sets containing three aerosol backscattering and two extinction coefficients (so called 3 + 2 ) the uncertainties in the retrieval of particle volume and surface area are below 45% when input data random uncertainties are below 20 %. Moreover, using linear estimates allows reliable retrievals even when the number of input data is reduced. To evaluate the approach, the results obtained using this technique are compared with those based on the previously developed full inversion scheme that relies on the regularization procedure. Both techniques were applied to the data measured by multiwavelength lidar at NASA/GSFC. The results obtained with both methods using the same observations are in good agreement. At the same time, the high speed of the retrieval using linear estimates makes the method preferable for generating aerosol information from extended lidar observations. To demonstrate the efficiency of the method, an extended time series of observations acquired in Turkey in May 2010 was processed using the linear estimates technique permitting, for what we believe to be the first time, temporal-height distributions of particle parameters.

A modal aeroelastic analysis scheme for turbomachinery blading. M.S. Thesis - Case Western Reserve Univ. Final Report

NASA Technical Reports Server (NTRS)

Smith, Todd E.

1991-01-01

An aeroelastic analysis is developed which has general application to all types of axial-flow turbomachinery blades. The approach is based on linear modal analysis, where the blade's dynamic response is represented as a linear combination of contributions from each of its in-vacuum free vibrational modes. A compressible linearized unsteady potential theory is used to model the flow over the oscillating blades. The two-dimensional unsteady flow is evaluated along several stacked axisymmetric strips along the span of the airfoil. The unsteady pressures at the blade surface are integrated to result in the generalized force acting on the blade due to simple harmonic motions. The unsteady aerodynamic forces are coupled to the blade normal modes in the frequency domain using modal analysis. An iterative eigenvalue problem is solved to determine the stability of the blade when the unsteady aerodynamic forces are included in the analysis. The approach is demonstrated by applying it to a high-energy subsonic turbine blade from a rocket engine turbopump power turbine. The results indicate that this turbine could undergo flutter in an edgewise mode of vibration.

[A novel quantitative approach to study dynamic anaerobic process at micro scale].

PubMed

Zhang, Zhong-Liang; Wu, Jing; Jiang, Jian-Kai; Jiang, Jie; Li, Huai-Zhi

2012-11-01

Anaerobic digestion is attracting more and more interests because of its advantages such as low cost and recovery of clean energy etc. In order to overcome the drawbacks of the existed methods to study the dynamic anaerobic process, a novel microscopical quantitative approach at the granule level was developed combining both the microdevice and the quantitative image analysis techniques. This experiment displayed the process and characteristics of the gas production at static state for the first time and the results indicated that the method was of satisfactory repeatability. The gas production process at static state could be divided into three stages including rapid linear increasing stage, decelerated increasing stage and slow linear increasing stage. The rapid linear increasing stage was long and the biogas rate was high under high initial organic loading rate. The results showed that it was feasible to make the anaerobic process to be carried out in the microdevice; furthermore this novel method was reliable and could clearly display the dynamic process of the anaerobic reaction at the micro scale. The results are helpful to understand the anaerobic process.

Coupled dimensionality reduction and classification for supervised and semi-supervised multilabel learning

PubMed Central

Gönen, Mehmet

2014-01-01

Coupled training of dimensionality reduction and classification is proposed previously to improve the prediction performance for single-label problems. Following this line of research, in this paper, we first introduce a novel Bayesian method that combines linear dimensionality reduction with linear binary classification for supervised multilabel learning and present a deterministic variational approximation algorithm to learn the proposed probabilistic model. We then extend the proposed method to find intrinsic dimensionality of the projected subspace using automatic relevance determination and to handle semi-supervised learning using a low-density assumption. We perform supervised learning experiments on four benchmark multilabel learning data sets by comparing our method with baseline linear dimensionality reduction algorithms. These experiments show that the proposed approach achieves good performance values in terms of hamming loss, average AUC, macro F1, and micro F1 on held-out test data. The low-dimensional embeddings obtained by our method are also very useful for exploratory data analysis. We also show the effectiveness of our approach in finding intrinsic subspace dimensionality and semi-supervised learning tasks. PMID:24532862

Coupled dimensionality reduction and classification for supervised and semi-supervised multilabel learning.

PubMed

Gönen, Mehmet

2014-03-01

Coupled training of dimensionality reduction and classification is proposed previously to improve the prediction performance for single-label problems. Following this line of research, in this paper, we first introduce a novel Bayesian method that combines linear dimensionality reduction with linear binary classification for supervised multilabel learning and present a deterministic variational approximation algorithm to learn the proposed probabilistic model. We then extend the proposed method to find intrinsic dimensionality of the projected subspace using automatic relevance determination and to handle semi-supervised learning using a low-density assumption. We perform supervised learning experiments on four benchmark multilabel learning data sets by comparing our method with baseline linear dimensionality reduction algorithms. These experiments show that the proposed approach achieves good performance values in terms of hamming loss, average AUC, macro F 1 , and micro F 1 on held-out test data. The low-dimensional embeddings obtained by our method are also very useful for exploratory data analysis. We also show the effectiveness of our approach in finding intrinsic subspace dimensionality and semi-supervised learning tasks.

Degree of coherence for vectorial electromagnetic fields as the distance between correlation matrices.

PubMed

Luis, Alfredo

2007-04-01

We assess the degree of coherence of vectorial electromagnetic fields in the space-frequency domain as the distance between the cross-spectral density matrix and the identity matrix representing completely incoherent light. This definition is compared with previous approaches. It is shown that this distance provides an upper bound for the degree of coherence and visibility for any pair of scalar waves obtained by linear combinations of the original fields. This same approach emerges when applying a previous definition of global coherence to a Young interferometer.

Space construction base control system

NASA Technical Reports Server (NTRS)

Kaczynski, R. F.

1979-01-01

Several approaches for an attitude control system are studied and developed for a large space construction base that is structurally flexible. Digital simulations were obtained using the following techniques: (1) the multivariable Nyquist array method combined with closed loop pole allocation, (2) the linear quadratic regulator method. Equations for the three-axis simulation using the multilevel control method were generated and are presented. Several alternate control approaches are also described. A technique is demonstrated for obtaining the dynamic structural properties of a vehicle which is constructed of two or more submodules of known dynamic characteristics.

Pattern Recognition Approaches for Breast Cancer DCE-MRI Classification: A Systematic Review.

PubMed

Fusco, Roberta; Sansone, Mario; Filice, Salvatore; Carone, Guglielmo; Amato, Daniela Maria; Sansone, Carlo; Petrillo, Antonella

2016-01-01

We performed a systematic review of several pattern analysis approaches for classifying breast lesions using dynamic, morphological, and textural features in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Several machine learning approaches, namely artificial neural networks (ANN), support vector machines (SVM), linear discriminant analysis (LDA), tree-based classifiers (TC), and Bayesian classifiers (BC), and features used for classification are described. The findings of a systematic review of 26 studies are presented. The sensitivity and specificity are respectively 91 and 83 % for ANN, 85 and 82 % for SVM, 96 and 85 % for LDA, 92 and 87 % for TC, and 82 and 85 % for BC. The sensitivity and specificity are respectively 82 and 74 % for dynamic features, 93 and 60 % for morphological features, 88 and 81 % for textural features, 95 and 86 % for a combination of dynamic and morphological features, and 88 and 84 % for a combination of dynamic, morphological, and other features. LDA and TC have the best performance. A combination of dynamic and morphological features gives the best performance.

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

PubMed

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

2013-01-01

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

Tight-binding approximations to time-dependent density functional theory — A fast approach for the calculation of electronically excited states

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

Rüger, Robert, E-mail: rueger@scm.com; Department of Theoretical Chemistry, Vrije Universiteit Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam; Wilhelm-Ostwald-Institut für Physikalische und Theoretische Chemie, Linnéstr. 2, 04103 Leipzig

2016-05-14

We propose a new method of calculating electronically excited states that combines a density functional theory based ground state calculation with a linear response treatment that employs approximations used in the time-dependent density functional based tight binding (TD-DFTB) approach. The new method termed time-dependent density functional theory TD-DFT+TB does not rely on the DFTB parametrization and is therefore applicable to systems involving all combinations of elements. We show that the new method yields UV/Vis absorption spectra that are in excellent agreement with computationally much more expensive TD-DFT calculations. Errors in vertical excitation energies are reduced by a factor of twomore » compared to TD-DFTB.« less

A Linear Bicharacteristic FDTD Method

NASA Technical Reports Server (NTRS)

Beggs, John H.

2001-01-01

The linear bicharacteristic scheme (LBS) was originally developed to improve unsteady solutions in computational acoustics and aeroacoustics [1]-[7]. It is a classical leapfrog algorithm, but is combined with upwind bias in the spatial derivatives. This approach preserves the time-reversibility of the leapfrog algorithm, which results in no dissipation, and it permits more flexibility by the ability to adopt a characteristic based method. The use of characteristic variables allows the LBS to treat the outer computational boundaries naturally using the exact compatibility equations. The LBS offers a central storage approach with lower dispersion than the Yee algorithm, plus it generalizes much easier to nonuniform grids. It has previously been applied to two and three-dimensional freespace electromagnetic propagation and scattering problems [3], [6], [7]. This paper extends the LBS to model lossy dielectric and magnetic materials. Results are presented for several one-dimensional model problems, and the FDTD algorithm is chosen as a convenient reference for comparison.

Modeling and Analysis of Micro-Spacecraft Attitude Sensing with Gyrowheel.

PubMed

Liu, Xiaokun; Zhao, Hui; Yao, Yu; He, Fenghua

2016-08-19

This paper proposes two kinds of approaches of angular rate sensing for micro-spacecraft with a gyrowheel (GW), which can combine attitude sensing with attitude control into one single device to achieve a compact micro-spacecraft design. In this implementation, during the three-dimensional attitude control torques being produced, two-dimensional spacecraft angular rates can be sensed from the signals of the GW sensors, such as the currents of the torque coils, the tilt angles of the rotor, the motor rotation, etc. This paper focuses on the problems of the angular rate sensing with the GW at large tilt angles of the rotor. For this purpose, a novel real-time linearization approach based on Lyapunov's linearization theory is proposed, and a GW linearized measurement model at arbitrary tilt angles of the rotor is derived. Furthermore, by representing the two-dimensional rotor tilt angles and tilt control torques as complex quantities and separating the twice periodic terms about the motor spin speed, the linearized measurement model at smaller tilt angles of the rotor is given and simplified. According to the respective characteristics, the application schemes of the two measurement models are analyzed from the engineering perspective. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed strategy.

Modeling and Analysis of Micro-Spacecraft Attitude Sensing with Gyrowheel

PubMed Central

Liu, Xiaokun; Zhao, Hui; Yao, Yu; He, Fenghua

2016-01-01

This paper proposes two kinds of approaches of angular rate sensing for micro-spacecraft with a gyrowheel (GW), which can combine attitude sensing with attitude control into one single device to achieve a compact micro-spacecraft design. In this implementation, during the three-dimensional attitude control torques being produced, two-dimensional spacecraft angular rates can be sensed from the signals of the GW sensors, such as the currents of the torque coils, the tilt angles of the rotor, the motor rotation, etc. This paper focuses on the problems of the angular rate sensing with the GW at large tilt angles of the rotor. For this purpose, a novel real-time linearization approach based on Lyapunov’s linearization theory is proposed, and a GW linearized measurement model at arbitrary tilt angles of the rotor is derived. Furthermore, by representing the two-dimensional rotor tilt angles and tilt control torques as complex quantities and separating the twice periodic terms about the motor spin speed, the linearized measurement model at smaller tilt angles of the rotor is given and simplified. According to the respective characteristics, the application schemes of the two measurement models are analyzed from the engineering perspective. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed strategy. PMID:27548178

A non-linear piezoelectric actuator calibration using N-dimensional Lissajous figure

NASA Astrophysics Data System (ADS)

Albertazzi, A.; Viotti, M. R.; Veiga, C. L. N.; Fantin, A. V.

2016-08-01

Piezoelectric translators (PZTs) are very often used as phase shifters in interferometry. However, they typically present a non-linear behavior and strong hysteresis. The use of an additional resistive or capacitive sensor make possible to linearize the response of the PZT by feedback control. This approach works well, but makes the device more complex and expensive. A less expensive approach uses a non-linear calibration. In this paper, the authors used data from at least five interferograms to form N-dimensional Lissajous figures to establish the actual relationship between the applied voltages and the resulting phase shifts [1]. N-dimensional Lissajous figures are formed when N sinusoidal signals are combined in an N-dimensional space, where one signal is assigned to each axis. It can be verified that the resulting Ndimensional ellipsis lays in a 2D plane. By fitting an ellipsis equation to the resulting 2D ellipsis it is possible to accurately compute the resulting phase value for each interferogram. In this paper, the relationship between the resulting phase shift and the applied voltage is simultaneously established for a set of 12 increments by a fourth degree polynomial. The results in speckle interferometry show that, after two or three interactions, the calibration error is usually smaller than 1°.

Recognition by Linear Combination of Models

DTIC Science & Technology

1989-08-01

to the model (or to the viewed object) prior to, or during the matching stage. Such an approach is used in [Chien & Aggarwal 1987 , Faugeras & Hebert...1986, Fishler & Bolles 1981, Huttenlocher & Ullman 1987 , Lowe 1985, Thompson & Mundy 1987 , Ullman 1986]. Key problems that arise in any alignment...cludes 3-D rotation, translation and scaling, followed by an orthographic projection. The 1 transformation is determined as in [Huttenlocher & Ullman 1987

A powerful and flexible approach to the analysis of RNA sequence count data.

PubMed

Zhou, Yi-Hui; Xia, Kai; Wright, Fred A

2011-10-01

A number of penalization and shrinkage approaches have been proposed for the analysis of microarray gene expression data. Similar techniques are now routinely applied to RNA sequence transcriptional count data, although the value of such shrinkage has not been conclusively established. If penalization is desired, the explicit modeling of mean-variance relationships provides a flexible testing regimen that 'borrows' information across genes, while easily incorporating design effects and additional covariates. We describe BBSeq, which incorporates two approaches: (i) a simple beta-binomial generalized linear model, which has not been extensively tested for RNA-Seq data and (ii) an extension of an expression mean-variance modeling approach to RNA-Seq data, involving modeling of the overdispersion as a function of the mean. Our approaches are flexible, allowing for general handling of discrete experimental factors and continuous covariates. We report comparisons with other alternate methods to handle RNA-Seq data. Although penalized methods have advantages for very small sample sizes, the beta-binomial generalized linear model, combined with simple outlier detection and testing approaches, appears to have favorable characteristics in power and flexibility. An R package containing examples and sample datasets is available at http://www.bios.unc.edu/research/genomic_software/BBSeq yzhou@bios.unc.edu; fwright@bios.unc.edu Supplementary data are available at Bioinformatics online.

Protograph LDPC Codes with Node Degrees at Least 3

NASA Technical Reports Server (NTRS)

Divsalar, Dariush; Jones, Christopher

2006-01-01

In this paper we present protograph codes with a small number of degree-3 nodes and one high degree node. The iterative decoding threshold for proposed rate 1/2 codes are lower, by about 0.2 dB, than the best known irregular LDPC codes with degree at least 3. The main motivation is to gain linear minimum distance to achieve low error floor. Also to construct rate-compatible protograph-based LDPC codes for fixed block length that simultaneously achieves low iterative decoding threshold and linear minimum distance. We start with a rate 1/2 protograph LDPC code with degree-3 nodes and one high degree node. Higher rate codes are obtained by connecting check nodes with degree-2 non-transmitted nodes. This is equivalent to constraint combining in the protograph. The condition where all constraints are combined corresponds to the highest rate code. This constraint must be connected to nodes of degree at least three for the graph to have linear minimum distance. Thus having node degree at least 3 for rate 1/2 guarantees linear minimum distance property to be preserved for higher rates. Through examples we show that the iterative decoding threshold as low as 0.544 dB can be achieved for small protographs with node degrees at least three. A family of low- to high-rate codes with minimum distance linearly increasing in block size and with capacity-approaching performance thresholds is presented. FPGA simulation results for a few example codes show that the proposed codes perform as predicted.

Statistical simulation of ensembles of precipitation fields for data assimilation applications

NASA Astrophysics Data System (ADS)

Haese, Barbara; Hörning, Sebastian; Chwala, Christian; Bárdossy, András; Schalge, Bernd; Kunstmann, Harald

2017-04-01

The simulation of the hydrological cycle by models is an indispensable tool for a variety of environmental challenges such as climate prediction, water resources management, or flood forecasting. One of the crucial variables within the hydrological system, and accordingly one of the main drivers for terrestrial hydrological processes, is precipitation. A correct reproduction of the spatio-temporal distribution of precipitation is crucial for the quality and performance of hydrological applications. In our approach we stochastically generate precipitation fields conditioned on various precipitation observations. Rain gauges provide high-quality information for a specific measurement point, but their spatial representativeness is often rare. Microwave links, e. g. from commercial cellular operators, on the other hand can be used to estimate line integrals of near-surface rainfall information. They provide a very dense observational system compared to rain gauges. A further prevalent source of precipitation information are weather radars, which provide rainfall pattern informations. In our approach we derive precipitation fields, which are conditioned on combinations of these different observation types. As method to generate precipitation fields we use the random mixing method. Following this method a precipitation field is received as a linear combination of unconditional spatial random fields, where the spatial dependence structure is described by copulas. The weights of the linear combination are chosen in the way that the observations and the spatial structure of precipitation are reproduced. One main advantage of the random mixing method is the opportunity to consider linear and non-linear constraints. For a demonstration of the method we use virtual observations generated from a virtual reality of the Neckar catchment. These virtual observations mimic advantages and disadvantages of real observations. This virtual data set allows us to evaluate simulated precipitation fields in a very detailed manner as well as to quantify uncertainties which are conveyed by measurement inaccuracies. In a further step we use real observations as a basis for the generation of precipitation fields. The resulting ensembles of precipitation fields are used for example for data assimilation applications or as input data for hydrological models.

Optimization of classification and regression analysis of four monoclonal antibodies from Raman spectra using collaborative machine learning approach.

PubMed

Le, Laetitia Minh Maï; Kégl, Balázs; Gramfort, Alexandre; Marini, Camille; Nguyen, David; Cherti, Mehdi; Tfaili, Sana; Tfayli, Ali; Baillet-Guffroy, Arlette; Prognon, Patrice; Chaminade, Pierre; Caudron, Eric

2018-07-01

The use of monoclonal antibodies (mAbs) constitutes one of the most important strategies to treat patients suffering from cancers such as hematological malignancies and solid tumors. These antibodies are prescribed by the physician and prepared by hospital pharmacists. An analytical control enables the quality of the preparations to be ensured. The aim of this study was to explore the development of a rapid analytical method for quality control. The method used four mAbs (Infliximab, Bevacizumab, Rituximab and Ramucirumab) at various concentrations and was based on recording Raman data and coupling them to a traditional chemometric and machine learning approach for data analysis. Compared to conventional linear approach, prediction errors are reduced with a data-driven approach using statistical machine learning methods. In the latter, preprocessing and predictive models are jointly optimized. An additional original aspect of the work involved on submitting the problem to a collaborative data challenge platform called Rapid Analytics and Model Prototyping (RAMP). This allowed using solutions from about 300 data scientists in collaborative work. Using machine learning, the prediction of the four mAbs samples was considerably improved. The best predictive model showed a combined error of 2.4% versus 14.6% using linear approach. The concentration and classification errors were 5.8% and 0.7%, only three spectra were misclassified over the 429 spectra of the test set. This large improvement obtained with machine learning techniques was uniform for all molecules but maximal for Bevacizumab with an 88.3% reduction on combined errors (2.1% versus 17.9%). Copyright © 2018 Elsevier B.V. All rights reserved.

Integrating Multiple Correlated Phenotypes for Genetic Association Analysis by Maximizing Heritability

PubMed Central

Zhou, Jin J.; Cho, Michael H.; Lange, Christoph; Lutz, Sharon; Silverman, Edwin K.; Laird, Nan M.

2015-01-01

Many correlated disease variables are analyzed jointly in genetic studies in the hope of increasing power to detect causal genetic variants. One approach involves assessing the relationship between each phenotype and each single nucleotide polymorphism (SNP) individually and using a Bonferroni correction for the effective number of tests conducted. Alternatively, one can apply a multivariate regression or a dimension reduction technique, such as principal component analysis (PCA), and test for the association with the principal components (PC) of the phenotypes rather than the individual phenotypes. Inspired by the previous approaches of combining phenotypes to maximize heritability at individual SNPs, in this paper, we propose to construct a maximally heritable phenotype (MaxH) by taking advantage of the estimated total heritability and co-heritability. The heritability and co-heritability only need to be estimated once, therefore our method is applicable to genome-wide scans. MaxH phenotype is a linear combination of the individual phenotypes with increased heritability and power over the phenotypes being combined. Simulations show that the heritability and power achieved agree well with the theory for large samples and two phenotypes. We compare our approach with commonly used methods and assess both the heritability and the power of the MaxH phenotype. Moreover we provide suggestions for how to choose the phenotypes for combination. An application of our approach to a COPD genome-wide association study shows the practical relevance. PMID:26111731

LPV Controller Interpolation for Improved Gain-Scheduling Control Performance

NASA Technical Reports Server (NTRS)

Wu, Fen; Kim, SungWan

2002-01-01

In this paper, a new gain-scheduling control design approach is proposed by combining LPV (linear parameter-varying) control theory with interpolation techniques. The improvement of gain-scheduled controllers can be achieved from local synthesis of Lyapunov functions and continuous construction of a global Lyapunov function by interpolation. It has been shown that this combined LPV control design scheme is capable of improving closed-loop performance derived from local performance improvement. The gain of the LPV controller will also change continuously across parameter space. The advantages of the newly proposed LPV control is demonstrated through a detailed AMB controller design example.

Serenity: A subsystem quantum chemistry program.

PubMed

Unsleber, Jan P; Dresselhaus, Thomas; Klahr, Kevin; Schnieders, David; Böckers, Michael; Barton, Dennis; Neugebauer, Johannes

2018-05-15

We present the new quantum chemistry program Serenity. It implements a wide variety of functionalities with a focus on subsystem methodology. The modular code structure in combination with publicly available external tools and particular design concepts ensures extensibility and robustness with a focus on the needs of a subsystem program. Several important features of the program are exemplified with sample calculations with subsystem density-functional theory, potential reconstruction techniques, a projection-based embedding approach and combinations thereof with geometry optimization, semi-numerical frequency calculations and linear-response time-dependent density-functional theory. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

Tight-binding model for borophene and borophane

NASA Astrophysics Data System (ADS)

Nakhaee, M.; Ketabi, S. A.; Peeters, F. M.

2018-03-01

Starting from the simplified linear combination of atomic orbitals method in combination with first-principles calculations, we construct a tight-binding (TB) model in the two-centre approximation for borophene and hydrogenated borophene (borophane). The Slater and Koster approach is applied to calculate the TB Hamiltonian of these systems. We obtain expressions for the Hamiltonian and overlap matrix elements between different orbitals for the different atoms and present the SK coefficients in a nonorthogonal basis set. An anisotropic Dirac cone is found in the band structure of borophane. We derive a Dirac low-energy Hamiltonian and compare the Fermi velocities with that of graphene.

Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands.

PubMed

Salem, Salem Ibrahim; Higa, Hiroto; Kim, Hyungjun; Kobayashi, Hiroshi; Oki, Kazuo; Oki, Taikan

2017-07-31

Numerous algorithms have been proposed to retrieve chlorophyll- a concentrations in Case 2 waters; however, the retrieval accuracy is far from satisfactory. In this research, seven algorithms are assessed with different band combinations of multispectral and hyperspectral bands using linear (LN), quadratic polynomial (QP) and power (PW) regression approaches, resulting in altogether 43 algorithmic combinations. These algorithms are evaluated by using simulated and measured datasets to understand the strengths and limitations of these algorithms. Two simulated datasets comprising 500,000 reflectance spectra each, both based on wide ranges of inherent optical properties (IOPs), are generated for the calibration and validation stages. Results reveal that the regression approach (i.e., LN, QP, and PW) has more influence on the simulated dataset than on the measured one. The algorithms that incorporated linear regression provide the highest retrieval accuracy for the simulated dataset. Results from simulated datasets reveal that the 3-band (3b) algorithm that incorporate 665-nm and 680-nm bands and band tuning selection approach outperformed other algorithms with root mean square error (RMSE) of 15.87 mg·m -3 , 16.25 mg·m -3 , and 19.05 mg·m -3 , respectively. The spatial distribution of the best performing algorithms, for various combinations of chlorophyll- a (Chla) and non-algal particles (NAP) concentrations, show that the 3b_tuning_QP and 3b_680_QP outperform other algorithms in terms of minimum RMSE frequency of 33.19% and 60.52%, respectively. However, the two algorithms failed to accurately retrieve Chla for many combinations of Chla and NAP, particularly for low Chla and NAP concentrations. In addition, the spatial distribution emphasizes that no single algorithm can provide outstanding accuracy for Chla retrieval and that multi-algorithms should be included to reduce the error. Comparing the results of the measured and simulated datasets reveal that the algorithms that incorporate the 665-nm band outperform other algorithms for measured dataset (RMSE = 36.84 mg·m -3 ), while algorithms that incorporate the band tuning approach provide the highest retrieval accuracy for the simulated dataset (RMSE = 25.05 mg·m -3 ).

Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands

PubMed Central

Higa, Hiroto; Kobayashi, Hiroshi; Oki, Kazuo

2017-01-01

Numerous algorithms have been proposed to retrieve chlorophyll-a concentrations in Case 2 waters; however, the retrieval accuracy is far from satisfactory. In this research, seven algorithms are assessed with different band combinations of multispectral and hyperspectral bands using linear (LN), quadratic polynomial (QP) and power (PW) regression approaches, resulting in altogether 43 algorithmic combinations. These algorithms are evaluated by using simulated and measured datasets to understand the strengths and limitations of these algorithms. Two simulated datasets comprising 500,000 reflectance spectra each, both based on wide ranges of inherent optical properties (IOPs), are generated for the calibration and validation stages. Results reveal that the regression approach (i.e., LN, QP, and PW) has more influence on the simulated dataset than on the measured one. The algorithms that incorporated linear regression provide the highest retrieval accuracy for the simulated dataset. Results from simulated datasets reveal that the 3-band (3b) algorithm that incorporate 665-nm and 680-nm bands and band tuning selection approach outperformed other algorithms with root mean square error (RMSE) of 15.87 mg·m−3, 16.25 mg·m−3, and 19.05 mg·m−3, respectively. The spatial distribution of the best performing algorithms, for various combinations of chlorophyll-a (Chla) and non-algal particles (NAP) concentrations, show that the 3b_tuning_QP and 3b_680_QP outperform other algorithms in terms of minimum RMSE frequency of 33.19% and 60.52%, respectively. However, the two algorithms failed to accurately retrieve Chla for many combinations of Chla and NAP, particularly for low Chla and NAP concentrations. In addition, the spatial distribution emphasizes that no single algorithm can provide outstanding accuracy for Chla retrieval and that multi-algorithms should be included to reduce the error. Comparing the results of the measured and simulated datasets reveal that the algorithms that incorporate the 665-nm band outperform other algorithms for measured dataset (RMSE = 36.84 mg·m−3), while algorithms that incorporate the band tuning approach provide the highest retrieval accuracy for the simulated dataset (RMSE = 25.05 mg·m−3). PMID:28758984

Engineering non-linear resonator mode interactions in circuit QED by continuous driving: Introduction

NASA Astrophysics Data System (ADS)

Pfaff, Wolfgang; Reagor, Matthew; Heeres, Reinier; Ofek, Nissim; Chou, Kevin; Blumoff, Jacob; Leghtas, Zaki; Touzard, Steven; Sliwa, Katrina; Holland, Eric; Krastanov, Stefan; Frunzio, Luigi; Devoret, Michel; Jiang, Liang; Schoelkopf, Robert

2015-03-01

High-Q microwave resonators show great promise for storing and manipulating quantum states in circuit QED. Using resonator modes as such a resource in quantum information processing applications requires the ability to manipulate the state of a resonator efficiently. Further, one must engineer appropriate coupling channels without spoiling the coherence properties of the resonator. We present an architecture that combines millisecond lifetimes for photonic quantum states stored in a linear resonator with fast measurement provided by a low-Q readout resonator. We demonstrate experimentally how a continuous drive on a transmon can be utilized to generate highly non-classical photonic states inside the high-Q resonator via effective nonlinear resonator mode interactions. Our approach opens new avenues for using modes of long-lived linear resonators in the circuit QED platform for quantum information processing tasks.

Quantum Fisher Information as a function response to a weak external perturbation

NASA Astrophysics Data System (ADS)

Rojas, Fernando; Maytorena, Jesus A.

The quantum fisher information (QFI) is known as a good indicator of entanglement in a multipartite systems. In this work we show that it can be treated as an induced response to an external field, in the same spirit of the usual linear response theory, with respect to a linear combination of observables of each subsystem. We derive an expression for a corresponding linear dynamical susceptibilitywhich contains relevant information about entanglement properties of a multipartite system. This approach is applied to investigate the hybrid entanglement in the driven Jaynes-Cummings model. The Fisher susceptibility response function is obtained and allows us to characterize the changes on quantum correlations between the qubit and photon states, in terms of the driving frequency, atom-field coupling, and temperature. We acknowledge financial support from DGAPA PAPPIT IN105717.

Optimal Combinations of Diagnostic Tests Based on AUC.

PubMed

Huang, Xin; Qin, Gengsheng; Fang, Yixin

2011-06-01

When several diagnostic tests are available, one can combine them to achieve better diagnostic accuracy. This article considers the optimal linear combination that maximizes the area under the receiver operating characteristic curve (AUC); the estimates of the combination's coefficients can be obtained via a nonparametric procedure. However, for estimating the AUC associated with the estimated coefficients, the apparent estimation by re-substitution is too optimistic. To adjust for the upward bias, several methods are proposed. Among them the cross-validation approach is especially advocated, and an approximated cross-validation is developed to reduce the computational cost. Furthermore, these proposed methods can be applied for variable selection to select important diagnostic tests. The proposed methods are examined through simulation studies and applications to three real examples. © 2010, The International Biometric Society.

Generalized concentration addition: a method for examining mixtures containing partial agonists.

PubMed

Howard, Gregory J; Webster, Thomas F

2009-08-07

Environmentally relevant toxic exposures often consist of simultaneous exposure to multiple agents. Methods to predict the expected outcome of such combinations are critical both to risk assessment and to an accurate judgment of whether combinations are synergistic or antagonistic. Concentration addition (CA) has commonly been used to assess the presence of synergy or antagonism in combinations of similarly acting chemicals, and to predict effects of combinations of such agents. CA has the advantage of clear graphical interpretation: Curves of constant joint effect (isoboles) must be negatively sloped straight lines if the mixture is concentration additive. However, CA cannot be directly used to assess combinations that include partial agonists, although such agents are of considerable interest. Here, we propose a natural extension of CA to a functional form that may be applied to mixtures including full agonists and partial agonists. This extended definition, for which we suggest the term "generalized concentration addition," encompasses linear isoboles with slopes of any sign. We apply this approach to the simple example of agents with dose-response relationships described by Hill functions with slope parameter n=1. The resulting isoboles are in all cases linear, with negative, zero and positive slopes. Using simple mechanistic models of ligand-receptor systems, we show that the same isobole pattern and joint effects are generated by modeled combinations of full and partial agonists. Special cases include combinations of two full agonists and a full agonist plus a competitive antagonist.

Reduced-Drift Virtual Gyro from an Array of Low-Cost Gyros.

PubMed

Vaccaro, Richard J; Zaki, Ahmed S

2017-02-11

A Kalman filter approach for combining the outputs of an array of high-drift gyros to obtain a virtual lower-drift gyro has been known in the literature for more than a decade. The success of this approach depends on the correlations of the random drift components of the individual gyros. However, no method of estimating these correlations has appeared in the literature. This paper presents an algorithm for obtaining the statistical model for an array of gyros, including the cross-correlations of the individual random drift components. In order to obtain this model, a new statistic, called the "Allan covariance" between two gyros, is introduced. The gyro array model can be used to obtain the Kalman filter-based (KFB) virtual gyro. Instead, we consider a virtual gyro obtained by taking a linear combination of individual gyro outputs. The gyro array model is used to calculate the optimal coefficients, as well as to derive a formula for the drift of the resulting virtual gyro. The drift formula for the optimal linear combination (OLC) virtual gyro is identical to that previously derived for the KFB virtual gyro. Thus, a Kalman filter is not necessary to obtain a minimum drift virtual gyro. The theoretical results of this paper are demonstrated using simulated as well as experimental data. In experimental results with a 28-gyro array, the OLC virtual gyro has a drift spectral density 40 times smaller than that obtained by taking the average of the gyro signals.

The receiver operational characteristic for binary classification with multiple indices and its application to the neuroimaging study of Alzheimer's disease.

PubMed

Wu, Xia; Li, Juan; Ayutyanont, Napatkamon; Protas, Hillary; Jagust, William; Fleisher, Adam; Reiman, Eric; Yao, Li; Chen, Kewei

2013-01-01

Given a single index, the receiver operational characteristic (ROC) curve analysis is routinely utilized for characterizing performances in distinguishing two conditions/groups in terms of sensitivity and specificity. Given the availability of multiple data sources (referred to as multi-indices), such as multimodal neuroimaging data sets, cognitive tests, and clinical ratings and genomic data in Alzheimer’s disease (AD) studies, the single-index-based ROC underutilizes all available information. For a long time, a number of algorithmic/analytic approaches combining multiple indices have been widely used to simultaneously incorporate multiple sources. In this study, we propose an alternative for combining multiple indices using logical operations, such as “AND,” “OR,” and “at least n” (where n is an integer), to construct multivariate ROC (multiV-ROC) and characterize the sensitivity and specificity statistically associated with the use of multiple indices. With and without the “leave-one-out” cross-validation, we used two data sets from AD studies to showcase the potentially increased sensitivity/specificity of the multiV-ROC in comparison to the single-index ROC and linear discriminant analysis (an analytic way of combining multi-indices). We conclude that, for the data sets we investigated, the proposed multiV-ROC approach is capable of providing a natural and practical alternative with improved classification accuracy as compared to univariate ROC and linear discriminant analysis.

The Receiver Operational Characteristic for Binary Classification with Multiple Indices and Its Application to the Neuroimaging Study of Alzheimer’s Disease

PubMed Central

Wu, Xia; Li, Juan; Ayutyanont, Napatkamon; Protas, Hillary; Jagust, William; Fleisher, Adam; Reiman, Eric; Yao, Li; Chen, Kewei

2014-01-01

Given a single index, the receiver operational characteristic (ROC) curve analysis is routinely utilized for characterizing performances in distinguishing two conditions/groups in terms of sensitivity and specificity. Given the availability of multiple data sources (referred to as multi-indices), such as multimodal neuroimaging data sets, cognitive tests, and clinical ratings and genomic data in Alzheimer’s disease (AD) studies, the single-index-based ROC underutilizes all available information. For a long time, a number of algorithmic/analytic approaches combining multiple indices have been widely used to simultaneously incorporate multiple sources. In this study, we propose an alternative for combining multiple indices using logical operations, such as “AND,” “OR,” and “at least n” (where n is an integer), to construct multivariate ROC (multiV-ROC) and characterize the sensitivity and specificity statistically associated with the use of multiple indices. With and without the “leave-one-out” cross-validation, we used two data sets from AD studies to showcase the potentially increased sensitivity/specificity of the multiV-ROC in comparison to the single-index ROC and linear discriminant analysis (an analytic way of combining multi-indices). We conclude that, for the data sets we investigated, the proposed multiV-ROC approach is capable of providing a natural and practical alternative with improved classification accuracy as compared to univariate ROC and linear discriminant analysis. PMID:23702553

Computational intelligence approaches for pattern discovery in biological systems.

PubMed

Fogel, Gary B

2008-07-01

Biology, chemistry and medicine are faced by tremendous challenges caused by an overwhelming amount of data and the need for rapid interpretation. Computational intelligence (CI) approaches such as artificial neural networks, fuzzy systems and evolutionary computation are being used with increasing frequency to contend with this problem, in light of noise, non-linearity and temporal dynamics in the data. Such methods can be used to develop robust models of processes either on their own or in combination with standard statistical approaches. This is especially true for database mining, where modeling is a key component of scientific understanding. This review provides an introduction to current CI methods, their application to biological problems, and concludes with a commentary about the anticipated impact of these approaches in bioinformatics.

CAMELOT: A machine learning approach for coarse-grained simulations of aggregation of block-copolymeric protein sequences

PubMed Central

Ruff, Kiersten M.; Harmon, Tyler S.; Pappu, Rohit V.

2015-01-01

We report the development and deployment of a coarse-graining method that is well suited for computer simulations of aggregation and phase separation of protein sequences with block-copolymeric architectures. Our algorithm, named CAMELOT for Coarse-grained simulations Aided by MachinE Learning Optimization and Training, leverages information from converged all atom simulations that is used to determine a suitable resolution and parameterize the coarse-grained model. To parameterize a system-specific coarse-grained model, we use a combination of Boltzmann inversion, non-linear regression, and a Gaussian process Bayesian optimization approach. The accuracy of the coarse-grained model is demonstrated through direct comparisons to results from all atom simulations. We demonstrate the utility of our coarse-graining approach using the block-copolymeric sequence from the exon 1 encoded sequence of the huntingtin protein. This sequence comprises of 17 residues from the N-terminal end of huntingtin (N17) followed by a polyglutamine (polyQ) tract. Simulations based on the CAMELOT approach are used to show that the adsorption and unfolding of the wild type N17 and its sequence variants on the surface of polyQ tracts engender a patchy colloid like architecture that promotes the formation of linear aggregates. These results provide a plausible explanation for experimental observations, which show that N17 accelerates the formation of linear aggregates in block-copolymeric N17-polyQ sequences. The CAMELOT approach is versatile and is generalizable for simulating the aggregation and phase behavior of a range of block-copolymeric protein sequences. PMID:26723608

A density matrix-based method for the linear-scaling calculation of dynamic second- and third-order properties at the Hartree-Fock and Kohn-Sham density functional theory levels.

PubMed

Kussmann, Jörg; Ochsenfeld, Christian

2007-11-28

A density matrix-based time-dependent self-consistent field (D-TDSCF) method for the calculation of dynamic polarizabilities and first hyperpolarizabilities using the Hartree-Fock and Kohn-Sham density functional theory approaches is presented. The D-TDSCF method allows us to reduce the asymptotic scaling behavior of the computational effort from cubic to linear for systems with a nonvanishing band gap. The linear scaling is achieved by combining a density matrix-based reformulation of the TDSCF equations with linear-scaling schemes for the formation of Fock- or Kohn-Sham-type matrices. In our reformulation only potentially linear-scaling matrices enter the formulation and efficient sparse algebra routines can be employed. Furthermore, the corresponding formulas for the first hyperpolarizabilities are given in terms of zeroth- and first-order one-particle reduced density matrices according to Wigner's (2n+1) rule. The scaling behavior of our method is illustrated for first exemplary calculations with systems of up to 1011 atoms and 8899 basis functions.

Application and flight test of linearizing transformations using measurement feedback to the nonlinear control problem

NASA Technical Reports Server (NTRS)

Antoniewicz, Robert F.; Duke, Eugene L.; Menon, P. K. A.

1991-01-01

The design of nonlinear controllers has relied on the use of detailed aerodynamic and engine models that must be associated with the control law in the flight system implementation. Many of these controllers were applied to vehicle flight path control problems and have attempted to combine both inner- and outer-loop control functions in a single controller. An approach to the nonlinear trajectory control problem is presented. This approach uses linearizing transformations with measurement feedback to eliminate the need for detailed aircraft models in outer-loop control applications. By applying this approach and separating the inner-loop and outer-loop functions two things were achieved: (1) the need for incorporating detailed aerodynamic models in the controller is obviated; and (2) the controller is more easily incorporated into existing aircraft flight control systems. An implementation of the controller is discussed, and this controller is tested on a six degree-of-freedom F-15 simulation and in flight on an F-15 aircraft. Simulation data are presented which validates this approach over a large portion of the F-15 flight envelope. Proof of this concept is provided by flight-test data that closely matches simulation results. Flight-test data are also presented.

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

NASA Astrophysics Data System (ADS)

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

2005-01-01

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

Performance analysis of a GPS equipment by general linear models approach

NASA Astrophysics Data System (ADS)

Teodoro, M. Filomena; Gonçalves, Fernando M.; Correia, Anacleto

2017-06-01

One of the major challenges in processing high-accurate long baselines is the presence of un-modelled ionospheric and tropospheric delays. There are effective mitigation strategies for ionospheric biases, such as the ionosphere-free linear combination of L1 and L2 carrier-phase, which can remove about 98% of the first-order ionospheric biases. With few exceptions this was the solution found by LGO for the 11760 baselines processed in this research. Therefore, for successful results, the appropriated approach to the mitigation of biases due to tropospheric delays is vital. The main aim of the investigations presented in this work was to evaluate the improvements, or not, of the rate of baselines successfully produced by adopting an advanced tropospheric bias mitigation strategy as opposed to a sample tropospheric bias mitigation approach. In both cases LGO uses as a priori tropospheric model the simplified Hopfield model, improved in the first case with a zenith tropospheric scale factor per station. Being aware that 1D and 2D present different behaviors, both cases are analyzed individually with each strategy.

A Force Balanced Fragmentation Method for ab Initio Molecular Dynamic Simulation of Protein.

PubMed

Xu, Mingyuan; Zhu, Tong; Zhang, John Z H

2018-01-01

A force balanced generalized molecular fractionation with conjugate caps (FB-GMFCC) method is proposed for ab initio molecular dynamic simulation of proteins. In this approach, the energy of the protein is computed by a linear combination of the QM energies of individual residues and molecular fragments that account for the two-body interaction of hydrogen bond between backbone peptides. The atomic forces on the caped H atoms were corrected to conserve the total force of the protein. Using this approach, ab initio molecular dynamic simulation of an Ace-(ALA) 9 -NME linear peptide showed the conservation of the total energy of the system throughout the simulation. Further a more robust 110 ps ab initio molecular dynamic simulation was performed for a protein with 56 residues and 862 atoms in explicit water. Compared with the classical force field, the ab initio molecular dynamic simulations gave better description of the geometry of peptide bonds. Although further development is still needed, the current approach is highly efficient, trivially parallel, and can be applied to ab initio molecular dynamic simulation study of large proteins.

Low-sensitivity H ∞ filter design for linear delta operator systems with sampling time jitter

NASA Astrophysics Data System (ADS)

Guo, Xiang-Gui; Yang, Guang-Hong

2012-04-01

This article is concerned with the problem of designing H ∞ filters for a class of linear discrete-time systems with low-sensitivity to sampling time jitter via delta operator approach. Delta-domain model is used to avoid the inherent numerical ill-condition resulting from the use of the standard shift-domain model at high sampling rates. Based on projection lemma in combination with the descriptor system approach often used to solve problems related to delay, a novel bounded real lemma with three slack variables for delta operator systems is presented. A sensitivity approach based on this novel lemma is proposed to mitigate the effects of sampling time jitter on system performance. Then, the problem of designing a low-sensitivity filter can be reduced to a convex optimisation problem. An important consideration in the design of correlation filters is the optimal trade-off between the standard H ∞ criterion and the sensitivity of the transfer function with respect to sampling time jitter. Finally, a numerical example demonstrating the validity of the proposed design method is given.

Conceptual design optimization study

NASA Technical Reports Server (NTRS)

Hollowell, S. J.; Beeman, E. R., II; Hiyama, R. M.

1990-01-01

The feasibility of applying multilevel functional decomposition and optimization techniques to conceptual design of advanced fighter aircraft was investigated. Applying the functional decomposition techniques to the conceptual design phase appears to be feasible. The initial implementation of the modified design process will optimize wing design variables. A hybrid approach, combining functional decomposition techniques for generation of aerodynamic and mass properties linear sensitivity derivatives with existing techniques for sizing mission performance and optimization, is proposed.

A data-driven approach for evaluating multi-modal therapy in traumatic brain injury

PubMed Central

Haefeli, Jenny; Ferguson, Adam R.; Bingham, Deborah; Orr, Adrienne; Won, Seok Joon; Lam, Tina I.; Shi, Jian; Hawley, Sarah; Liu, Jialing; Swanson, Raymond A.; Massa, Stephen M.

2017-01-01

Combination therapies targeting multiple recovery mechanisms have the potential for additive or synergistic effects, but experimental design and analyses of multimodal therapeutic trials are challenging. To address this problem, we developed a data-driven approach to integrate and analyze raw source data from separate pre-clinical studies and evaluated interactions between four treatments following traumatic brain injury. Histologic and behavioral outcomes were measured in 202 rats treated with combinations of an anti-inflammatory agent (minocycline), a neurotrophic agent (LM11A-31), and physical therapy consisting of assisted exercise with or without botulinum toxin-induced limb constraint. Data was curated and analyzed in a linked workflow involving non-linear principal component analysis followed by hypothesis testing with a linear mixed model. Results revealed significant benefits of the neurotrophic agent LM11A-31 on learning and memory outcomes after traumatic brain injury. In addition, modulations of LM11A-31 effects by co-administration of minocycline and by the type of physical therapy applied reached statistical significance. These results suggest a combinatorial effect of drug and physical therapy interventions that was not evident by univariate analysis. The study designs and analytic techniques applied here form a structured, unbiased, internally validated workflow that may be applied to other combinatorial studies, both in animals and humans. PMID:28205533

A data-driven approach for evaluating multi-modal therapy in traumatic brain injury.

PubMed

Haefeli, Jenny; Ferguson, Adam R; Bingham, Deborah; Orr, Adrienne; Won, Seok Joon; Lam, Tina I; Shi, Jian; Hawley, Sarah; Liu, Jialing; Swanson, Raymond A; Massa, Stephen M

2017-02-16

Combination therapies targeting multiple recovery mechanisms have the potential for additive or synergistic effects, but experimental design and analyses of multimodal therapeutic trials are challenging. To address this problem, we developed a data-driven approach to integrate and analyze raw source data from separate pre-clinical studies and evaluated interactions between four treatments following traumatic brain injury. Histologic and behavioral outcomes were measured in 202 rats treated with combinations of an anti-inflammatory agent (minocycline), a neurotrophic agent (LM11A-31), and physical therapy consisting of assisted exercise with or without botulinum toxin-induced limb constraint. Data was curated and analyzed in a linked workflow involving non-linear principal component analysis followed by hypothesis testing with a linear mixed model. Results revealed significant benefits of the neurotrophic agent LM11A-31 on learning and memory outcomes after traumatic brain injury. In addition, modulations of LM11A-31 effects by co-administration of minocycline and by the type of physical therapy applied reached statistical significance. These results suggest a combinatorial effect of drug and physical therapy interventions that was not evident by univariate analysis. The study designs and analytic techniques applied here form a structured, unbiased, internally validated workflow that may be applied to other combinatorial studies, both in animals and humans.

Historical HIV incidence modelling in regional subgroups: use of flexible discrete models with penalized splines based on prior curves.

PubMed

Greenland, S

1996-03-15

This paper presents an approach to back-projection (back-calculation) of human immunodeficiency virus (HIV) person-year infection rates in regional subgroups based on combining a log-linear model for subgroup differences with a penalized spline model for trends. The penalized spline approach allows flexible trend estimation but requires far fewer parameters than fully non-parametric smoothers, thus saving parameters that can be used in estimating subgroup effects. Use of reasonable prior curve to construct the penalty function minimizes the degree of smoothing needed beyond model specification. The approach is illustrated in application to acquired immunodeficiency syndrome (AIDS) surveillance data from Los Angeles County.

MR Imaging-Guided Attenuation Correction of PET Data in PET/MR Imaging.

PubMed

Izquierdo-Garcia, David; Catana, Ciprian

2016-04-01

Attenuation correction (AC) is one of the most important challenges in the recently introduced combined PET/magnetic resonance (MR) scanners. PET/MR AC (MR-AC) approaches aim to develop methods that allow accurate estimation of the linear attenuation coefficients of the tissues and other components located in the PET field of view. MR-AC methods can be divided into 3 categories: segmentation, atlas, and PET based. This review provides a comprehensive list of the state-of-the-art MR-AC approaches and their pros and cons. The main sources of artifacts are presented. Finally, this review discusses the current status of MR-AC approaches for clinical applications. Copyright © 2016 Elsevier Inc. All rights reserved.

Stable and low diffusive hybrid upwind splitting methods

NASA Technical Reports Server (NTRS)

Coquel, Frederic; Liou, Meng-Sing

1992-01-01

We introduce in this paper a new concept for upwinding: the Hybrid Upwind Splitting (HUS). This original strategy for upwinding is achieved by combining the two previous existing approaches, the Flux Vector (FVS) and Flux Difference Splittings (FDS), while retaining their own interesting features. Indeed, our approach yields upwind methods that share the robustness of FVS schemes in the capture of nonlinear waves and the accuracy of some FDS schemes in the capture of linear waves. We describe here some examples of such HUS methods obtained by hybridizing the Osher approach with FVS schemes. Numerical illustrations are displayed and will prove in particular the relevance of the HUS methods we propose for viscous calculations.

Study of self-focusing of Non Gaussian laser beam in a plasma with density variation using moment theory approach

NASA Astrophysics Data System (ADS)

Pathak, Nidhi; Kaur, Sukhdeep; Singh, Sukhmander

2018-05-01

In this paper, self-focusing/defocusing effects have been studied by taking into account the combined effect of ponder-motive and relativistic non linearity during the laser plasma interaction with density variation. The formulation is based on the numerical analysis of second order nonlinear differential equation for appropriate set of laser and plasma parameters by employing moment theory approach. We found that self-focusing increases with increasing the laser intensity and density variation. The results obtained are valuable in high harmonic generation, inertial confinement fusion and charge particle acceleration.

An implementation of the look-ahead Lanczos algorithm for non-Hermitian matrices, part 2

NASA Technical Reports Server (NTRS)

Freund, Roland W.; Nachtigal, Noel M.

1990-01-01

It is shown how the look-ahead Lanczos process (combined with a quasi-minimal residual QMR) approach) can be used to develop a robust black box solver for large sparse non-Hermitian linear systems. Details of an implementation of the resulting QMR algorithm are presented. It is demonstrated that the QMR method is closely related to the biconjugate gradient (BCG) algorithm; however, unlike BCG, the QMR algorithm has smooth convergence curves and good numerical properties. We report numerical experiments with our implementation of the look-ahead Lanczos algorithm, both for eigenvalue problem and linear systems. Also, program listings of FORTRAN implementations of the look-ahead algorithm and the QMR method are included.

Wavelet packets for multi- and hyper-spectral imagery

NASA Astrophysics Data System (ADS)

Benedetto, J. J.; Czaja, W.; Ehler, M.; Flake, C.; Hirn, M.

2010-01-01

State of the art dimension reduction and classification schemes in multi- and hyper-spectral imaging rely primarily on the information contained in the spectral component. To better capture the joint spatial and spectral data distribution we combine the Wavelet Packet Transform with the linear dimension reduction method of Principal Component Analysis. Each spectral band is decomposed by means of the Wavelet Packet Transform and we consider a joint entropy across all the spectral bands as a tool to exploit the spatial information. Dimension reduction is then applied to the Wavelet Packets coefficients. We present examples of this technique for hyper-spectral satellite imaging. We also investigate the role of various shrinkage techniques to model non-linearity in our approach.

Desired Accuracy Estimation of Noise Function from ECG Signal by Fuzzy Approach

PubMed Central

Vahabi, Zahra; Kermani, Saeed

2012-01-01

Unknown noise and artifacts present in medical signals with non-linear fuzzy filter will be estimated and then removed. An adaptive neuro-fuzzy interference system which has a non-linear structure presented for the noise function prediction by before Samples. This paper is about a neuro-fuzzy method to estimate unknown noise of Electrocardiogram signal. Adaptive neural combined with Fuzzy System to construct a fuzzy Predictor. For this system setting parameters such as the number of Membership Functions for each input and output, training epochs, type of MFs for each input and output, learning algorithm and etc. is determined by learning data. At the end simulated experimental results are presented for proper validation. PMID:23717810

Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification.

PubMed

Spinnato, J; Roubaud, M-C; Burle, B; Torrésani, B

2015-06-01

The main goal of this work is to develop a model for multisensor signals, such as magnetoencephalography or electroencephalography (EEG) signals that account for inter-trial variability, suitable for corresponding binary classification problems. An important constraint is that the model be simple enough to handle small size and unbalanced datasets, as often encountered in BCI-type experiments. The method involves the linear mixed effects statistical model, wavelet transform, and spatial filtering, and aims at the characterization of localized discriminant features in multisensor signals. After discrete wavelet transform and spatial filtering, a projection onto the relevant wavelet and spatial channels subspaces is used for dimension reduction. The projected signals are then decomposed as the sum of a signal of interest (i.e., discriminant) and background noise, using a very simple Gaussian linear mixed model. Thanks to the simplicity of the model, the corresponding parameter estimation problem is simplified. Robust estimates of class-covariance matrices are obtained from small sample sizes and an effective Bayes plug-in classifier is derived. The approach is applied to the detection of error potentials in multichannel EEG data in a very unbalanced situation (detection of rare events). Classification results prove the relevance of the proposed approach in such a context. The combination of the linear mixed model, wavelet transform and spatial filtering for EEG classification is, to the best of our knowledge, an original approach, which is proven to be effective. This paper improves upon earlier results on similar problems, and the three main ingredients all play an important role.

Combining Biomarkers Linearly and Nonlinearly for Classification Using the Area Under the ROC Curve

PubMed Central

Fong, Youyi; Yin, Shuxin; Huang, Ying

2016-01-01

In biomedical studies, it is often of interest to classify/predict a subject’s disease status based on a variety of biomarker measurements. A commonly used classification criterion is based on AUC - Area under the Receiver Operating Characteristic Curve. Many methods have been proposed to optimize approximated empirical AUC criteria, but there are two limitations to the existing methods. First, most methods are only designed to find the best linear combination of biomarkers, which may not perform well when there is strong nonlinearity in the data. Second, many existing linear combination methods use gradient-based algorithms to find the best marker combination, which often result in sub-optimal local solutions. In this paper, we address these two problems by proposing a new kernel-based AUC optimization method called Ramp AUC (RAUC). This method approximates the empirical AUC loss function with a ramp function, and finds the best combination by a difference of convex functions algorithm. We show that as a linear combination method, RAUC leads to a consistent and asymptotically normal estimator of the linear marker combination when the data is generated from a semiparametric generalized linear model, just as the Smoothed AUC method (SAUC). Through simulation studies and real data examples, we demonstrate that RAUC out-performs SAUC in finding the best linear marker combinations, and can successfully capture nonlinear pattern in the data to achieve better classification performance. We illustrate our method with a dataset from a recent HIV vaccine trial. PMID:27058981

A Comparison of Two-Stage Approaches for Fitting Nonlinear Ordinary Differential Equation (ODE) Models with Mixed Effects

PubMed Central

Chow, Sy-Miin; Bendezú, Jason J.; Cole, Pamela M.; Ram, Nilam

2016-01-01

Several approaches currently exist for estimating the derivatives of observed data for model exploration purposes, including functional data analysis (FDA), generalized local linear approximation (GLLA), and generalized orthogonal local derivative approximation (GOLD). These derivative estimation procedures can be used in a two-stage process to fit mixed effects ordinary differential equation (ODE) models. While the performance and utility of these routines for estimating linear ODEs have been established, they have not yet been evaluated in the context of nonlinear ODEs with mixed effects. We compared properties of the GLLA and GOLD to an FDA-based two-stage approach denoted herein as functional ordinary differential equation with mixed effects (FODEmixed) in a Monte Carlo study using a nonlinear coupled oscillators model with mixed effects. Simulation results showed that overall, the FODEmixed outperformed both the GLLA and GOLD across all the embedding dimensions considered, but a novel use of a fourth-order GLLA approach combined with very high embedding dimensions yielded estimation results that almost paralleled those from the FODEmixed. We discuss the strengths and limitations of each approach and demonstrate how output from each stage of FODEmixed may be used to inform empirical modeling of young children’s self-regulation. PMID:27391255

A Comparison of Two-Stage Approaches for Fitting Nonlinear Ordinary Differential Equation Models with Mixed Effects.

PubMed

Chow, Sy-Miin; Bendezú, Jason J; Cole, Pamela M; Ram, Nilam

2016-01-01

Several approaches exist for estimating the derivatives of observed data for model exploration purposes, including functional data analysis (FDA; Ramsay & Silverman, 2005 ), generalized local linear approximation (GLLA; Boker, Deboeck, Edler, & Peel, 2010 ), and generalized orthogonal local derivative approximation (GOLD; Deboeck, 2010 ). These derivative estimation procedures can be used in a two-stage process to fit mixed effects ordinary differential equation (ODE) models. While the performance and utility of these routines for estimating linear ODEs have been established, they have not yet been evaluated in the context of nonlinear ODEs with mixed effects. We compared properties of the GLLA and GOLD to an FDA-based two-stage approach denoted herein as functional ordinary differential equation with mixed effects (FODEmixed) in a Monte Carlo (MC) study using a nonlinear coupled oscillators model with mixed effects. Simulation results showed that overall, the FODEmixed outperformed both the GLLA and GOLD across all the embedding dimensions considered, but a novel use of a fourth-order GLLA approach combined with very high embedding dimensions yielded estimation results that almost paralleled those from the FODEmixed. We discuss the strengths and limitations of each approach and demonstrate how output from each stage of FODEmixed may be used to inform empirical modeling of young children's self-regulation.

Increased statistical power with combined independent randomization tests used with multiple-baseline design.

PubMed

Tyrrell, Pascal N; Corey, Paul N; Feldman, Brian M; Silverman, Earl D

2013-06-01

Physicians often assess the effectiveness of treatments on a small number of patients. Multiple-baseline designs (MBDs), based on the Wampold-Worsham (WW) method of randomization and applied to four subjects, have relatively low power. Our objective was to propose another approach with greater power that does not suffer from the time requirements of the WW method applied to a greater number of subjects. The power of a design that involves the combination of two four-subject MBDs was estimated using computer simulation and compared with the four- and eight-subject designs. The effect of a delayed linear response to treatment on the power of the test was also investigated. Power was found to be adequate (>80%) for a standardized mean difference (SMD) greater than 0.8. The effect size associated with 80% power from combined tests was smaller than that of the single four-subject MBD (SMD=1.3) and comparable with the eight-subject MBD (SMD=0.6). A delayed linear response to the treatment resulted in important reductions in power (20-35%). By combining two four-subject MBD tests, an investigator can detect better effect sizes (SMD=0.8) and be able to complete a comparatively timelier and feasible study. Copyright © 2013 Elsevier Inc. All rights reserved.

Clinically Practical Approach for Screening of Low Muscularity Using Electronic Linear Measures on Computed Tomography Images in Critically Ill Patients.

PubMed

Avrutin, Egor; Moisey, Lesley L; Zhang, Roselyn; Khattab, Jenna; Todd, Emma; Premji, Tahira; Kozar, Rosemary; Heyland, Daren K; Mourtzakis, Marina

2017-12-06

Computed tomography (CT) scans performed during routine hospital care offer the opportunity to quantify skeletal muscle and predict mortality and morbidity in intensive care unit (ICU) patients. Existing methods of muscle cross-sectional area (CSA) quantification require specialized software, training, and time commitment that may not be feasible in a clinical setting. In this article, we explore a new screening method to identify patients with low muscle mass. We analyzed 145 scans of elderly ICU patients (≥65 years old) using a combination of measures obtained with a digital ruler, commonly found on hospital radiological software. The psoas and paraspinal muscle groups at the level of the third lumbar vertebra (L3) were evaluated by using 2 linear measures each and compared with an established method of CT image analysis of total muscle CSA in the L3 region. There was a strong association between linear measures of psoas and paraspinal muscle groups and total L3 muscle CSA (R 2 = 0.745, P < 0.001). Linear measures, age, and sex were included as covariates in a multiple logistic regression to predict those with low muscle mass; receiver operating characteristic (ROC) area under the curve (AUC) of the combined psoas and paraspinal linear index model was 0.920. Intraclass correlation coefficients (ICCs) were used to evaluate intrarater and interrater reliability, resulting in scores of 0.979 (95% CI: 0.940-0.992) and 0.937 (95% CI: 0.828-0.978), respectively. A digital ruler can reliably predict L3 muscle CSA, and these linear measures may be used to identify critically ill patients with low muscularity who are at risk for worse clinical outcomes. © 2017 American Society for Parenteral and Enteral Nutrition.

UV Spectrophotometric Simultaneous Determination of Paracetamol and Ibuprofen in Combined Tablets by Derivative and Wavelet Transforms

PubMed Central

Hoang, Vu Dang; Ly, Dong Thi Ha; Tho, Nguyen Huu; Minh Thi Nguyen, Hue

2014-01-01

The application of first-order derivative and wavelet transforms to UV spectra and ratio spectra was proposed for the simultaneous determination of ibuprofen and paracetamol in their combined tablets. A new hybrid approach on the combined use of first-order derivative and wavelet transforms to spectra was also discussed. In this application, DWT (sym6 and haar), CWT (mexh), and FWT were optimized to give the highest spectral recoveries. Calibration graphs in the linear concentration ranges of ibuprofen (12–32 mg/L) and paracetamol (20–40 mg/L) were obtained by measuring the amplitudes of the transformed signals. Our proposed spectrophotometric methods were statistically compared to HPLC in terms of precision and accuracy. PMID:24949492

UV spectrophotometric simultaneous determination of paracetamol and ibuprofen in combined tablets by derivative and wavelet transforms.

PubMed

Hoang, Vu Dang; Ly, Dong Thi Ha; Tho, Nguyen Huu; Nguyen, Hue Minh Thi

2014-01-01

The application of first-order derivative and wavelet transforms to UV spectra and ratio spectra was proposed for the simultaneous determination of ibuprofen and paracetamol in their combined tablets. A new hybrid approach on the combined use of first-order derivative and wavelet transforms to spectra was also discussed. In this application, DWT (sym6 and haar), CWT (mexh), and FWT were optimized to give the highest spectral recoveries. Calibration graphs in the linear concentration ranges of ibuprofen (12-32 mg/L) and paracetamol (20-40 mg/L) were obtained by measuring the amplitudes of the transformed signals. Our proposed spectrophotometric methods were statistically compared to HPLC in terms of precision and accuracy.

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

PubMed

Held, Elizabeth; Cape, Joshua; Tintle, Nathan

2016-01-01

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

Combined acute ecotoxicity of malathion and deltamethrin to Daphnia magna (Crustacea, Cladocera): comparison of different data analysis approaches.

PubMed

Toumi, Héla; Boumaiza, Moncef; Millet, Maurice; Radetski, Claudemir Marcos; Camara, Baba Issa; Felten, Vincent; Masfaraud, Jean-François; Férard, Jean-François

2018-04-19

We studied the combined acute effect (i.e., after 48 h) of deltamethrin (a pyrethroid insecticide) and malathion (an organophosphate insecticide) on Daphnia magna. Two approaches were used to examine the potential interaction effects of eight mixtures of deltamethrin and malathion: (i) calculation of mixture toxicity index (MTI) and safety factor index (SFI) and (ii) response surface methodology coupled with isobole-based statistical model (using generalized linear model). According to the calculation of MTI and SFI, one tested mixture was found additive while the two other tested mixtures were found no additive (MTI) or antagonistic (SFI), but these differences between index responses are only due to differences in terminology related to these two indexes. Through the surface response approach and isobologram analysis, we concluded that there was a significant antagonistic effect of the binary mixtures of deltamethrin and malathion that occurs on D. magna immobilization, after 48 h of exposure. Index approaches and surface response approach with isobologram analysis are complementary. Calculation of mixture toxicity index and safety factor index allows identifying punctually the type of interaction for several tested mixtures, while the surface response approach with isobologram analysis integrates all the data providing a global outcome about the type of interactive effect. Only the surface response approach and isobologram analysis allowed the statistical assessment of the ecotoxicological interaction. Nevertheless, we recommend the use of both approaches (i) to identify the combined effects of contaminants and (ii) to improve risk assessment and environmental management.

Autonomous Guidance of Agile Small-scale Rotorcraft

NASA Technical Reports Server (NTRS)

Mettler, Bernard; Feron, Eric

2004-01-01

This report describes a guidance system for agile vehicles based on a hybrid closed-loop model of the vehicle dynamics. The hybrid model represents the vehicle dynamics through a combination of linear-time-invariant control modes and pre-programmed, finite-duration maneuvers. This particular hybrid structure can be realized through a control system that combines trim controllers and a maneuvering control logic. The former enable precise trajectory tracking, and the latter enables trajectories at the edge of the vehicle capabilities. The closed-loop model is much simpler than the full vehicle equations of motion, yet it can capture a broad range of dynamic behaviors. It also supports a consistent link between the physical layer and the decision-making layer. The trajectory generation was formulated as an optimization problem using mixed-integer-linear-programming. The optimization is solved in a receding horizon fashion. Several techniques to improve the computational tractability were investigate. Simulation experiments using NASA Ames 'R-50 model show that this approach fully exploits the vehicle's agility.

Comparison of different modelling approaches of drive train temperature for the purposes of wind turbine failure detection

NASA Astrophysics Data System (ADS)

Tautz-Weinert, J.; Watson, S. J.

2016-09-01

Effective condition monitoring techniques for wind turbines are needed to improve maintenance processes and reduce operational costs. Normal behaviour modelling of temperatures with information from other sensors can help to detect wear processes in drive trains. In a case study, modelling of bearing and generator temperatures is investigated with operational data from the SCADA systems of more than 100 turbines. The focus is here on automated training and testing on a farm level to enable an on-line system, which will detect failures without human interpretation. Modelling based on linear combinations, artificial neural networks, adaptive neuro-fuzzy inference systems, support vector machines and Gaussian process regression is compared. The selection of suitable modelling inputs is discussed with cross-correlation analyses and a sensitivity study, which reveals that the investigated modelling techniques react in different ways to an increased number of inputs. The case study highlights advantages of modelling with linear combinations and artificial neural networks in a feedforward configuration.

Efficient Homodifunctional Bimolecular Ring-Closure Method for Cyclic Polymers by Combining RAFT and Self-Accelerating Click Reaction.

PubMed

Qu, Lin; Sun, Peng; Wu, Ying; Zhang, Ke; Liu, Zhengping

2017-08-01

An efficient metal-free homodifunctional bimolecular ring-closure method is developed for the formation of cyclic polymers by combining reversible addition-fragmentation chain transfer (RAFT) polymerization and self-accelerating click reaction. In this approach, α,ω-homodifunctional linear polymers with azide terminals are prepared by RAFT polymerization and postmodification of polymer chain end groups. By virtue of sym-dibenzo-1,5-cyclooctadiene-3,7-diyne (DBA) as small linkers, well-defined cyclic polymers are then prepared using the self-accelerating double strain-promoted azide-alkyne click (DSPAAC) reaction to ring-close the azide end-functionalized homodifunctional linear polymer precursors. Due to the self-accelerating property of DSPAAC ring-closing reaction, this novel method eliminates the requirement of equimolar amounts of telechelic polymers and small linkers in traditional bimolecular ring-closure methods. It facilitates this method to efficiently and conveniently produce varied pure cyclic polymers by employing an excess molar amount of DBA small linkers. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

J functions for the process ud→WA

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

Bardin, D. Yu., E-mail: bardin@nu.jinr.ru; Kalinovskaya, L. V., E-mail: kalinov@mail.cern.ch; Uglov, E. D., E-mail: e.uglov@gmail.com

In this paper we present a description of the universal approach for analytic calculations for a certain class of J functions for six topologies of the boxes for the process ud → WA. These functions J arise at the reduction of the infrared divergent box diagrams. The standard Passarino–Veltman reduction of the four-point box diagram with an internal photon line connecting two external lines on the mass shell leads to infrared-divergent and mass-singular D{sub 0} functions. In the system SANC a systematic procedure is adopted to separate both types of singularities into the simplest objects, namely C{sub 0} functions. Themore » functions J, in turn, are represented as certain linear combinations of the standard D{sub 0} and C{sub 0} functions. The subtracted J functions are free of both types of singularities and are expressed as explicit and compact linear combinations of dilogarithm functions. We present extensive comparisons of numerical results of SANC with those obtained with the aid of the LoopTools package.« less

Deformation mechanisms in a coal mine roadway in extremely swelling soft rock.

PubMed

Li, Qinghai; Shi, Weiping; Yang, Renshu

2016-01-01

The problem of roadway support in swelling soft rock was one of the challenging problems during mining. For most geological conditions, combinations of two or more supporting approaches could meet the requirements of most roadways; however, in extremely swelling soft rock, combined approaches even could not control large deformations. The purpose of this work was to probe the roadway deformation mechanisms in extremely swelling soft rock. Based on the main return air-way in a coal mine, deformation monitoring and geomechanical analysis were conducted, as well as plastic zone mechanical model was analysed. Results indicated that this soft rock was potentially very swelling. When the ground stress acted alone, the support strength needed in situ was not too large and combined supporting approaches could meet this requirement; however, when this potential released, the roadway would undergo permanent deformation. When the loose zone reached 3 m within surrounding rock, remote stress p ∞ and supporting stress P presented a linear relationship. Namely, the greater the swelling stress, the more difficult it would be in roadway supporting. So in this extremely swelling soft rock, a better way to control roadway deformation was to control the releasing of surrounding rock's swelling potential.

Isotropic-resolution linear-array-based photoacoustic computed tomography through inverse Radon transform

NASA Astrophysics Data System (ADS)

Li, Guo; Xia, Jun; Li, Lei; Wang, Lidai; Wang, Lihong V.

2015-03-01

Linear transducer arrays are readily available for ultrasonic detection in photoacoustic computed tomography. They offer low cost, hand-held convenience, and conventional ultrasonic imaging. However, the elevational resolution of linear transducer arrays, which is usually determined by the weak focus of the cylindrical acoustic lens, is about one order of magnitude worse than the in-plane axial and lateral spatial resolutions. Therefore, conventional linear scanning along the elevational direction cannot provide high-quality three-dimensional photoacoustic images due to the anisotropic spatial resolutions. Here we propose an innovative method to achieve isotropic resolutions for three-dimensional photoacoustic images through combined linear and rotational scanning. In each scan step, we first elevationally scan the linear transducer array, and then rotate the linear transducer array along its center in small steps, and scan again until 180 degrees have been covered. To reconstruct isotropic three-dimensional images from the multiple-directional scanning dataset, we use the standard inverse Radon transform originating from X-ray CT. We acquired a three-dimensional microsphere phantom image through the inverse Radon transform method and compared it with a single-elevational-scan three-dimensional image. The comparison shows that our method improves the elevational resolution by up to one order of magnitude, approaching the in-plane lateral-direction resolution. In vivo rat images were also acquired.

Imparting Motion to a Test Object Such as a Motor Vehicle in a Controlled Fashion

NASA Technical Reports Server (NTRS)

Southward, Stephen C. (Inventor); Reubush, Chandler (Inventor); Pittman, Bryan (Inventor); Roehrig, Kurt (Inventor); Gerard, Doug (Inventor)

2014-01-01

An apparatus imparts motion to a test object such as a motor vehicle in a controlled fashion. A base has mounted on it a linear electromagnetic motor having a first end and a second end, the first end being connected to the base. A pneumatic cylinder and piston combination have a first end and a second end, the first end connected to the base so that the pneumatic cylinder and piston combination is generally parallel with the linear electromagnetic motor. The second ends of the linear electromagnetic motor and pneumatic cylinder and piston combination being commonly linked to a mount for the test object. A control system for the linear electromagnetic motor and pneumatic cylinder and piston combination drives the pneumatic cylinder and piston combination to support a substantial static load of the test object and the linear electromagnetic motor to impart controlled motion to the test object.

Prediction of monthly rainfall in Victoria, Australia: Clusterwise linear regression approach

NASA Astrophysics Data System (ADS)

Bagirov, Adil M.; Mahmood, Arshad; Barton, Andrew

2017-05-01

This paper develops the Clusterwise Linear Regression (CLR) technique for prediction of monthly rainfall. The CLR is a combination of clustering and regression techniques. It is formulated as an optimization problem and an incremental algorithm is designed to solve it. The algorithm is applied to predict monthly rainfall in Victoria, Australia using rainfall data with five input meteorological variables over the period of 1889-2014 from eight geographically diverse weather stations. The prediction performance of the CLR method is evaluated by comparing observed and predicted rainfall values using four measures of forecast accuracy. The proposed method is also compared with the CLR using the maximum likelihood framework by the expectation-maximization algorithm, multiple linear regression, artificial neural networks and the support vector machines for regression models using computational results. The results demonstrate that the proposed algorithm outperforms other methods in most locations.

Linear integrated optics in 3C silicon carbide.

PubMed

Martini, Francesco; Politi, Alberto

2017-05-15

The development of new photonic materials that combine diverse optical capabilities is needed to boost the integration of different quantum and classical components within the same chip. Amongst all candidates, the superior optical properties of cubic silicon carbide (3C SiC) could be merged with its crystalline point defects, enabling single photon generation, manipulation and light-matter interaction on a single device. The development of photonics devices in SiC has been limited by the presence of the silicon substrate, over which thin crystalline films are heteroepitaxially grown. By employing a novel approach in the material fabrication, we demonstrate grating couplers with coupling efficiency reaching -6 dB, sub-µm waveguides and high intrinsic quality factor (up to 24,000) ring resonators. These components are the basis for linear optical networks and essential for developing a wide range of photonics component for non-linear and quantum optics.

A powerful and flexible approach to the analysis of RNA sequence count data

PubMed Central

Zhou, Yi-Hui; Xia, Kai; Wright, Fred A.

2011-01-01

Motivation: A number of penalization and shrinkage approaches have been proposed for the analysis of microarray gene expression data. Similar techniques are now routinely applied to RNA sequence transcriptional count data, although the value of such shrinkage has not been conclusively established. If penalization is desired, the explicit modeling of mean–variance relationships provides a flexible testing regimen that ‘borrows’ information across genes, while easily incorporating design effects and additional covariates. Results: We describe BBSeq, which incorporates two approaches: (i) a simple beta-binomial generalized linear model, which has not been extensively tested for RNA-Seq data and (ii) an extension of an expression mean–variance modeling approach to RNA-Seq data, involving modeling of the overdispersion as a function of the mean. Our approaches are flexible, allowing for general handling of discrete experimental factors and continuous covariates. We report comparisons with other alternate methods to handle RNA-Seq data. Although penalized methods have advantages for very small sample sizes, the beta-binomial generalized linear model, combined with simple outlier detection and testing approaches, appears to have favorable characteristics in power and flexibility. Availability: An R package containing examples and sample datasets is available at http://www.bios.unc.edu/research/genomic_software/BBSeq Contact: yzhou@bios.unc.edu; fwright@bios.unc.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:21810900

Travel time tomography with local image regularization by sparsity constrained dictionary learning

NASA Astrophysics Data System (ADS)

Bianco, M.; Gerstoft, P.

2017-12-01

We propose a regularization approach for 2D seismic travel time tomography which models small rectangular groups of slowness pixels, within an overall or `global' slowness image, as sparse linear combinations of atoms from a dictionary. The groups of slowness pixels are referred to as patches and a dictionary corresponds to a collection of functions or `atoms' describing the slowness in each patch. These functions could for example be wavelets.The patch regularization is incorporated into the global slowness image. The global image models the broad features, while the local patch images incorporate prior information from the dictionary. Further, high resolution slowness within patches is permitted if the travel times from the global estimates support it. The proposed approach is formulated as an algorithm, which is repeated until convergence is achieved: 1) From travel times, find the global slowness image with a minimum energy constraint on the pixel variance relative to a reference. 2) Find the patch level solutions to fit the global estimate as a sparse linear combination of dictionary atoms.3) Update the reference as the weighted average of the patch level solutions.This approach relies on the redundancy of the patches in the seismic image. Redundancy means that the patches are repetitions of a finite number of patterns, which are described by the dictionary atoms. Redundancy in the earth's structure was demonstrated in previous works in seismics where dictionaries of wavelet functions regularized inversion. We further exploit redundancy of the patches by using dictionary learning algorithms, a form of unsupervised machine learning, to estimate optimal dictionaries from the data in parallel with the inversion. We demonstrate our approach on densely, but irregularly sampled synthetic seismic images.

Novel Integration Radial and Axial Magnetic Bearing

NASA Technical Reports Server (NTRS)

Blumenstock, Kenneth; Brown, Gary

2000-01-01

Typically, fully active magnetically suspended systems require one axial and two radial magnetic bearings. Combining radial and axial functions into a single device allows for more compact and elegant packaging. Furthermore, in the case of high-speed devices such as energy storage flywheels, it is beneficial to minimize shaft length to keep rotor mode frequencies as high as possible. Attempts have been made to combine radial and axial functionality, but with certain drawbacks. One approach requires magnetic control flux to flow through a bias magnet reducing control effectiveness, thus resulting in increased resistive losses. This approach also requires axial force producing magnetic flux to flow in a direction into the rotor laminate that is undesirable for minimizing eddy-current losses resulting in rotational losses. Another approach applies a conical rotor shape to what otherwise would be a radial heteropolar magnetic bearing configuration. However, positional non-linear effects are introduced with this scheme and the same windings are used for bias, radial, and axial control adding complexity to the controller and electronics. For this approach, the amount of axial capability must be limited. It would be desirable for an integrated radial and axial magnetic bearing to have the following characteristics; separate inputs for radial and axial control for electronics and control simplicity, all magnetic control fluxes should only flow through their respective air gaps and should not flow through any bias magnets for minimal resistive losses, be of a homopolar design to minimize rotational losses, position related non-linear effects should be minimized, and dependent upon the design parameters, be able to achieve any radial/axial force or power ratio as desired. The integrated radial and axial magnetic bearing described in this paper exhibits all these characteristics. Magnetic circuit design, design equations, and magnetic field modeling results will be presented.

Novel Integrated Radial and Axial Magnetic Bearing

NASA Technical Reports Server (NTRS)

Blumenstock, Kenneth A.; Brown, Gary L.; Powers, Edward I. (Technical Monitor)

2000-01-01

Typically, fully active magnetically suspended systems require one axial and two radial magnetic bearings. Combining radial and axial functions into a single device allows for more compact and elegant packaging. Furthermore, in the case of high-speed devices such as energy storage flywheels, it is beneficial to minimize shaft length to keep rotor mode frequencies as high as possible. Attempts have been made to combine radial and axial functionality, but with certain drawbacks. One approach requires magnetic control flux to flow through a bias magnet reducing control effectiveness, thus resulting in increased resistive losses. This approach also requires axial force producing magnetic flux to flow in a direction into the rotor laminate that is undesirable for minimizing eddy-current losses resulting in rotational losses. Another approach applies a conical rotor shape to what otherwise would be a radial heteropolar magnetic bearing configuration. However, positional non-linear effects are introduced with this scheme and the same windings are used for bias, radial, and axial control adding complexity to the controller and electronics. For this approach, the amount of axial capability must be limited. It would be desirable for an integrated radial and axial magnetic bearing to have the following characteristics, separate inputs for radial and axial control for electronics and control simplicity, all magnetic control fluxes should only flow through their respective air gaps and should not flow through any bias magnets for minimal resistive losses, be of a homopolar design to minimize rotational losses, position related non-linear effects should be minimized, and dependent upon the design parameters, be able to achieve any radial/axial force or power ratio as desired. The integrated radial and axial magnetic bearing described in this paper exhibits all these characteristics. Magnetic circuit design, design equations, and analysis results will be presented.

Group Theory and Crystal Field Theory: A Simple and Rigorous Derivation of the Spectroscopic Terms Generated by the t[subscript 2g][superscript 2] Electronic Configuration in a Strong Octahedral Field

ERIC Educational Resources Information Center

Morpurgo, Simone

2007-01-01

The principles of symmetry and group theory are applied to the zero-order wavefunctions associated with the strong-field t[subscript 2g][superscript 2] configuration and their symmetry-adapted linear combinations (SALC) associated with the generated energy terms are derived. This approach will enable students to better understand the use of…

Dynamical generation of noiseless quantum subsystems

PubMed

Viola; Knill; Lloyd

2000-10-16

We combine dynamical decoupling and universal control methods for open quantum systems with coding procedures. By exploiting a general algebraic approach, we show how appropriate encodings of quantum states result in obtaining universal control over dynamically generated noise-protected subsystems with limited control resources. In particular, we provide a constructive scheme based on two-body Hamiltonians for performing universal quantum computation over large noiseless spaces which can be engineered in the presence of arbitrary linear quantum noise.

A sequential linear optimization approach for controller design

NASA Technical Reports Server (NTRS)

Horta, L. G.; Juang, J.-N.; Junkins, J. L.

1985-01-01

A linear optimization approach with a simple real arithmetic algorithm is presented for reliable controller design and vibration suppression of flexible structures. Using first order sensitivity of the system eigenvalues with respect to the design parameters in conjunction with a continuation procedure, the method converts a nonlinear optimization problem into a maximization problem with linear inequality constraints. The method of linear programming is then applied to solve the converted linear optimization problem. The general efficiency of the linear programming approach allows the method to handle structural optimization problems with a large number of inequality constraints on the design vector. The method is demonstrated using a truss beam finite element model for the optimal sizing and placement of active/passive-structural members for damping augmentation. Results using both the sequential linear optimization approach and nonlinear optimization are presented and compared. The insensitivity to initial conditions of the linear optimization approach is also demonstrated.

Meta-Analysis of Effect Sizes Reported at Multiple Time Points Using General Linear Mixed Model.

PubMed

Musekiwa, Alfred; Manda, Samuel O M; Mwambi, Henry G; Chen, Ding-Geng

2016-01-01

Meta-analysis of longitudinal studies combines effect sizes measured at pre-determined time points. The most common approach involves performing separate univariate meta-analyses at individual time points. This simplistic approach ignores dependence between longitudinal effect sizes, which might result in less precise parameter estimates. In this paper, we show how to conduct a meta-analysis of longitudinal effect sizes where we contrast different covariance structures for dependence between effect sizes, both within and between studies. We propose new combinations of covariance structures for the dependence between effect size and utilize a practical example involving meta-analysis of 17 trials comparing postoperative treatments for a type of cancer, where survival is measured at 6, 12, 18 and 24 months post randomization. Although the results from this particular data set show the benefit of accounting for within-study serial correlation between effect sizes, simulations are required to confirm these results.

Optimization of GRIN lenses coupling system for twin-core fiber interconnection with single core fibers

NASA Astrophysics Data System (ADS)

Chen, Gongdai; Deng, Hongchang; Yuan, Libo

2018-07-01

We aim at a more compact, flexible, and simpler core-to-fiber coupling approach, optimal combinations of two graded refractive index (GRIN) lenses have been demonstrated for the interconnection between a twin-core single-mode fiber and two single-core single-mode fibers. The optimal two-lens combinations achieve an efficient core-to-fiber separating coupling and allow the fibers and lenses to coaxially assemble. Finally, axial deviations and transverse displacements of the components are discussed, and the latter increases the coupling loss more significantly. The gap length between the two lenses is designed to be fine-tuned to compensate for the transverse displacement, and the good linear compensation relationship contributes to the device manufacturing. This approach has potential applications in low coupling loss and low crosstalk devices without sophisticated alignment and adjustment, and enables the channel separating for multicore fibers.

Orthology detection combining clustering and synteny for very large datasets.

PubMed

Lechner, Marcus; Hernandez-Rosales, Maribel; Doerr, Daniel; Wieseke, Nicolas; Thévenin, Annelyse; Stoye, Jens; Hartmann, Roland K; Prohaska, Sonja J; Stadler, Peter F

2014-01-01

The elucidation of orthology relationships is an important step both in gene function prediction as well as towards understanding patterns of sequence evolution. Orthology assignments are usually derived directly from sequence similarities for large data because more exact approaches exhibit too high computational costs. Here we present PoFF, an extension for the standalone tool Proteinortho, which enhances orthology detection by combining clustering, sequence similarity, and synteny. In the course of this work, FFAdj-MCS, a heuristic that assesses pairwise gene order using adjacencies (a similarity measure related to the breakpoint distance) was adapted to support multiple linear chromosomes and extended to detect duplicated regions. PoFF largely reduces the number of false positives and enables more fine-grained predictions than purely similarity-based approaches. The extension maintains the low memory requirements and the efficient concurrency options of its basis Proteinortho, making the software applicable to very large datasets.

Orthology Detection Combining Clustering and Synteny for Very Large Datasets

PubMed Central

Lechner, Marcus; Hernandez-Rosales, Maribel; Doerr, Daniel; Wieseke, Nicolas; Thévenin, Annelyse; Stoye, Jens; Hartmann, Roland K.; Prohaska, Sonja J.; Stadler, Peter F.

2014-01-01

The elucidation of orthology relationships is an important step both in gene function prediction as well as towards understanding patterns of sequence evolution. Orthology assignments are usually derived directly from sequence similarities for large data because more exact approaches exhibit too high computational costs. Here we present PoFF, an extension for the standalone tool Proteinortho, which enhances orthology detection by combining clustering, sequence similarity, and synteny. In the course of this work, FFAdj-MCS, a heuristic that assesses pairwise gene order using adjacencies (a similarity measure related to the breakpoint distance) was adapted to support multiple linear chromosomes and extended to detect duplicated regions. PoFF largely reduces the number of false positives and enables more fine-grained predictions than purely similarity-based approaches. The extension maintains the low memory requirements and the efficient concurrency options of its basis Proteinortho, making the software applicable to very large datasets. PMID:25137074

Quantifying substrate uptake by individual cells of marine bacterioplankton by catalyzed reporter deposition fluorescence in situ hybridization combined with microautoradiography.

PubMed

Sintes, Eva; Herndl, Gerhard J

2006-11-01

Catalyzed reporter deposition fluorescence in situ hybridization combined with microautoradiography (MICRO-CARD-FISH) is increasingly being used to obtain qualitative information on substrate uptake by individual members of specific prokaryotic communities. Here we evaluated the potential for using this approach quantitatively by relating the measured silver grain area around cells taking up (3)H-labeled leucine to bulk leucine uptake measurements. The increase in the silver grain area over time around leucine-assimilating cells of coastal bacterial assemblages was linear during 4 to 6 h of incubation. By establishing standardized conditions for specific activity levels and concomitantly performing uptake measurements with the bulk community, MICRO-CARD-FISH can be used quantitatively to determine uptake rates on a single-cell level. Therefore, this approach allows comparisons of single-cell activities for bacterial communities obtained from different sites or growing under different ecological conditions.

Quantifying Substrate Uptake by Individual Cells of Marine Bacterioplankton by Catalyzed Reporter Deposition Fluorescence In Situ Hybridization Combined with Microautoradiography▿

PubMed Central

Sintes, Eva; Herndl, Gerhard J.

2006-01-01

Catalyzed reporter deposition fluorescence in situ hybridization combined with microautoradiography (MICRO-CARD-FISH) is increasingly being used to obtain qualitative information on substrate uptake by individual members of specific prokaryotic communities. Here we evaluated the potential for using this approach quantitatively by relating the measured silver grain area around cells taking up 3H-labeled leucine to bulk leucine uptake measurements. The increase in the silver grain area over time around leucine-assimilating cells of coastal bacterial assemblages was linear during 4 to 6 h of incubation. By establishing standardized conditions for specific activity levels and concomitantly performing uptake measurements with the bulk community, MICRO-CARD-FISH can be used quantitatively to determine uptake rates on a single-cell level. Therefore, this approach allows comparisons of single-cell activities for bacterial communities obtained from different sites or growing under different ecological conditions. PMID:16950912

A game theoretic approach to a finite-time disturbance attenuation problem

NASA Technical Reports Server (NTRS)

Rhee, Ihnseok; Speyer, Jason L.

1991-01-01

A disturbance attenuation problem over a finite-time interval is considered by a game theoretic approach where the control, restricted to a function of the measurement history, plays against adversaries composed of the process and measurement disturbances, and the initial state. A zero-sum game, formulated as a quadratic cost criterion subject to linear time-varying dynamics and measurements, is solved by a calculus of variation technique. By first maximizing the quadratic cost criterion with respect to the process disturbance and initial state, a full information game between the control and the measurement residual subject to the estimator dynamics results. The resulting solution produces an n-dimensional compensator which expresses the controller as a linear combination of the measurement history. A disturbance attenuation problem is solved based on the results of the game problem. For time-invariant systems it is shown that under certain conditions the time-varying controller becomes time-invariant on the infinite-time interval. The resulting controller satisfies an H(infinity) norm bound.

Dark energy and modified gravity in the Effective Field Theory of Large-Scale Structure

NASA Astrophysics Data System (ADS)

Cusin, Giulia; Lewandowski, Matthew; Vernizzi, Filippo

2018-04-01

We develop an approach to compute observables beyond the linear regime of dark matter perturbations for general dark energy and modified gravity models. We do so by combining the Effective Field Theory of Dark Energy and Effective Field Theory of Large-Scale Structure approaches. In particular, we parametrize the linear and nonlinear effects of dark energy on dark matter clustering in terms of the Lagrangian terms introduced in a companion paper [1], focusing on Horndeski theories and assuming the quasi-static approximation. The Euler equation for dark matter is sourced, via the Newtonian potential, by new nonlinear vertices due to modified gravity and, as in the pure dark matter case, by the effects of short-scale physics in the form of the divergence of an effective stress tensor. The effective fluid introduces a counterterm in the solution to the matter continuity and Euler equations, which allows a controlled expansion of clustering statistics on mildly nonlinear scales. We use this setup to compute the one-loop dark-matter power spectrum.

Fast estimate of Hartley entropy in image sharpening

NASA Astrophysics Data System (ADS)

Krbcová, Zuzana; Kukal, Jaromír.; Svihlik, Jan; Fliegel, Karel

2016-09-01

Two classes of linear IIR filters: Laplacian of Gaussian (LoG) and Difference of Gaussians (DoG) are frequently used as high pass filters for contextual vision and edge detection. They are also used for image sharpening when linearly combined with the original image. Resulting sharpening filters are radially symmetric in spatial and frequency domains. Our approach is based on the radial approximation of unknown optimal filter, which is designed as a weighted sum of Gaussian filters with various radii. The novel filter is designed for MRI image enhancement where the image intensity represents anatomical structure plus additive noise. We prefer the gradient norm of Hartley entropy of whole image intensity as a measure which has to be maximized for the best sharpening. The entropy estimation procedure is as fast as FFT included in the filter but this estimate is a continuous function of enhanced image intensities. Physically motivated heuristic is used for optimum sharpening filter design by its parameter tuning. Our approach is compared with Wiener filter on MRI images.

Multivariate quadrature for representing cloud condensation nuclei activity of aerosol populations

DOE PAGES

Fierce, Laura; McGraw, Robert L.

2017-07-26

Here, sparse representations of atmospheric aerosols are needed for efficient regional- and global-scale chemical transport models. Here we introduce a new framework for representing aerosol distributions, based on the quadrature method of moments. Given a set of moment constraints, we show how linear programming, combined with an entropy-inspired cost function, can be used to construct optimized quadrature representations of aerosol distributions. The sparse representations derived from this approach accurately reproduce cloud condensation nuclei (CCN) activity for realistically complex distributions simulated by a particleresolved model. Additionally, the linear programming techniques described in this study can be used to bound key aerosolmore » properties, such as the number concentration of CCN. Unlike the commonly used sparse representations, such as modal and sectional schemes, the maximum-entropy approach described here is not constrained to pre-determined size bins or assumed distribution shapes. This study is a first step toward a particle-based aerosol scheme that will track multivariate aerosol distributions with sufficient computational efficiency for large-scale simulations.« less

Multivariate quadrature for representing cloud condensation nuclei activity of aerosol populations

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

Fierce, Laura; McGraw, Robert L.

Here, sparse representations of atmospheric aerosols are needed for efficient regional- and global-scale chemical transport models. Here we introduce a new framework for representing aerosol distributions, based on the quadrature method of moments. Given a set of moment constraints, we show how linear programming, combined with an entropy-inspired cost function, can be used to construct optimized quadrature representations of aerosol distributions. The sparse representations derived from this approach accurately reproduce cloud condensation nuclei (CCN) activity for realistically complex distributions simulated by a particleresolved model. Additionally, the linear programming techniques described in this study can be used to bound key aerosolmore » properties, such as the number concentration of CCN. Unlike the commonly used sparse representations, such as modal and sectional schemes, the maximum-entropy approach described here is not constrained to pre-determined size bins or assumed distribution shapes. This study is a first step toward a particle-based aerosol scheme that will track multivariate aerosol distributions with sufficient computational efficiency for large-scale simulations.« less

Simulations of nanocrystals under pressure: combining electronic enthalpy and linear-scaling density-functional theory.

PubMed

Corsini, Niccolò R C; Greco, Andrea; Hine, Nicholas D M; Molteni, Carla; Haynes, Peter D

2013-08-28

We present an implementation in a linear-scaling density-functional theory code of an electronic enthalpy method, which has been found to be natural and efficient for the ab initio calculation of finite systems under hydrostatic pressure. Based on a definition of the system volume as that enclosed within an electronic density isosurface [M. Cococcioni, F. Mauri, G. Ceder, and N. Marzari, Phys. Rev. Lett. 94, 145501 (2005)], it supports both geometry optimizations and molecular dynamics simulations. We introduce an approach for calibrating the parameters defining the volume in the context of geometry optimizations and discuss their significance. Results in good agreement with simulations using explicit solvents are obtained, validating our approach. Size-dependent pressure-induced structural transformations and variations in the energy gap of hydrogenated silicon nanocrystals are investigated, including one comparable in size to recent experiments. A detailed analysis of the polyamorphic transformations reveals three types of amorphous structures and their persistence on depressurization is assessed.

Simulations of nanocrystals under pressure: Combining electronic enthalpy and linear-scaling density-functional theory

NASA Astrophysics Data System (ADS)

Corsini, Niccolò R. C.; Greco, Andrea; Hine, Nicholas D. M.; Molteni, Carla; Haynes, Peter D.

2013-08-01

We present an implementation in a linear-scaling density-functional theory code of an electronic enthalpy method, which has been found to be natural and efficient for the ab initio calculation of finite systems under hydrostatic pressure. Based on a definition of the system volume as that enclosed within an electronic density isosurface [M. Cococcioni, F. Mauri, G. Ceder, and N. Marzari, Phys. Rev. Lett. 94, 145501 (2005)], 10.1103/PhysRevLett.94.145501, it supports both geometry optimizations and molecular dynamics simulations. We introduce an approach for calibrating the parameters defining the volume in the context of geometry optimizations and discuss their significance. Results in good agreement with simulations using explicit solvents are obtained, validating our approach. Size-dependent pressure-induced structural transformations and variations in the energy gap of hydrogenated silicon nanocrystals are investigated, including one comparable in size to recent experiments. A detailed analysis of the polyamorphic transformations reveals three types of amorphous structures and their persistence on depressurization is assessed.

A linear programming approach to max-sum problem: a review.

PubMed

Werner, Tomás

2007-07-01

The max-sum labeling problem, defined as maximizing a sum of binary (i.e., pairwise) functions of discrete variables, is a general NP-hard optimization problem with many applications, such as computing the MAP configuration of a Markov random field. We review a not widely known approach to the problem, developed by Ukrainian researchers Schlesinger et al. in 1976, and show how it contributes to recent results, most importantly, those on the convex combination of trees and tree-reweighted max-product. In particular, we review Schlesinger et al.'s upper bound on the max-sum criterion, its minimization by equivalent transformations, its relation to the constraint satisfaction problem, the fact that this minimization is dual to a linear programming relaxation of the original problem, and the three kinds of consistency necessary for optimality of the upper bound. We revisit problems with Boolean variables and supermodular problems. We describe two algorithms for decreasing the upper bound. We present an example application for structural image analysis.

Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering

NASA Technical Reports Server (NTRS)

Dhaeseleer, Patrik; Liang, Shoudan; Somogyi, Roland

2000-01-01

Advances in molecular biological, analytical, and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-duster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e., who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting, and bioengineering.

Tire Force Estimation using a Proportional Integral Observer

NASA Astrophysics Data System (ADS)

Farhat, Ahmad; Koenig, Damien; Hernandez-Alcantara, Diana; Morales-Menendez, Ruben

2017-01-01

This paper addresses a method for detecting critical stability situations in the lateral vehicle dynamics by estimating the non-linear part of the tire forces. These forces indicate the road holding performance of the vehicle. The estimation method is based on a robust fault detection and estimation approach which minimize the disturbance and uncertainties to residual sensitivity. It consists in the design of a Proportional Integral Observer (PIO), while minimizing the well known H ∞ norm for the worst case uncertainties and disturbance attenuation, and combining a transient response specification. This multi-objective problem is formulated as a Linear Matrix Inequalities (LMI) feasibility problem where a cost function subject to LMI constraints is minimized. This approach is employed to generate a set of switched robust observers for uncertain switched systems, where the convergence of the observer is ensured using a Multiple Lyapunov Function (MLF). Whilst the forces to be estimated can not be physically measured, a simulation scenario with CarSimTM is presented to illustrate the developed method.

A Two-Dimensional Linear Bicharacteristic FDTD Method

NASA Technical Reports Server (NTRS)

Beggs, John H.

2002-01-01

The linear bicharacteristic scheme (LBS) was originally developed to improve unsteady solutions in computational acoustics and aeroacoustics. The LBS has previously been extended to treat lossy materials for one-dimensional problems. It is a classical leapfrog algorithm, but is combined with upwind bias in the spatial derivatives. This approach preserves the time-reversibility of the leapfrog algorithm, which results in no dissipation, and it permits more flexibility by the ability to adopt a characteristic based method. The use of characteristic variables allows the LBS to include the Perfectly Matched Layer boundary condition with no added storage or complexity. The LBS offers a central storage approach with lower dispersion than the Yee algorithm, plus it generalizes much easier to nonuniform grids. It has previously been applied to two and three-dimensional free-space electromagnetic propagation and scattering problems. This paper extends the LBS to the two-dimensional case. Results are presented for point source radiation problems, and the FDTD algorithm is chosen as a convenient reference for comparison.

On the use of information theory for the analysis of synchronous nociceptive withdrawal reflexes and somatosensory evoked potentials elicited by graded electrical stimulation.

PubMed

Arguissain, Federico G; Biurrun Manresa, José A; Mørch, Carsten D; Andersen, Ole K

2015-01-30

To date, few studies have combined the simultaneous acquisition of nociceptive withdrawal reflexes (NWR) and somatosensory evoked potentials (SEPs). In fact, it is unknown whether the combination of these two signals acquired simultaneously could provide additional information on somatosensory processing at spinal and supraspinal level compared to individual NWR and SEP signals. By using the concept of mutual information (MI), it is possible to quantify the relation between electrical stimuli and simultaneous elicited electrophysiological responses in humans based on the estimated stimulus-response signal probability distributions. All selected features from NWR and SEPs were informative in regard to the stimulus when considered individually. Specifically, the information carried by NWR features was significantly higher than the information contained in the SEP features (p<0.05). Moreover, the joint information carried by the combination of features showed an overall redundancy compared to the sum of the individual contributions. Comparison with existing methods MI can be used to quantify the information that single-trial NWR and SEP features convey, as well as the information carried jointly by NWR and SEPs. This is a model-free approach that considers linear and non-linear correlations at any order and is not constrained by parametric assumptions. The current study introduces a novel approach that allows the quantification of the individual and joint information content of single-trial NWR and SEP features. This methodology could be used to decode and interpret spinal and supraspinal interaction in studies modulating the responsiveness of the nociceptive system. Copyright © 2014 Elsevier B.V. All rights reserved.

Combination of dynamic Bayesian network classifiers for the recognition of degraded characters

NASA Astrophysics Data System (ADS)

Likforman-Sulem, Laurence; Sigelle, Marc

2009-01-01

We investigate in this paper the combination of DBN (Dynamic Bayesian Network) classifiers, either independent or coupled, for the recognition of degraded characters. The independent classifiers are a vertical HMM and a horizontal HMM whose observable outputs are the image columns and the image rows respectively. The coupled classifiers, presented in a previous study, associate the vertical and horizontal observation streams into single DBNs. The scores of the independent and coupled classifiers are then combined linearly at the decision level. We compare the different classifiers -independent, coupled or linearly combined- on two tasks: the recognition of artificially degraded handwritten digits and the recognition of real degraded old printed characters. Our results show that coupled DBNs perform better on degraded characters than the linear combination of independent HMM scores. Our results also show that the best classifier is obtained by linearly combining the scores of the best coupled DBN and the best independent HMM.

Combined linear theory/impact theory method for analysis and design of high speed configurations

NASA Technical Reports Server (NTRS)

Brooke, D.; Vondrasek, D. V.

1980-01-01

Pressure distributions on a wing body at Mach 4.63 are calculated. The combined theory is shown to give improved predictions over either linear theory or impact theory alone. The combined theory is also applied in the inverse design mode to calculate optimum camber slopes at Mach 4.63. Comparisons with optimum camber slopes obtained from unmodified linear theory show large differences. Analysis of the results indicate that the combined theory correctly predicts the effect of thickness on the loading distributions at high Mach numbers, and that finite thickness wings optimized at high Mach numbers using unmodified linear theory will not achieve the minimum drag characteristics for which they are designed.

On the use of the Reciprocity Gap Functional in inverse scattering with near-field data: An application to mammography

NASA Astrophysics Data System (ADS)

Delbary, Fabrice; Aramini, Riccardo; Bozza, Giovanni; Brignone, Massimo; Piana, Michele

2008-11-01

Microwave tomography is a non-invasive approach to the early diagnosis of breast cancer. However the problem of visualizing tumors from diffracted microwaves is a difficult nonlinear ill-posed inverse scattering problem. We propose a qualitative approach to the solution of such a problem, whereby the shape and location of cancerous tissues can be detected by means of a combination of the Reciprocity Gap Functional method and the Linear Sampling method. We validate this approach to synthetic near-fields produced by a finite element method for boundary integral equations, where the breast is mimicked by the axial view of two nested cylinders, the external one representing the skin and the internal one representing the fat tissue.

Temporal rainfall disaggregation using a multiplicative cascade model for spatial application in urban hydrology

NASA Astrophysics Data System (ADS)

Müller, H.; Haberlandt, U.

2018-01-01

Rainfall time series of high temporal resolution and spatial density are crucial for urban hydrology. The multiplicative random cascade model can be used for temporal rainfall disaggregation of daily data to generate such time series. Here, the uniform splitting approach with a branching number of 3 in the first disaggregation step is applied. To achieve a final resolution of 5 min, subsequent steps after disaggregation are necessary. Three modifications at different disaggregation levels are tested in this investigation (uniform splitting at Δt = 15 min, linear interpolation at Δt = 7.5 min and Δt = 3.75 min). Results are compared both with observations and an often used approach, based on the assumption that a time steps with Δt = 5.625 min, as resulting if a branching number of 2 is applied throughout, can be replaced with Δt = 5 min (called the 1280 min approach). Spatial consistence is implemented in the disaggregated time series using a resampling algorithm. In total, 24 recording stations in Lower Saxony, Northern Germany with a 5 min resolution have been used for the validation of the disaggregation procedure. The urban-hydrological suitability is tested with an artificial combined sewer system of about 170 hectares. The results show that all three variations outperform the 1280 min approach regarding reproduction of wet spell duration, average intensity, fraction of dry intervals and lag-1 autocorrelation. Extreme values with durations of 5 min are also better represented. For durations of 1 h, all approaches show only slight deviations from the observed extremes. The applied resampling algorithm is capable to achieve sufficient spatial consistence. The effects on the urban hydrological simulations are significant. Without spatial consistence, flood volumes of manholes and combined sewer overflow are strongly underestimated. After resampling, results using disaggregated time series as input are in the range of those using observed time series. Best overall performance regarding rainfall statistics are obtained by the method in which the disaggregation process ends at time steps with 7.5 min duration, deriving the 5 min time steps by linear interpolation. With subsequent resampling this method leads to a good representation of manhole flooding and combined sewer overflow volume in terms of hydrological simulations and outperforms the 1280 min approach.

Stochastic static fault slip inversion from geodetic data with non-negativity and bound constraints

NASA Astrophysics Data System (ADS)

Nocquet, J.-M.

2018-07-01

Despite surface displacements observed by geodesy are linear combinations of slip at faults in an elastic medium, determining the spatial distribution of fault slip remains a ill-posed inverse problem. A widely used approach to circumvent the illness of the inversion is to add regularization constraints in terms of smoothing and/or damping so that the linear system becomes invertible. However, the choice of regularization parameters is often arbitrary, and sometimes leads to significantly different results. Furthermore, the resolution analysis is usually empirical and cannot be made independently of the regularization. The stochastic approach of inverse problems provides a rigorous framework where the a priori information about the searched parameters is combined with the observations in order to derive posterior probabilities of the unkown parameters. Here, I investigate an approach where the prior probability density function (pdf) is a multivariate Gaussian function, with single truncation to impose positivity of slip or double truncation to impose positivity and upper bounds on slip for interseismic modelling. I show that the joint posterior pdf is similar to the linear untruncated Gaussian case and can be expressed as a truncated multivariate normal (TMVN) distribution. The TMVN form can then be used to obtain semi-analytical formulae for the single, 2-D or n-D marginal pdf. The semi-analytical formula involves the product of a Gaussian by an integral term that can be evaluated using recent developments in TMVN probabilities calculations. Posterior mean and covariance can also be efficiently derived. I show that the maximum posterior (MAP) can be obtained using a non-negative least-squares algorithm for the single truncated case or using the bounded-variable least-squares algorithm for the double truncated case. I show that the case of independent uniform priors can be approximated using TMVN. The numerical equivalence to Bayesian inversions using Monte Carlo Markov chain (MCMC) sampling is shown for a synthetic example and a real case for interseismic modelling in Central Peru. The TMVN method overcomes several limitations of the Bayesian approach using MCMC sampling. First, the need of computer power is largely reduced. Second, unlike Bayesian MCMC-based approach, marginal pdf, mean, variance or covariance are obtained independently one from each other. Third, the probability and cumulative density functions can be obtained with any density of points. Finally, determining the MAP is extremely fast.

Spectral likelihood expansions for Bayesian inference

NASA Astrophysics Data System (ADS)

Nagel, Joseph B.; Sudret, Bruno

2016-03-01

A spectral approach to Bayesian inference is presented. It pursues the emulation of the posterior probability density. The starting point is a series expansion of the likelihood function in terms of orthogonal polynomials. From this spectral likelihood expansion all statistical quantities of interest can be calculated semi-analytically. The posterior is formally represented as the product of a reference density and a linear combination of polynomial basis functions. Both the model evidence and the posterior moments are related to the expansion coefficients. This formulation avoids Markov chain Monte Carlo simulation and allows one to make use of linear least squares instead. The pros and cons of spectral Bayesian inference are discussed and demonstrated on the basis of simple applications from classical statistics and inverse modeling.

Numerical simulation of heat and mass transfer in unsteady nanofluid between two orthogonally moving porous coaxial disks

NASA Astrophysics Data System (ADS)

Ali, Kashif; Akbar, Muhammad Zubair; Iqbal, Muhammad Farooq; Ashraf, Muhammad

2014-10-01

The paper deals with the study of heat and mass transfer in an unsteady viscous incompressible water-based nanofluid (containing Titanium dioxide nanoparticles) between two orthogonally moving porous coaxial disks with suction. A combination of iterative (successive over relaxation) and a direct method is employed for solving the sparse systems of linear algebraic equations arising from the FD discretization of the linearized self similar ODEs. It has been noticed that the rate of mass transfer at the disks decreases with the permeability Reynolds number whether the disks are approaching or receding. The findings of the present investigation may be beneficial for the electronic industry in maintaining the electronic components under effective and safe operational conditions.

Velocity-gauge real-time TDDFT within a numerical atomic orbital basis set

NASA Astrophysics Data System (ADS)

Pemmaraju, C. D.; Vila, F. D.; Kas, J. J.; Sato, S. A.; Rehr, J. J.; Yabana, K.; Prendergast, David

2018-05-01

The interaction of laser fields with solid-state systems can be modeled efficiently within the velocity-gauge formalism of real-time time dependent density functional theory (RT-TDDFT). In this article, we discuss the implementation of the velocity-gauge RT-TDDFT equations for electron dynamics within a linear combination of atomic orbitals (LCAO) basis set framework. Numerical results obtained from our LCAO implementation, for the electronic response of periodic systems to both weak and intense laser fields, are compared to those obtained from established real-space grid and Full-Potential Linearized Augmented Planewave approaches. Potential applications of the LCAO based scheme in the context of extreme ultra-violet and soft X-ray spectroscopies involving core-electronic excitations are discussed.

An efficient variable projection formulation for separable nonlinear least squares problems.

PubMed

Gan, Min; Li, Han-Xiong

2014-05-01

We consider in this paper a class of nonlinear least squares problems in which the model can be represented as a linear combination of nonlinear functions. The variable projection algorithm projects the linear parameters out of the problem, leaving the nonlinear least squares problems involving only the nonlinear parameters. To implement the variable projection algorithm more efficiently, we propose a new variable projection functional based on matrix decomposition. The advantage of the proposed formulation is that the size of the decomposed matrix may be much smaller than those of previous ones. The Levenberg-Marquardt algorithm using finite difference method is then applied to minimize the new criterion. Numerical results show that the proposed approach achieves significant reduction in computing time.

Nonlinear spike-and-slab sparse coding for interpretable image encoding.

PubMed

Shelton, Jacquelyn A; Sheikh, Abdul-Saboor; Bornschein, Jörg; Sterne, Philip; Lücke, Jörg

2015-01-01

Sparse coding is a popular approach to model natural images but has faced two main challenges: modelling low-level image components (such as edge-like structures and their occlusions) and modelling varying pixel intensities. Traditionally, images are modelled as a sparse linear superposition of dictionary elements, where the probabilistic view of this problem is that the coefficients follow a Laplace or Cauchy prior distribution. We propose a novel model that instead uses a spike-and-slab prior and nonlinear combination of components. With the prior, our model can easily represent exact zeros for e.g. the absence of an image component, such as an edge, and a distribution over non-zero pixel intensities. With the nonlinearity (the nonlinear max combination rule), the idea is to target occlusions; dictionary elements correspond to image components that can occlude each other. There are major consequences of the model assumptions made by both (non)linear approaches, thus the main goal of this paper is to isolate and highlight differences between them. Parameter optimization is analytically and computationally intractable in our model, thus as a main contribution we design an exact Gibbs sampler for efficient inference which we can apply to higher dimensional data using latent variable preselection. Results on natural and artificial occlusion-rich data with controlled forms of sparse structure show that our model can extract a sparse set of edge-like components that closely match the generating process, which we refer to as interpretable components. Furthermore, the sparseness of the solution closely follows the ground-truth number of components/edges in the images. The linear model did not learn such edge-like components with any level of sparsity. This suggests that our model can adaptively well-approximate and characterize the meaningful generation process.

Resonant-spin-ordering of vortex cores in interacting mesomagnets

NASA Astrophysics Data System (ADS)

Jain, Shikha

2013-03-01

The magnetic system of interacting vortex-state elements have a dynamically reconfigurable ground state characterized by different relative polarities and chiralities of the individual disks; and have a corresponding dynamically controlled spectrum of collective excitation modes that determine the microwave absorption of the crystal. The development of effective methods for dynamic control of the ground state in this vortex-type magnonic crystal is of interest both from fundamental and technological viewpoints. Control of vortex chirality has been demonstrated previously using various techniques; however, control and manipulation of vortex polarities remain challenging. In this work, we present a robust and efficient way of selecting the ground state configuration of interacting magnetic elements using resonant-spin-ordering approach. This is achieved by driving the system from the linear regime of constant vortex gyrations to the non-linear regime of vortex-core reversals at a fixed excitation frequency of one of the coupled modes. Subsequently reducing the excitation field to the linear regime stabilizes the system to a polarity combination whose resonant frequency is decoupled from the initialization frequency. We have utilized the resonant approach to transition between the two polarity combinations (parallel or antiparallel) in a model system of connected dot-pairs which may form the building blocks of vortex-based magnonic crystals. Taking a step further, we have extended the technique by studying many-particle system for its potential as spin-torque oscillators or logic devices. Work at Argonne was supported by the U. S. DOE, Office of BES, under Contract No. DE-AC02-06CH11357. This work was in part supported by grant DMR-1015175 from the U. S. National Science Foundation, by a Contract from the U.S. Army TARDEC and RDECOM.

Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata

PubMed Central

Chen, Yangzhou; Guo, Yuqi; Wang, Ying

2017-01-01

In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research. PMID:28353664

Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata.

PubMed

Chen, Yangzhou; Guo, Yuqi; Wang, Ying

2017-03-29

In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research.

Nonlinear Spike-And-Slab Sparse Coding for Interpretable Image Encoding

PubMed Central

Shelton, Jacquelyn A.; Sheikh, Abdul-Saboor; Bornschein, Jörg; Sterne, Philip; Lücke, Jörg

2015-01-01

Sparse coding is a popular approach to model natural images but has faced two main challenges: modelling low-level image components (such as edge-like structures and their occlusions) and modelling varying pixel intensities. Traditionally, images are modelled as a sparse linear superposition of dictionary elements, where the probabilistic view of this problem is that the coefficients follow a Laplace or Cauchy prior distribution. We propose a novel model that instead uses a spike-and-slab prior and nonlinear combination of components. With the prior, our model can easily represent exact zeros for e.g. the absence of an image component, such as an edge, and a distribution over non-zero pixel intensities. With the nonlinearity (the nonlinear max combination rule), the idea is to target occlusions; dictionary elements correspond to image components that can occlude each other. There are major consequences of the model assumptions made by both (non)linear approaches, thus the main goal of this paper is to isolate and highlight differences between them. Parameter optimization is analytically and computationally intractable in our model, thus as a main contribution we design an exact Gibbs sampler for efficient inference which we can apply to higher dimensional data using latent variable preselection. Results on natural and artificial occlusion-rich data with controlled forms of sparse structure show that our model can extract a sparse set of edge-like components that closely match the generating process, which we refer to as interpretable components. Furthermore, the sparseness of the solution closely follows the ground-truth number of components/edges in the images. The linear model did not learn such edge-like components with any level of sparsity. This suggests that our model can adaptively well-approximate and characterize the meaningful generation process. PMID:25954947

Experimental design and statistical analysis for three-drug combination studies.

PubMed

Fang, Hong-Bin; Chen, Xuerong; Pei, Xin-Yan; Grant, Steven; Tan, Ming

2017-06-01

Drug combination is a critically important therapeutic approach for complex diseases such as cancer and HIV due to its potential for efficacy at lower, less toxic doses and the need to move new therapies rapidly into clinical trials. One of the key issues is to identify which combinations are additive, synergistic, or antagonistic. While the value of multidrug combinations has been well recognized in the cancer research community, to our best knowledge, all existing experimental studies rely on fixing the dose of one drug to reduce the dimensionality, e.g. looking at pairwise two-drug combinations, a suboptimal design. Hence, there is an urgent need to develop experimental design and analysis methods for studying multidrug combinations directly. Because the complexity of the problem increases exponentially with the number of constituent drugs, there has been little progress in the development of methods for the design and analysis of high-dimensional drug combinations. In fact, contrary to common mathematical reasoning, the case of three-drug combinations is fundamentally more difficult than two-drug combinations. Apparently, finding doses of the combination, number of combinations, and replicates needed to detect departures from additivity depends on dose-response shapes of individual constituent drugs. Thus, different classes of drugs of different dose-response shapes need to be treated as a separate case. Our application and case studies develop dose finding and sample size method for detecting departures from additivity with several common (linear and log-linear) classes of single dose-response curves. Furthermore, utilizing the geometric features of the interaction index, we propose a nonparametric model to estimate the interaction index surface by B-spine approximation and derive its asymptotic properties. Utilizing the method, we designed and analyzed a combination study of three anticancer drugs, PD184, HA14-1, and CEP3891 inhibiting myeloma H929 cell line. To our best knowledge, this is the first ever three drug combinations study performed based on the original 4D dose-response surface formed by dose ranges of three drugs.

A Fast Solver for Implicit Integration of the Vlasov--Poisson System in the Eulerian Framework

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

Garrett, C. Kristopher; Hauck, Cory D.

In this paper, we present a domain decomposition algorithm to accelerate the solution of Eulerian-type discretizations of the linear, steady-state Vlasov equation. The steady-state solver then forms a key component in the implementation of fully implicit or nearly fully implicit temporal integrators for the nonlinear Vlasov--Poisson system. The solver relies on a particular decomposition of phase space that enables the use of sweeping techniques commonly used in radiation transport applications. The original linear system for the phase space unknowns is then replaced by a smaller linear system involving only unknowns on the boundary between subdomains, which can then be solvedmore » efficiently with Krylov methods such as GMRES. Steady-state solves are combined to form an implicit Runge--Kutta time integrator, and the Vlasov equation is coupled self-consistently to the Poisson equation via a linearized procedure or a nonlinear fixed-point method for the electric field. Finally, numerical results for standard test problems demonstrate the efficiency of the domain decomposition approach when compared to the direct application of an iterative solver to the original linear system.« less

Dissecting Antibodies with Regards to Linear and Conformational Epitopes

PubMed Central

Forsström, Björn; Bisławska Axnäs, Barbara; Rockberg, Johan; Danielsson, Hanna; Bohlin, Anna; Uhlen, Mathias

2015-01-01

An important issue for the performance and specificity of an antibody is the nature of the binding to its protein target, including if the recognition involves linear or conformational epitopes. Here, we dissect polyclonal sera by creating epitope-specific antibody fractions using a combination of epitope mapping and an affinity capture approach involving both synthesized peptides and recombinant protein fragments. This allowed us to study the relative amounts of antibodies to linear and conformational epitopes in the polyclonal sera as well as the ability of each antibody-fraction to detect its target protein in Western blot assays. The majority of the analyzed polyclonal sera were found to have most of the target-specific antibodies directed towards linear epitopes and these were in many cases giving Western blot bands of correct molecular weight. In contrast, many of the antibodies towards conformational epitopes did not bind their target proteins in the Western blot assays. The results from this work have given us insights regarding the nature of the antibody response generated by immunization with recombinant protein fragments and has demonstrated the advantage of using antibodies recognizing linear epitopes for immunoassay involving wholly or partially denatured protein targets. PMID:25816293

Plasmonic modes in nanowire dimers: A study based on the hydrodynamic Drude model including nonlocal and nonlinear effects

NASA Astrophysics Data System (ADS)

Moeferdt, Matthias; Kiel, Thomas; Sproll, Tobias; Intravaia, Francesco; Busch, Kurt

2018-02-01

A combined analytical and numerical study of the modes in two distinct plasmonic nanowire systems is presented. The computations are based on a discontinuous Galerkin time-domain approach, and a fully nonlinear and nonlocal hydrodynamic Drude model for the metal is utilized. In the linear regime, these computations demonstrate the strong influence of nonlocality on the field distributions as well as on the scattering and absorption spectra. Based on these results, second-harmonic-generation efficiencies are computed over a frequency range that covers all relevant modes of the linear spectra. In order to interpret the physical mechanisms that lead to corresponding field distributions, the associated linear quasielectrostatic problem is solved analytically via conformal transformation techniques. This provides an intuitive classification of the linear excitations of the systems that is then applied to the full Maxwell case. Based on this classification, group theory facilitates the determination of the selection rules for the efficient excitation of modes in both the linear and nonlinear regimes. This leads to significantly enhanced second-harmonic generation via judiciously exploiting the system symmetries. These results regarding the mode structure and second-harmonic generation are of direct relevance to other nanoantenna systems.

A Fast Solver for Implicit Integration of the Vlasov--Poisson System in the Eulerian Framework

DOE PAGES

Garrett, C. Kristopher; Hauck, Cory D.

2018-04-05

In this paper, we present a domain decomposition algorithm to accelerate the solution of Eulerian-type discretizations of the linear, steady-state Vlasov equation. The steady-state solver then forms a key component in the implementation of fully implicit or nearly fully implicit temporal integrators for the nonlinear Vlasov--Poisson system. The solver relies on a particular decomposition of phase space that enables the use of sweeping techniques commonly used in radiation transport applications. The original linear system for the phase space unknowns is then replaced by a smaller linear system involving only unknowns on the boundary between subdomains, which can then be solvedmore » efficiently with Krylov methods such as GMRES. Steady-state solves are combined to form an implicit Runge--Kutta time integrator, and the Vlasov equation is coupled self-consistently to the Poisson equation via a linearized procedure or a nonlinear fixed-point method for the electric field. Finally, numerical results for standard test problems demonstrate the efficiency of the domain decomposition approach when compared to the direct application of an iterative solver to the original linear system.« less

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

Rodriguez, George; Gilbertson, Steve Michael

Chirped fiber Bragg grating (CFBG) sensors coupled to high speed interrogation systems are described as robust diagnostic approaches to monitoring shock wave and detonation front propagation tracking events for use in high energy density shock physics applications. Taking advantage of the linear distributed spatial encoding of the spectral band in single-mode CFBGs, embedded fiber systems and associated photonic interrogation methodologies are shown as an effective approach to sensing shock and detonation-driven loading processes along the CFBG length. Two approaches, one that detects spectral changes in the integrated spectrum of the CFBG and another coherent pulse interrogation approach that fully resolvesmore » its spectral response, shows that 100-MHz–1-GHz interrogation rates are possible with spatial resolution along the CFBG in the 50 µm to sub-millimeter range depending on the combination of CFBG parameters (i.e., length, chirp rate, spectrum) and interrogator design specifics. In conclusion, results from several dynamic tests are used to demonstrate the performance of these high speed systems for shock and detonation propagation tracking under strong and weak shock pressure loading: (1) linear detonation front tracking in the plastic bonded explosive (PBX) PBX-9501; (2) tracking of radial decaying shock with crossover to non-destructive CFBG response; (3) shock wave tracking along an aluminum cylinder wall under weak loading accompanied by dynamic strain effects in the CFBG sensor.« less

An iterative fullwave simulation approach to multiple scattering in media with randomly distributed microbubbles

NASA Astrophysics Data System (ADS)

Joshi, Aditya; Lindsey, Brooks D.; Dayton, Paul A.; Pinton, Gianmarco; Muller, Marie

2017-05-01

Ultrasound contrast agents (UCA), such as microbubbles, enhance the scattering properties of blood, which is otherwise hypoechoic. The multiple scattering interactions of the acoustic field with UCA are poorly understood due to the complexity of the multiple scattering theories and the nonlinear microbubble response. The majority of bubble models describe the behavior of UCA as single, isolated microbubbles suspended in infinite medium. Multiple scattering models such as the independent scattering approximation can approximate phase velocity and attenuation for low scatterer volume fractions. However, all current models and simulation approaches only describe multiple scattering and nonlinear bubble dynamics separately. Here we present an approach that combines two existing models: (1) a full-wave model that describes nonlinear propagation and scattering interactions in a heterogeneous attenuating medium and (2) a Paul-Sarkar model that describes the nonlinear interactions between an acoustic field and microbubbles. These two models were solved numerically and combined with an iterative approach. The convergence of this combined model was explored in silico for 0.5 × 106 microbubbles ml-1, 1% and 2% bubble concentration by volume. The backscattering predicted by our modeling approach was verified experimentally with water tank measurements performed with a 128-element linear array transducer. An excellent agreement in terms of the fundamental and harmonic acoustic fields is shown. Additionally, our model correctly predicts the phase velocity and attenuation measured using through transmission and predicted by the independent scattering approximation.

Composite solvers for linear saddle point problems arising from the incompressible Stokes equations with highly heterogeneous viscosity structure

NASA Astrophysics Data System (ADS)

Sanan, P.; Schnepp, S. M.; May, D.; Schenk, O.

2014-12-01

Geophysical applications require efficient forward models for non-linear Stokes flow on high resolution spatio-temporal domains. The bottleneck in applying the forward model is solving the linearized, discretized Stokes problem which takes the form of a large, indefinite (saddle point) linear system. Due to the heterogeniety of the effective viscosity in the elliptic operator, devising effective preconditioners for saddle point problems has proven challenging and highly problem-dependent. Nevertheless, at least three approaches show promise for preconditioning these difficult systems in an algorithmically scalable way using multigrid and/or domain decomposition techniques. The first is to work with a hierarchy of coarser or smaller saddle point problems. The second is to use the Schur complement method to decouple and sequentially solve for the pressure and velocity. The third is to use the Schur decomposition to devise preconditioners for the full operator. These involve sub-solves resembling inexact versions of the sequential solve. The choice of approach and sub-methods depends crucially on the motivating physics, the discretization, and available computational resources. Here we examine the performance trade-offs for preconditioning strategies applied to idealized models of mantle convection and lithospheric dynamics, characterized by large viscosity gradients. Due to the arbitrary topological structure of the viscosity field in geodynamical simulations, we utilize low order, inf-sup stable mixed finite element spatial discretizations which are suitable when sharp viscosity variations occur in element interiors. Particular attention is paid to possibilities within the decoupled and approximate Schur complement factorization-based monolithic approaches to leverage recently-developed flexible, communication-avoiding, and communication-hiding Krylov subspace methods in combination with `heavy' smoothers, which require solutions of large per-node sub-problems, well-suited to solution on hybrid computational clusters. To manage the combinatorial explosion of solver options (which include hybridizations of all the approaches mentioned above), we leverage the modularity of the PETSc library.

Generalized linear mixed models with varying coefficients for longitudinal data.

PubMed

Zhang, Daowen

2004-03-01

The routinely assumed parametric functional form in the linear predictor of a generalized linear mixed model for longitudinal data may be too restrictive to represent true underlying covariate effects. We relax this assumption by representing these covariate effects by smooth but otherwise arbitrary functions of time, with random effects used to model the correlation induced by among-subject and within-subject variation. Due to the usually intractable integration involved in evaluating the quasi-likelihood function, the double penalized quasi-likelihood (DPQL) approach of Lin and Zhang (1999, Journal of the Royal Statistical Society, Series B61, 381-400) is used to estimate the varying coefficients and the variance components simultaneously by representing a nonparametric function by a linear combination of fixed effects and random effects. A scaled chi-squared test based on the mixed model representation of the proposed model is developed to test whether an underlying varying coefficient is a polynomial of certain degree. We evaluate the performance of the procedures through simulation studies and illustrate their application with Indonesian children infectious disease data.

A linearization of quantum channels

NASA Astrophysics Data System (ADS)

Crowder, Tanner

2015-06-01

Because the quantum channels form a compact, convex set, we can express any quantum channel as a convex combination of extremal channels. We give a Euclidean representation for the channels whose inverses are also valid channels; these are a subset of the extreme points. They form a compact, connected Lie group, and we calculate its Lie algebra. Lastly, we calculate a maximal torus for the group and provide a constructive approach to decomposing any invertible channel into a product of elementary channels.

A systems approach to model the relationship between aflatoxin gene cluster expression, environmental factors, growth and toxin production by Aspergillus flavus

PubMed Central

Abdel-Hadi, Ahmed; Schmidt-Heydt, Markus; Parra, Roberto; Geisen, Rolf; Magan, Naresh

2012-01-01

A microarray analysis was used to examine the effect of combinations of water activity (aw, 0.995–0.90) and temperature (20–42°C) on the activation of aflatoxin biosynthetic genes (30 genes) in Aspergillus flavus grown on a conducive YES (20 g yeast extract, 150 g sucrose, 1 g MgSO4·7H2O) medium. The relative expression of 10 key genes (aflF, aflD, aflE, aflM, aflO, aflP, aflQ, aflX, aflR and aflS) in the biosynthetic pathway was examined in relation to different environmental factors and phenotypic aflatoxin B1 (AFB1) production. These data, plus data on relative growth rates and AFB1 production under different aw × temperature conditions were used to develop a mixed-growth-associated product formation model. The gene expression data were normalized and then used as a linear combination of the data for all 10 genes and combined with the physical model. This was used to relate gene expression to aw and temperature conditions to predict AFB1 production. The relationship between the observed AFB1 production provided a good linear regression fit to the predicted production based in the model. The model was then validated by examining datasets outside the model fitting conditions used (37°C, 40°C and different aw levels). The relationship between structural genes (aflD, aflM) in the biosynthetic pathway and the regulatory genes (aflS, aflJ) was examined in relation to aw and temperature by developing ternary diagrams of relative expression. These findings are important in developing a more integrated systems approach by combining gene expression, ecophysiological influences and growth data to predict mycotoxin production. This could help in developing a more targeted approach to develop prevention strategies to control such carcinogenic natural metabolites that are prevalent in many staple food products. The model could also be used to predict the impact of climate change on toxin production. PMID:21880616

Artificial Neural Network approach to develop unique Classification and Raga identification tools for Pattern Recognition in Carnatic Music

NASA Astrophysics Data System (ADS)

Srimani, P. K.; Parimala, Y. G.

2011-12-01

A unique approach has been developed to study patterns in ragas of Carnatic Classical music based on artificial neural networks. Ragas in Carnatic music which have found their roots in the Vedic period, have grown on a Scientific foundation over thousands of years. However owing to its vastness and complexities it has always been a challenge for scientists and musicologists to give an all encompassing perspective both qualitatively and quantitatively. Cognition, comprehension and perception of ragas in Indian classical music have always been the subject of intensive research, highly intriguing and many facets of these are hitherto not unravelled. This paper is an attempt to view the melakartha ragas with a cognitive perspective using artificial neural network based approach which has given raise to very interesting results. The 72 ragas of the melakartha system were defined through the combination of frequencies occurring in each of them. The data sets were trained using several neural networks. 100% accurate pattern recognition and classification was obtained using linear regression, TLRN, MLP and RBF networks. Performance of the different network topologies, by varying various network parameters, were compared. Linear regression was found to be the best performing network.

Iterative absolute electroanalytical approach to characterization of bulk redox conducting systems.

PubMed

Lewera, Adam; Miecznikowski, Krzysztof; Chojak, Malgorzata; Makowski, Oktawian; Golimowski, Jerzy; Kulesza, Pawel J

2004-05-15

A novel electroanalytical approach is proposed here, and it is demonstrated with the direct and simultaneous determination of two unknowns: the concentration of redox sites and the apparent diffusion coefficient for charge propagation in a single crystal of dodecatungstophosphoric acid. This Keggin-type polyoxometalate serves as a model bulk redox conducting inorganic material for solid-state voltammetry. The system has been investigated using an ultramicrodisk working electrode in the absence of external liquid supporting electrolyte. The analytical method requires numerical solution of the combination of two equations in which the first one describes current (or charge) in a well-defined (either spherical or linear) diffusional regime and the second general equation describes chronoamperometric (or normal pulse voltammetric current) under mixed (linear-spherical) conditions. The iterative approach is based on successive approximations through calculation and minimizing the least-squares error function. The method is fairly universal, and in principle, it can be extended to the investigation of other bulk systems including sol-gel processed materials, redox melts, and solutions on condition that they are electroactive and well behaved, they contain redox centers at sufficiently high level, and a number of electrons for the redox reaction considered is known.

Modeling first impressions from highly variable facial images.

PubMed

Vernon, Richard J W; Sutherland, Clare A M; Young, Andrew W; Hartley, Tom

2014-08-12

First impressions of social traits, such as trustworthiness or dominance, are reliably perceived in faces, and despite their questionable validity they can have considerable real-world consequences. We sought to uncover the information driving such judgments, using an attribute-based approach. Attributes (physical facial features) were objectively measured from feature positions and colors in a database of highly variable "ambient" face photographs, and then used as input for a neural network to model factor dimensions (approachability, youthful-attractiveness, and dominance) thought to underlie social attributions. A linear model based on this approach was able to account for 58% of the variance in raters' impressions of previously unseen faces, and factor-attribute correlations could be used to rank attributes by their importance to each factor. Reversing this process, neural networks were then used to predict facial attributes and corresponding image properties from specific combinations of factor scores. In this way, the factors driving social trait impressions could be visualized as a series of computer-generated cartoon face-like images, depicting how attributes change along each dimension. This study shows that despite enormous variation in ambient images of faces, a substantial proportion of the variance in first impressions can be accounted for through linear changes in objectively defined features.

lpNet: a linear programming approach to reconstruct signal transduction networks.

PubMed

Matos, Marta R A; Knapp, Bettina; Kaderali, Lars

2015-10-01

With the widespread availability of high-throughput experimental technologies it has become possible to study hundreds to thousands of cellular factors simultaneously, such as coding- or non-coding mRNA or protein concentrations. Still, extracting information about the underlying regulatory or signaling interactions from these data remains a difficult challenge. We present a flexible approach towards network inference based on linear programming. Our method reconstructs the interactions of factors from a combination of perturbation/non-perturbation and steady-state/time-series data. We show both on simulated and real data that our methods are able to reconstruct the underlying networks fast and efficiently, thus shedding new light on biological processes and, in particular, into disease's mechanisms of action. We have implemented the approach as an R package available through bioconductor. This R package is freely available under the Gnu Public License (GPL-3) from bioconductor.org (http://bioconductor.org/packages/release/bioc/html/lpNet.html) and is compatible with most operating systems (Windows, Linux, Mac OS) and hardware architectures. bettina.knapp@helmholtz-muenchen.de 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.

Combining angular differential imaging and accurate polarimetry with SPHERE/IRDIS to characterize young giant exoplanets

NASA Astrophysics Data System (ADS)

van Holstein, Rob G.; Snik, Frans; Girard, Julien H.; de Boer, Jozua; Ginski, C.; Keller, Christoph U.; Stam, Daphne M.; Beuzit, Jean-Luc; Mouillet, David; Kasper, Markus; Langlois, Maud; Zurlo, Alice; de Kok, Remco J.; Vigan, Arthur

2017-09-01

Young giant exoplanets emit infrared radiation that can be linearly polarized up to several percent. This linear polarization can trace: 1) the presence of atmospheric cloud and haze layers, 2) spatial structure, e.g. cloud bands and rotational flattening, 3) the spin axis orientation and 4) particle sizes and cloud top pressure. We introduce a novel high-contrast imaging scheme that combines angular differential imaging (ADI) and accurate near-infrared polarimetry to characterize self-luminous giant exoplanets. We implemented this technique at VLT/SPHEREIRDIS and developed the corresponding observing strategies, the polarization calibration and the data-reduction approaches. The combination of ADI and polarimetry is challenging, because the field rotation required for ADI negatively affects the polarimetric performance. By combining ADI and polarimetry we can characterize planets that can be directly imaged with a very high signal-to-noise ratio. We use the IRDIS pupil-tracking mode and combine ADI and principal component analysis to reduce speckle noise. We take advantage of IRDIS' dual-beam polarimetric mode to eliminate differential effects that severely limit the polarimetric sensitivity (flat-fielding errors, differential aberrations and seeing), and thus further suppress speckle noise. To correct for instrumental polarization effects, we apply a detailed Mueller matrix model that describes the telescope and instrument and that has an absolute polarimetric accuracy <= 0.1%. Using this technique we have observed the planets of HR 8799 and the (sub-stellar) companion PZ Tel B. Unfortunately, we do not detect a polarization signal in a first analysis. We estimate preliminary 1σ upper limits on the degree of linear polarization of ˜ 1% and ˜ 0.1% for the planets of HR 8799 and PZ Tel B, respectively. The achieved sub-percent sensitivity and accuracy show that our technique has great promise for characterizing exoplanets through direct-imaging polarimetry

A Robust Approach For Acoustic Noise Suppression In Speech Using ANFIS

NASA Astrophysics Data System (ADS)

Martinek, Radek; Kelnar, Michal; Vanus, Jan; Bilik, Petr; Zidek, Jan

2015-11-01

The authors of this article deals with the implementation of a combination of techniques of the fuzzy system and artificial intelligence in the application area of non-linear noise and interference suppression. This structure used is called an Adaptive Neuro Fuzzy Inference System (ANFIS). This system finds practical use mainly in audio telephone (mobile) communication in a noisy environment (transport, production halls, sports matches, etc). Experimental methods based on the two-input adaptive noise cancellation concept was clearly outlined. Within the experiments carried out, the authors created, based on the ANFIS structure, a comprehensive system for adaptive suppression of unwanted background interference that occurs in audio communication and degrades the audio signal. The system designed has been tested on real voice signals. This article presents the investigation and comparison amongst three distinct approaches to noise cancellation in speech; they are LMS (least mean squares) and RLS (recursive least squares) adaptive filtering and ANFIS. A careful review of literatures indicated the importance of non-linear adaptive algorithms over linear ones in noise cancellation. It was concluded that the ANFIS approach had the overall best performance as it efficiently cancelled noise even in highly noise-degraded speech. Results were drawn from the successful experimentation, subjective-based tests were used to analyse their comparative performance while objective tests were used to validate them. Implementation of algorithms was experimentally carried out in Matlab to justify the claims and determine their relative performances.

Reliable estimation of orbit errors in spaceborne SAR interferometry. The network approach

NASA Astrophysics Data System (ADS)

Bähr, Hermann; Hanssen, Ramon F.

2012-12-01

An approach to improve orbital state vectors by orbit error estimates derived from residual phase patterns in synthetic aperture radar interferograms is presented. For individual interferograms, an error representation by two parameters is motivated: the baseline error in cross-range and the rate of change of the baseline error in range. For their estimation, two alternatives are proposed: a least squares approach that requires prior unwrapping and a less reliable gridsearch method handling the wrapped phase. In both cases, reliability is enhanced by mutual control of error estimates in an overdetermined network of linearly dependent interferometric combinations of images. Thus, systematic biases, e.g., due to unwrapping errors, can be detected and iteratively eliminated. Regularising the solution by a minimum-norm condition results in quasi-absolute orbit errors that refer to particular images. For the 31 images of a sample ENVISAT dataset, orbit corrections with a mutual consistency on the millimetre level have been inferred from 163 interferograms. The method itself qualifies by reliability and rigorous geometric modelling of the orbital error signal but does not consider interfering large scale deformation effects. However, a separation may be feasible in a combined processing with persistent scatterer approaches or by temporal filtering of the estimates.

An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation.

PubMed

Warid, Warid; Hizam, Hashim; Mariun, Norman; Abdul-Wahab, Noor Izzri

2016-01-01

This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality.

An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation

PubMed Central

Warid, Warid; Hizam, Hashim; Mariun, Norman; Abdul-Wahab, Noor Izzri

2016-01-01

This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality. PMID:26954783

The Extremely Low Activity Comet 209P/LINEAR During Its Extraordinary Close Approach in 2014

NASA Astrophysics Data System (ADS)

Schleicher, David G.; knight, Matthew m.

2016-10-01

We present results from our observing campaign of Comet 209P/LINEAR during its exceptionally close approach to Earth during 2014 May, the third smallest perigee of any comet in two centuries. These circumstances permitted us to pursue several studies of this intrinsically faint object, including measurements of gas and dust production rates, searching for coma morphology, and direct detection of the nucleus to measure its properties. Indeed, we successfully measured the lowest water production rates of an intact comet in over 35 years and a corresponding smallest active area, ∼0.007 km2. When combined with the nucleus size found from radar, this also yields the smallest active fraction for any comet, ∼0.024%. In all, this strongly suggests that 209P/LINEAR is on its way to becoming an inert object. The nucleus was detected but could not easily be disentangled from the inner coma due to seeing variations and changing spatial scales. Even so, we were able to measure a double-peaked lightcurve consistent with the shorter of two viable rotational periods found by Hergenrother. Radial profiles of the dust coma are quite steep, similar to that observed for some other very anemic comets, and suggest that vaporizing icy grains are present.

A simplified calculation procedure for mass isotopomer distribution analysis (MIDA) based on multiple linear regression.

PubMed

Fernández-Fernández, Mario; Rodríguez-González, Pablo; García Alonso, J Ignacio

2016-10-01

We have developed a novel, rapid and easy calculation procedure for Mass Isotopomer Distribution Analysis based on multiple linear regression which allows the simultaneous calculation of the precursor pool enrichment and the fraction of newly synthesized labelled proteins (fractional synthesis) using linear algebra. To test this approach, we used the peptide RGGGLK as a model tryptic peptide containing three subunits of glycine. We selected glycine labelled in two 13 C atoms ( 13 C 2 -glycine) as labelled amino acid to demonstrate that spectral overlap is not a problem in the proposed methodology. The developed methodology was tested first in vitro by changing the precursor pool enrichment from 10 to 40% of 13 C 2 -glycine. Secondly, a simulated in vivo synthesis of proteins was designed by combining the natural abundance RGGGLK peptide and 10 or 20% 13 C 2 -glycine at 1 : 1, 1 : 3 and 3 : 1 ratios. Precursor pool enrichments and fractional synthesis values were calculated with satisfactory precision and accuracy using a simple spreadsheet. This novel approach can provide a relatively rapid and easy means to measure protein turnover based on stable isotope tracers. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

An Efficient Test for Gene-Environment Interaction in Generalized Linear Mixed Models with Family Data.

PubMed

Mazo Lopera, Mauricio A; Coombes, Brandon J; de Andrade, Mariza

2017-09-27

Gene-environment (GE) interaction has important implications in the etiology of complex diseases that are caused by a combination of genetic factors and environment variables. Several authors have developed GE analysis in the context of independent subjects or longitudinal data using a gene-set. In this paper, we propose to analyze GE interaction for discrete and continuous phenotypes in family studies by incorporating the relatedness among the relatives for each family into a generalized linear mixed model (GLMM) and by using a gene-based variance component test. In addition, we deal with collinearity problems arising from linkage disequilibrium among single nucleotide polymorphisms (SNPs) by considering their coefficients as random effects under the null model estimation. We show that the best linear unbiased predictor (BLUP) of such random effects in the GLMM is equivalent to the ridge regression estimator. This equivalence provides a simple method to estimate the ridge penalty parameter in comparison to other computationally-demanding estimation approaches based on cross-validation schemes. We evaluated the proposed test using simulation studies and applied it to real data from the Baependi Heart Study consisting of 76 families. Using our approach, we identified an interaction between BMI and the Peroxisome Proliferator Activated Receptor Gamma ( PPARG ) gene associated with diabetes.

Linear ordinary differential equations with constant coefficients. Revisiting the impulsive response method using factorization

NASA Astrophysics Data System (ADS)

Camporesi, Roberto

2011-06-01

We present an approach to the impulsive response method for solving linear constant-coefficient ordinary differential equations based on the factorization of the differential operator. The approach is elementary, we only assume a basic knowledge of calculus and linear algebra. In particular, we avoid the use of distribution theory, as well as of the other more advanced approaches: Laplace transform, linear systems, the general theory of linear equations with variable coefficients and the variation of constants method. The approach presented here can be used in a first course on differential equations for science and engineering majors.

A Combined Photochemical and Multicomponent Reaction Approach to Precision Oligomers.

PubMed

Konrad, Waldemar; Bloesser, Fabian R; Wetzel, Katharina S; Boukis, Andreas C; Meier, Michael A R; Barner-Kowollik, Christopher

2018-03-07

We introduce the convergent synthesis of linear monodisperse sequence-defined oligomers through a unique approach, combining the Passerini three-component reaction (P-3CR) and a Diels-Alder (DA) reaction based on photocaged dienes. A set of oligomers is prepared resting on a Passerini linker unit carrying an isocyano group for chain extension by P-3CR and a maleimide moiety for photoenol conjugation enabling a modular approach for chain growth. Monodisperse oligomers are accessible in a stepwise fashion by switching between both reaction types. Employing sebacic acid as a core unit allows the synthesis of a library of symmetric sequence-defined oligomers. The oligomers consist of alternating P-3CR and photoblocks with molecular weights up to 3532.16 g mol -1 , demonstrating the successful switching from P-3CR to photoenol conjugation. In-depth characterization was carried out including size-exclusion chromatography (SEC), high-resolution electrospray ionization mass spectrometry (ESI-MS) and NMR spectroscopy, evidencing the monodisperse nature of the precision oligomers. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

A heuristic approach using multiple criteria for environmentally benign 3PLs selection

NASA Astrophysics Data System (ADS)

Kongar, Elif

2005-11-01

Maintaining competitiveness in an environment where price and quality differences between competing products are disappearing depends on the company's ability to reduce costs and supply time. Timely responses to rapidly changing market conditions require an efficient Supply Chain Management (SCM). Outsourcing logistics to third-party logistics service providers (3PLs) is one commonly used way of increasing the efficiency of logistics operations, while creating a more "core competency focused" business environment. However, this alone may not be sufficient. Due to recent environmental regulations and growing public awareness regarding environmental issues, 3PLs need to be not only efficient but also environmentally benign to maintain companies' competitiveness. Even though an efficient and environmentally benign combination of 3PLs can theoretically be obtained using exhaustive search algorithms, heuristics approaches to the selection process may be superior in terms of the computational complexity. In this paper, a hybrid approach that combines a multiple criteria Genetic Algorithm (GA) with Linear Physical Weighting Algorithm (LPPW) to be used in efficient and environmentally benign 3PLs is proposed. A numerical example is also provided to illustrate the method and the analyses.

Nonparametric regression applied to quantitative structure-activity relationships

PubMed

Constans; Hirst

2000-03-01

Several nonparametric regressors have been applied to modeling quantitative structure-activity relationship (QSAR) data. The simplest regressor, the Nadaraya-Watson, was assessed in a genuine multivariate setting. Other regressors, the local linear and the shifted Nadaraya-Watson, were implemented within additive models--a computationally more expedient approach, better suited for low-density designs. Performances were benchmarked against the nonlinear method of smoothing splines. A linear reference point was provided by multilinear regression (MLR). Variable selection was explored using systematic combinations of different variables and combinations of principal components. For the data set examined, 47 inhibitors of dopamine beta-hydroxylase, the additive nonparametric regressors have greater predictive accuracy (as measured by the mean absolute error of the predictions or the Pearson correlation in cross-validation trails) than MLR. The use of principal components did not improve the performance of the nonparametric regressors over use of the original descriptors, since the original descriptors are not strongly correlated. It remains to be seen if the nonparametric regressors can be successfully coupled with better variable selection and dimensionality reduction in the context of high-dimensional QSARs.

Daubechies wavelets for linear scaling density functional theory.

PubMed

Mohr, Stephan; Ratcliff, Laura E; Boulanger, Paul; Genovese, Luigi; Caliste, Damien; Deutsch, Thierry; Goedecker, Stefan

2014-05-28

We demonstrate that Daubechies wavelets can be used to construct a minimal set of optimized localized adaptively contracted basis functions in which the Kohn-Sham orbitals can be represented with an arbitrarily high, controllable precision. Ground state energies and the forces acting on the ions can be calculated in this basis with the same accuracy as if they were calculated directly in a Daubechies wavelets basis, provided that the amplitude of these adaptively contracted basis functions is sufficiently small on the surface of the localization region, which is guaranteed by the optimization procedure described in this work. This approach reduces the computational costs of density functional theory calculations, and can be combined with sparse matrix algebra to obtain linear scaling with respect to the number of electrons in the system. Calculations on systems of 10,000 atoms or more thus become feasible in a systematic basis set with moderate computational resources. Further computational savings can be achieved by exploiting the similarity of the adaptively contracted basis functions for closely related environments, e.g., in geometry optimizations or combined calculations of neutral and charged systems.

Synthesis and spectral properties of Methyl-Phenyl pyrazoloquinoxaline fluorescence emitters: Experiment and DFT/TDDFT calculations

NASA Astrophysics Data System (ADS)

Gąsiorski, P.; Matusiewicz, M.; Gondek, E.; Uchacz, T.; Wojtasik, K.; Danel, A.; Shchur, Ya.; Kityk, A. V.

2018-01-01

Paper reports the synthesis and spectroscopic studies of two novel 1-Methyl-3-phenyl-1H-pyrazolo[3,4-b]quinoxaline (PQX) derivatives with 6-substituted methyl (MeMPPQX) or methoxy (MeOMPPQX) side groups. The optical absorption and fluorescence emission spectra are recorded in solvents of different polarity. Steady state and time-resolved spectroscopy provide photophysical characterization of MeMPPQX and MeOMPPQX dyes as materials for potential luminescence or electroluminescence applications. Measured optical absorption and fluorescence emission spectra are compared with quantum-chemical DFT/TDDFT calculations using long-range corrected xc-functionals, LRC-BLYP and CAM-B3LYP in combination with self-consistent reaction field model based on linear response (LR), state specific (SS) or corrected linear response (CLR) solvations. Performances of relevant theoretical models and approaches are compared. The reparameterized LRC-BLYP functional (ω = 0.231 Bohr-1) in combination with CLR solvation provides most accurate prediction of both excitation and emission energies. The MeMPPQX and MeOMPPQX dyes represent efficient fluorescence emitters in blue-green region of the visible spectra.

Fluctuation removal around spectral and temporal constancy limits via use of an extended space expectation value weight function for singular quantum systems

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

Kalay, Berfin; Demiralp, Metin

2015-03-10

This work is a new extension to our a very recent work whose paper will appear in the proceedings of a very recent international conference. What we have done in the previous work is the use of a weight operator to suppress the singularities causing nonexistence of some of temporal Maclaurin expansion coefficients. The weight operator has been constructed in such a way that certain number of expectation values of position operator’s first positive integer powers with and without the chosen weight operator match. Therein this match has not been considered for the momentum operator’s corresponding power expectation values andmore » a finite linear combination of the spatial variable’s first reciprocal powers has been used in the construction of the weight operator. Here, that approach is extended to the case where matches for both position and momentum operators are considered and the weight operator involves finite linear combinations of the spatial variable’s both positive integer powers and their reciprocals.« less

Multineuron spike train analysis with R-convolution linear combination kernel.

PubMed

Tezuka, Taro

2018-06-01

A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Avionic Air Data Sensors Fault Detection and Isolation by means of Singular Perturbation and Geometric Approach

PubMed Central

2017-01-01

Singular Perturbations represent an advantageous theory to deal with systems characterized by a two-time scale separation, such as the longitudinal dynamics of aircraft which are called phugoid and short period. In this work, the combination of the NonLinear Geometric Approach and the Singular Perturbations leads to an innovative Fault Detection and Isolation system dedicated to the isolation of faults affecting the air data system of a general aviation aircraft. The isolation capabilities, obtained by means of the approach proposed in this work, allow for the solution of a fault isolation problem otherwise not solvable by means of standard geometric techniques. Extensive Monte-Carlo simulations, exploiting a high fidelity aircraft simulator, show the effectiveness of the proposed Fault Detection and Isolation system. PMID:28946673

Unbiased split variable selection for random survival forests using maximally selected rank statistics.

PubMed

Wright, Marvin N; Dankowski, Theresa; Ziegler, Andreas

2017-04-15

The most popular approach for analyzing survival data is the Cox regression model. The Cox model may, however, be misspecified, and its proportionality assumption may not always be fulfilled. An alternative approach for survival prediction is random forests for survival outcomes. The standard split criterion for random survival forests is the log-rank test statistic, which favors splitting variables with many possible split points. Conditional inference forests avoid this split variable selection bias. However, linear rank statistics are utilized by default in conditional inference forests to select the optimal splitting variable, which cannot detect non-linear effects in the independent variables. An alternative is to use maximally selected rank statistics for the split point selection. As in conditional inference forests, splitting variables are compared on the p-value scale. However, instead of the conditional Monte-Carlo approach used in conditional inference forests, p-value approximations are employed. We describe several p-value approximations and the implementation of the proposed random forest approach. A simulation study demonstrates that unbiased split variable selection is possible. However, there is a trade-off between unbiased split variable selection and runtime. In benchmark studies of prediction performance on simulated and real datasets, the new method performs better than random survival forests if informative dichotomous variables are combined with uninformative variables with more categories and better than conditional inference forests if non-linear covariate effects are included. In a runtime comparison, the method proves to be computationally faster than both alternatives, if a simple p-value approximation is used. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Restoring Low Sidelobe Antenna Patterns with Failed Elements in a Phased Array Antenna

DTIC Science & Technology

2016-02-01

optimum low sidelobes are demonstrated in several examples. Index Terms — Array signal processing, beams, linear algebra , phased arrays, shaped...represented by a linear combination of low sidelobe beamformers with no failed elements, ’s, in a neighborhood around under the constraint that the linear ...would expect that linear combinations of them in a neighborhood around would also have low sidelobes. The algorithms in this paper exploit this

Aerosol Impacts on Cirrus Clouds and High-Power Laser Transmission: A Combined Satellite Observation and Modeling Approach

DTIC Science & Technology

2009-03-22

indirect effect (AIE) index determined from the slope of the fitted linear equation involving cloud particle size vs. aerosol optical depth is about a... raindrop . The model simulations were performed for a 48-hour period, starting at 00Z on 29 March 2007, about 20 hours prior to ABL test flight time...UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) MS. KRISTEN LUND UNIV OF CALIFORNIA LOS ANGELES, CA 90095 8. PERFORMING

Improving EMG based classification of basic hand movements using EMD.

PubMed

Sapsanis, Christos; Georgoulas, George; Tzes, Anthony; Lymberopoulos, Dimitrios

2013-01-01

This paper presents a pattern recognition approach for the identification of basic hand movements using surface electromyographic (EMG) data. The EMG signal is decomposed using Empirical Mode Decomposition (EMD) into Intrinsic Mode Functions (IMFs) and subsequently a feature extraction stage takes place. Various combinations of feature subsets are tested using a simple linear classifier for the detection task. Our results suggest that the use of EMD can increase the discrimination ability of the conventional feature sets extracted from the raw EMG signal.

A study on ?-dissipative synchronisation of coupled reaction-diffusion neural networks with time-varying delays

NASA Astrophysics Data System (ADS)

Ali, M. Syed; Zhu, Quanxin; Pavithra, S.; Gunasekaran, N.

2018-03-01

This study examines the problem of dissipative synchronisation of coupled reaction-diffusion neural networks with time-varying delays. This paper proposes a complex dynamical network consisting of N linearly and diffusively coupled identical reaction-diffusion neural networks. By constructing a suitable Lyapunov-Krasovskii functional (LKF), utilisation of Jensen's inequality and reciprocally convex combination (RCC) approach, strictly ?-dissipative conditions of the addressed systems are derived. Finally, a numerical example is given to show the effectiveness of the theoretical results.

The application of finite volume methods for modelling three-dimensional incompressible flow on an unstructured mesh

NASA Astrophysics Data System (ADS)

Lonsdale, R. D.; Webster, R.

This paper demonstrates the application of a simple finite volume approach to a finite element mesh, combining the economy of the former with the geometrical flexibility of the latter. The procedure is used to model a three-dimensional flow on a mesh of linear eight-node brick (hexahedra). Simulations are performed for a wide range of flow problems, some in excess of 94,000 nodes. The resulting computer code ASTEC that incorporates these procedures is described.

The Group Selection or Assessment Centre (AC) Approach to Personnel Selection: An Annotated Bibliography,

DTIC Science & Technology

1988-02-01

sponsors of ACs, e.g. their cost in terms of the time lost by senior managers because of their partici.nnts as assessors. Emhasises the need to address...American Psychologist. 37, 180-189. Reports use of the AC method to study the progress of a group of managers over a period of time . The study included...on 254 managers attending an AC, for changes in validities over time . Peer and assessor ratings, along with linear combinations, were found

Velocity-gauge real-time TDDFT within a numerical atomic orbital basis set

DOE PAGES

Pemmaraju, C. D.; Vila, F. D.; Kas, J. J.; ...

2018-02-07

The interaction of laser fields with solid-state systems can be modeled efficiently within the velocity-gauge formalism of real-time time dependent density functional theory (RT-TDDFT). In this article, we discuss the implementation of the velocity-gauge RT-TDDFT equations for electron dynamics within a linear combination of atomic orbitals (LCAO) basis set framework. Numerical results obtained from our LCAO implementation, for the electronic response of periodic systems to both weak and intense laser fields, are compared to those obtained from established real-space grid and Full-Potential Linearized Augmented Planewave approaches. As a result, potential applications of the LCAO based scheme in the context ofmore » extreme ultra-violet and soft X-ray spectroscopies involving core-electronic excitations are discussed.« less

Aether: leveraging linear programming for optimal cloud computing in genomics.

PubMed

Luber, Jacob M; Tierney, Braden T; Cofer, Evan M; Patel, Chirag J; Kostic, Aleksandar D

2018-05-01

Across biology, we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities. Here, we present Aether (http://aether.kosticlab.org), an intuitive, easy-to-use, cost-effective and scalable framework that uses linear programming to optimally bid on and deploy combinations of underutilized cloud computing resources. Our approach simultaneously minimizes the cost of data analysis and provides an easy transition from users' existing HPC pipelines. Data utilized are available at https://pubs.broadinstitute.org/diabimmune and with EBI SRA accession ERP005989. Source code is available at (https://github.com/kosticlab/aether). Examples, documentation and a tutorial are available at http://aether.kosticlab.org. chirag_patel@hms.harvard.edu or aleksandar.kostic@joslin.harvard.edu. Supplementary data are available at Bioinformatics online.

An approach of traffic signal control based on NLRSQP algorithm

NASA Astrophysics Data System (ADS)

Zou, Yuan-Yang; Hu, Yu

2017-11-01

This paper presents a linear program model with linear complementarity constraints (LPLCC) to solve traffic signal optimization problem. The objective function of the model is to obtain the minimization of total queue length with weight factors at the end of each cycle. Then, a combination algorithm based on the nonlinear least regression and sequence quadratic program (NLRSQP) is proposed, by which the local optimal solution can be obtained. Furthermore, four numerical experiments are proposed to study how to set the initial solution of the algorithm that can get a better local optimal solution more quickly. In particular, the results of numerical experiments show that: The model is effective for different arrival rates and weight factors; and the lower bound of the initial solution is, the better optimal solution can be obtained.

Velocity-gauge real-time TDDFT within a numerical atomic orbital basis set

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

Pemmaraju, C. D.; Vila, F. D.; Kas, J. J.

The interaction of laser fields with solid-state systems can be modeled efficiently within the velocity-gauge formalism of real-time time dependent density functional theory (RT-TDDFT). In this article, we discuss the implementation of the velocity-gauge RT-TDDFT equations for electron dynamics within a linear combination of atomic orbitals (LCAO) basis set framework. Numerical results obtained from our LCAO implementation, for the electronic response of periodic systems to both weak and intense laser fields, are compared to those obtained from established real-space grid and Full-Potential Linearized Augmented Planewave approaches. As a result, potential applications of the LCAO based scheme in the context ofmore » extreme ultra-violet and soft X-ray spectroscopies involving core-electronic excitations are discussed.« less

Efficient Kriging Algorithms

NASA Technical Reports Server (NTRS)

Memarsadeghi, Nargess

2011-01-01

More efficient versions of an interpolation method, called kriging, have been introduced in order to reduce its traditionally high computational cost. Written in C++, these approaches were tested on both synthetic and real data. Kriging is a best unbiased linear estimator and suitable for interpolation of scattered data points. Kriging has long been used in the geostatistic and mining communities, but is now being researched for use in the image fusion of remotely sensed data. This allows a combination of data from various locations to be used to fill in any missing data from any single location. To arrive at the faster algorithms, sparse SYMMLQ iterative solver, covariance tapering, Fast Multipole Methods (FMM), and nearest neighbor searching techniques were used. These implementations were used when the coefficient matrix in the linear system is symmetric, but not necessarily positive-definite.

Microhartree precision in density functional theory calculations

NASA Astrophysics Data System (ADS)

Gulans, Andris; Kozhevnikov, Anton; Draxl, Claudia

2018-04-01

To address ultimate precision in density functional theory calculations we employ the full-potential linearized augmented plane-wave + local-orbital (LAPW + lo) method and justify its usage as a benchmark method. LAPW + lo and two completely unrelated numerical approaches, the multiresolution analysis (MRA) and the linear combination of atomic orbitals, yield total energies of atoms with mean deviations of 0.9 and 0.2 μ Ha , respectively. Spectacular agreement with the MRA is reached also for total and atomization energies of the G2-1 set consisting of 55 molecules. With the example of α iron we demonstrate the capability of LAPW + lo to reach μ Ha /atom precision also for periodic systems, which allows also for the distinction between the numerical precision and the accuracy of a given functional.

Combining fixed effects and instrumental variable approaches for estimating the effect of psychosocial job quality on mental health: evidence from 13 waves of a nationally representative cohort study.

PubMed

Milner, Allison; Aitken, Zoe; Kavanagh, Anne; LaMontagne, Anthony D; Pega, Frank; Petrie, Dennis

2017-06-23

Previous studies suggest that poor psychosocial job quality is a risk factor for mental health problems, but they use conventional regression analytic methods that cannot rule out reverse causation, unmeasured time-invariant confounding and reporting bias. This study combines two quasi-experimental approaches to improve causal inference by better accounting for these biases: (i) linear fixed effects regression analysis and (ii) linear instrumental variable analysis. We extract 13 annual waves of national cohort data including 13 260 working-age (18-64 years) employees. The exposure variable is self-reported level of psychosocial job quality. The instruments used are two common workplace entitlements. The outcome variable is the Mental Health Inventory (MHI-5). We adjust for measured time-varying confounders. In the fixed effects regression analysis adjusted for time-varying confounders, a 1-point increase in psychosocial job quality is associated with a 1.28-point improvement in mental health on the MHI-5 scale (95% CI: 1.17, 1.40; P < 0.001). When the fixed effects was combined with the instrumental variable analysis, a 1-point increase psychosocial job quality is related to 1.62-point improvement on the MHI-5 scale (95% CI: -0.24, 3.48; P = 0.088). Our quasi-experimental results provide evidence to confirm job stressors as risk factors for mental ill health using methods that improve causal inference. © The Author 2017. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

Digital Image Restoration Under a Regression Model - The Unconstrained, Linear Equality and Inequality Constrained Approaches

DTIC Science & Technology

1974-01-01

REGRESSION MODEL - THE UNCONSTRAINED, LINEAR EQUALITY AND INEQUALITY CONSTRAINED APPROACHES January 1974 Nelson Delfino d’Avila Mascarenha;? Image...Report 520 DIGITAL IMAGE RESTORATION UNDER A REGRESSION MODEL THE UNCONSTRAINED, LINEAR EQUALITY AND INEQUALITY CONSTRAINED APPROACHES January...a two- dimensional form adequately describes the linear model . A dis- cretization is performed by using quadrature methods. By trans

The calculation of steady non-linear transonic flow over finite wings with linear theory aerodynamics

NASA Technical Reports Server (NTRS)

Cunningham, A. M., Jr.

1976-01-01

The feasibility of calculating steady mean flow solutions for nonlinear transonic flow over finite wings with a linear theory aerodynamic computer program is studied. The methodology is based on independent solutions for upper and lower surface pressures that are coupled through the external flow fields. Two approaches for coupling the solutions are investigated which include the diaphragm and the edge singularity method. The final method is a combination of both where a line source along the wing leading edge is used to account for blunt nose airfoil effects; and the upper and lower surface flow fields are coupled through a diaphragm in the plane of the wing. An iterative solution is used to arrive at the nonuniform flow solution for both nonlifting and lifting cases. Final results for a swept tapered wing in subcritical flow show that the method converges in three iterations and gives excellent agreement with experiment at alpha = 0 deg and 2 deg. Recommendations are made for development of a procedure for routine application.

Non-linear dynamic compensation system

NASA Technical Reports Server (NTRS)

Lin, Yu-Hwan (Inventor); Lurie, Boris J. (Inventor)

1992-01-01

A non-linear dynamic compensation subsystem is added in the feedback loop of a high precision optical mirror positioning control system to smoothly alter the control system response bandwidth from a relatively wide response bandwidth optimized for speed of control system response to a bandwidth sufficiently narrow to reduce position errors resulting from the quantization noise inherent in the inductosyn used to measure mirror position. The non-linear dynamic compensation system includes a limiter for limiting the error signal within preselected limits, a compensator for modifying the limiter output to achieve the reduced bandwidth response, and an adder for combining the modified error signal with the difference between the limited and unlimited error signals. The adder output is applied to control system motor so that the system response is optimized for accuracy when the error signal is within the preselected limits, optimized for speed of response when the error signal is substantially beyond the preselected limits and smoothly varied therebetween as the error signal approaches the preselected limits.

Optical Measurement of Radiocarbon below Unity Fraction Modern by Linear Absorption Spectroscopy.

PubMed

Fleisher, Adam J; Long, David A; Liu, Qingnan; Gameson, Lyn; Hodges, Joseph T

2017-09-21

High-precision measurements of radiocarbon ( 14 C) near or below a fraction modern 14 C of 1 (F 14 C ≤ 1) are challenging and costly. An accurate, ultrasensitive linear absorption approach to detecting 14 C would provide a simple and robust benchtop alternative to off-site accelerator mass spectrometry facilities. Here we report the quantitative measurement of 14 C in gas-phase samples of CO 2 with F 14 C < 1 using cavity ring-down spectroscopy in the linear absorption regime. Repeated analysis of CO 2 derived from the combustion of either biogenic or petrogenic sources revealed a robust ability to differentiate samples with F 14 C < 1. With a combined uncertainty of 14 C/ 12 C = 130 fmol/mol (F 14 C = 0.11), initial performance of the calibration-free instrument is sufficient to investigate a variety of applications in radiocarbon measurement science including the study of biofuels and bioplastics, illicitly traded specimens, bomb dating, and atmospheric transport.

Multivariate meta-analysis for non-linear and other multi-parameter associations

PubMed Central

Gasparrini, A; Armstrong, B; Kenward, M G

2012-01-01

In this paper, we formalize the application of multivariate meta-analysis and meta-regression to synthesize estimates of multi-parameter associations obtained from different studies. This modelling approach extends the standard two-stage analysis used to combine results across different sub-groups or populations. The most straightforward application is for the meta-analysis of non-linear relationships, described for example by regression coefficients of splines or other functions, but the methodology easily generalizes to any setting where complex associations are described by multiple correlated parameters. The modelling framework of multivariate meta-analysis is implemented in the package mvmeta within the statistical environment R. As an illustrative example, we propose a two-stage analysis for investigating the non-linear exposure–response relationship between temperature and non-accidental mortality using time-series data from multiple cities. Multivariate meta-analysis represents a useful analytical tool for studying complex associations through a two-stage procedure. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22807043

Simultaneous fitting of genomic-BLUP and Bayes-C components in a genomic prediction model.

PubMed

Iheshiulor, Oscar O M; Woolliams, John A; Svendsen, Morten; Solberg, Trygve; Meuwissen, Theo H E

2017-08-24

The rapid adoption of genomic selection is due to two key factors: availability of both high-throughput dense genotyping and statistical methods to estimate and predict breeding values. The development of such methods is still ongoing and, so far, there is no consensus on the best approach. Currently, the linear and non-linear methods for genomic prediction (GP) are treated as distinct approaches. The aim of this study was to evaluate the implementation of an iterative method (called GBC) that incorporates aspects of both linear [genomic-best linear unbiased prediction (G-BLUP)] and non-linear (Bayes-C) methods for GP. The iterative nature of GBC makes it less computationally demanding similar to other non-Markov chain Monte Carlo (MCMC) approaches. However, as a Bayesian method, GBC differs from both MCMC- and non-MCMC-based methods by combining some aspects of G-BLUP and Bayes-C methods for GP. Its relative performance was compared to those of G-BLUP and Bayes-C. We used an imputed 50 K single-nucleotide polymorphism (SNP) dataset based on the Illumina Bovine50K BeadChip, which included 48,249 SNPs and 3244 records. Daughter yield deviations for somatic cell count, fat yield, milk yield, and protein yield were used as response variables. GBC was frequently (marginally) superior to G-BLUP and Bayes-C in terms of prediction accuracy and was significantly better than G-BLUP only for fat yield. On average across the four traits, GBC yielded a 0.009 and 0.006 increase in prediction accuracy over G-BLUP and Bayes-C, respectively. Computationally, GBC was very much faster than Bayes-C and similar to G-BLUP. Our results show that incorporating some aspects of G-BLUP and Bayes-C in a single model can improve accuracy of GP over the commonly used method: G-BLUP. Generally, GBC did not statistically perform better than G-BLUP and Bayes-C, probably due to the close relationships between reference and validation individuals. Nevertheless, it is a flexible tool, in the sense, that it simultaneously incorporates some aspects of linear and non-linear models for GP, thereby exploiting family relationships while also accounting for linkage disequilibrium between SNPs and genes with large effects. The application of GBC in GP merits further exploration.

Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects.

PubMed

Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

2016-01-01

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms in the future.

Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects

PubMed Central

Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

2017-01-01

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms in the future. PMID:28167896

GWAS with longitudinal phenotypes: performance of approximate procedures

PubMed Central

Sikorska, Karolina; Montazeri, Nahid Mostafavi; Uitterlinden, André; Rivadeneira, Fernando; Eilers, Paul HC; Lesaffre, Emmanuel

2015-01-01

Analysis of genome-wide association studies with longitudinal data using standard procedures, such as linear mixed model (LMM) fitting, leads to discouragingly long computation times. There is a need to speed up the computations significantly. In our previous work (Sikorska et al: Fast linear mixed model computations for genome-wide association studies with longitudinal data. Stat Med 2012; 32.1: 165–180), we proposed the conditional two-step (CTS) approach as a fast method providing an approximation to the P-value for the longitudinal single-nucleotide polymorphism (SNP) effect. In the first step a reduced conditional LMM is fit, omitting all the SNP terms. In the second step, the estimated random slopes are regressed on SNPs. The CTS has been applied to the bone mineral density data from the Rotterdam Study and proved to work very well even in unbalanced situations. In another article (Sikorska et al: GWAS on your notebook: fast semi-parallel linear and logistic regression for genome-wide association studies. BMC Bioinformatics 2013; 14: 166), we suggested semi-parallel computations, greatly speeding up fitting many linear regressions. Combining CTS with fast linear regression reduces the computation time from several weeks to a few minutes on a single computer. Here, we explore further the properties of the CTS both analytically and by simulations. We investigate the performance of our proposal in comparison with a related but different approach, the two-step procedure. It is analytically shown that for the balanced case, under mild assumptions, the P-value provided by the CTS is the same as from the LMM. For unbalanced data and in realistic situations, simulations show that the CTS method does not inflate the type I error rate and implies only a minimal loss of power. PMID:25712081

[Psychiatric Rehabilitation - From the Linear Continuum Approach Towards Supported Inclusion].

PubMed

Richter, Dirk; Hertig, Res; Hoffmann, Holger

2016-11-01

Background: For many decades, psychiatric rehabilitation in the German-speaking countries is following a conventional linear continuum approach. Methods: Recent developments in important fields related to psychiatric rehabilitation (UN Convention on the Rights of People with Disabilities, theory of rehabilitation, empirical research) are reviewed. Results: Common to all developments in the reviewed fields are the principles of choice, autonomy and social inclusion. These principles contradict the conventional linear continuum approach. Conclusions: The linear continuum approach of psychiatric rehabilitation should be replaced by the "supported inclusion"-approach. © Georg Thieme Verlag KG Stuttgart · New York.

Multiple kernel learning in protein-protein interaction extraction from biomedical literature.

PubMed

Yang, Zhihao; Tang, Nan; Zhang, Xiao; Lin, Hongfei; Li, Yanpeng; Yang, Zhiwei

2011-03-01

Knowledge about protein-protein interactions (PPIs) unveils the molecular mechanisms of biological processes. The volume and content of published biomedical literature on protein interactions is expanding rapidly, making it increasingly difficult for interaction database administrators, responsible for content input and maintenance to detect and manually update protein interaction information. The objective of this work is to develop an effective approach to automatic extraction of PPI information from biomedical literature. We present a weighted multiple kernel learning-based approach for automatic PPI extraction from biomedical literature. The approach combines the following kernels: feature-based, tree, graph and part-of-speech (POS) path. In particular, we extend the shortest path-enclosed tree (SPT) and dependency path tree to capture richer contextual information. Our experimental results show that the combination of SPT and dependency path tree extensions contributes to the improvement of performance by almost 0.7 percentage units in F-score and 2 percentage units in area under the receiver operating characteristics curve (AUC). Combining two or more appropriately weighed individual will further improve the performance. Both on the individual corpus and cross-corpus evaluation our combined kernel can achieve state-of-the-art performance with respect to comparable evaluations, with 64.41% F-score and 88.46% AUC on the AImed corpus. As different kernels calculate the similarity between two sentences from different aspects. Our combined kernel can reduce the risk of missing important features. More specifically, we use a weighted linear combination of individual kernels instead of assigning the same weight to each individual kernel, thus allowing the introduction of each kernel to incrementally contribute to the performance improvement. In addition, SPT and dependency path tree extensions can improve the performance by including richer context information. Copyright © 2010 Elsevier B.V. All rights reserved.

Local hyperspectral data multisharpening based on linear/linear-quadratic nonnegative matrix factorization by integrating lidar data

NASA Astrophysics Data System (ADS)

Benhalouche, Fatima Zohra; Karoui, Moussa Sofiane; Deville, Yannick; Ouamri, Abdelaziz

2015-10-01

In this paper, a new Spectral-Unmixing-based approach, using Nonnegative Matrix Factorization (NMF), is proposed to locally multi-sharpen hyperspectral data by integrating a Digital Surface Model (DSM) obtained from LIDAR data. In this new approach, the nature of the local mixing model is detected by using the local variance of the object elevations. The hyper/multispectral images are explored using small zones. In each zone, the variance of the object elevations is calculated from the DSM data in this zone. This variance is compared to a threshold value and the adequate linear/linearquadratic spectral unmixing technique is used in the considered zone to independently unmix hyperspectral and multispectral data, using an adequate linear/linear-quadratic NMF-based approach. The obtained spectral and spatial information thus respectively extracted from the hyper/multispectral images are then recombined in the considered zone, according to the selected mixing model. Experiments based on synthetic hyper/multispectral data are carried out to evaluate the performance of the proposed multi-sharpening approach and literature linear/linear-quadratic approaches used on the whole hyper/multispectral data. In these experiments, real DSM data are used to generate synthetic data containing linear and linear-quadratic mixed pixel zones. The DSM data are also used for locally detecting the nature of the mixing model in the proposed approach. Globally, the proposed approach yields good spatial and spectral fidelities for the multi-sharpened data and significantly outperforms the used literature methods.

A fresh look at linear ordinary differential equations with constant coefficients. Revisiting the impulsive response method using factorization

NASA Astrophysics Data System (ADS)

Camporesi, Roberto

2016-01-01

We present an approach to the impulsive response method for solving linear constant-coefficient ordinary differential equations of any order based on the factorization of the differential operator. The approach is elementary, we only assume a basic knowledge of calculus and linear algebra. In particular, we avoid the use of distribution theory, as well as of the other more advanced approaches: Laplace transform, linear systems, the general theory of linear equations with variable coefficients and variation of parameters. The approach presented here can be used in a first course on differential equations for science and engineering majors.

Weighted functional linear regression models for gene-based association analysis.

PubMed

Belonogova, Nadezhda M; Svishcheva, Gulnara R; Wilson, James F; Campbell, Harry; Axenovich, Tatiana I

2018-01-01

Functional linear regression models are effectively used in gene-based association analysis of complex traits. These models combine information about individual genetic variants, taking into account their positions and reducing the influence of noise and/or observation errors. To increase the power of methods, where several differently informative components are combined, weights are introduced to give the advantage to more informative components. Allele-specific weights have been introduced to collapsing and kernel-based approaches to gene-based association analysis. Here we have for the first time introduced weights to functional linear regression models adapted for both independent and family samples. Using data simulated on the basis of GAW17 genotypes and weights defined by allele frequencies via the beta distribution, we demonstrated that type I errors correspond to declared values and that increasing the weights of causal variants allows the power of functional linear models to be increased. We applied the new method to real data on blood pressure from the ORCADES sample. Five of the six known genes with P < 0.1 in at least one analysis had lower P values with weighted models. Moreover, we found an association between diastolic blood pressure and the VMP1 gene (P = 8.18×10-6), when we used a weighted functional model. For this gene, the unweighted functional and weighted kernel-based models had P = 0.004 and 0.006, respectively. The new method has been implemented in the program package FREGAT, which is freely available at https://cran.r-project.org/web/packages/FREGAT/index.html.

Simultaneous elastic parameter inversion in 2-D/3-D TTI medium combined later arrival times

NASA Astrophysics Data System (ADS)

Bai, Chao-ying; Wang, Tao; Yang, Shang-bei; Li, Xing-wang; Huang, Guo-jiao

2016-04-01

Traditional traveltime inversion for anisotropic medium is, in general, based on a "weak" assumption in the anisotropic property, which simplifies both the forward part (ray tracing is performed once only) and the inversion part (a linear inversion solver is possible). But for some real applications, a general (both "weak" and "strong") anisotropic medium should be considered. In such cases, one has to develop a ray tracing algorithm to handle with the general (including "strong") anisotropic medium and also to design a non-linear inversion solver for later tomography. Meanwhile, it is constructive to investigate how much the tomographic resolution can be improved by introducing the later arrivals. For this motivation, we incorporated our newly developed ray tracing algorithm (multistage irregular shortest-path method) for general anisotropic media with a non-linear inversion solver (a damped minimum norm, constrained least squares problem with a conjugate gradient approach) to formulate a non-linear inversion solver for anisotropic medium. This anisotropic traveltime inversion procedure is able to combine the later (reflected) arrival times. Both 2-D/3-D synthetic inversion experiments and comparison tests show that (1) the proposed anisotropic traveltime inversion scheme is able to recover the high contrast anomalies and (2) it is possible to improve the tomographic resolution by introducing the later (reflected) arrivals, but not as expected in the isotropic medium, because the different velocity (qP, qSV and qSH) sensitivities (or derivatives) respective to the different elastic parameters are not the same but are also dependent on the inclination angle.

An analysis of hypercritical states in elastic and inelastic systems

NASA Astrophysics Data System (ADS)

Kowalczk, Maciej

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

The feasibility of manual parameter tuning for deformable breast MR image registration from a multi-objective optimization perspective.

PubMed

Pirpinia, Kleopatra; Bosman, Peter A N; Loo, Claudette E; Winter-Warnars, Gonneke; Janssen, Natasja N Y; Scholten, Astrid N; Sonke, Jan-Jakob; van Herk, Marcel; Alderliesten, Tanja

2017-06-23

Deformable image registration is typically formulated as an optimization problem involving a linearly weighted combination of terms that correspond to objectives of interest (e.g. similarity, deformation magnitude). The weights, along with multiple other parameters, need to be manually tuned for each application, a task currently addressed mainly via trial-and-error approaches. Such approaches can only be successful if there is a sensible interplay between parameters, objectives, and desired registration outcome. This, however, is not well established. To study this interplay, we use multi-objective optimization, where multiple solutions exist that represent the optimal trade-offs between the objectives, forming a so-called Pareto front. Here, we focus on weight tuning. To study the space a user has to navigate during manual weight tuning, we randomly sample multiple linear combinations. To understand how these combinations relate to desirability of registration outcome, we associate with each outcome a mean target registration error (TRE) based on expert-defined anatomical landmarks. Further, we employ a multi-objective evolutionary algorithm that optimizes the weight combinations, yielding a Pareto front of solutions, which can be directly navigated by the user. To study how the complexity of manual weight tuning changes depending on the registration problem, we consider an easy problem, prone-to-prone breast MR image registration, and a hard problem, prone-to-supine breast MR image registration. Lastly, we investigate how guidance information as an additional objective influences the prone-to-supine registration outcome. Results show that the interplay between weights, objectives, and registration outcome makes manual weight tuning feasible for the prone-to-prone problem, but very challenging for the harder prone-to-supine problem. Here, patient-specific, multi-objective weight optimization is needed, obtaining a mean TRE of 13.6 mm without guidance information reduced to 7.3 mm with guidance information, but also providing a Pareto front that exhibits an intuitively sensible interplay between weights, objectives, and registration outcome, allowing outcome selection.

The feasibility of manual parameter tuning for deformable breast MR image registration from a multi-objective optimization perspective

NASA Astrophysics Data System (ADS)

Pirpinia, Kleopatra; Bosman, Peter A. N.; E Loo, Claudette; Winter-Warnars, Gonneke; Y Janssen, Natasja N.; Scholten, Astrid N.; Sonke, Jan-Jakob; van Herk, Marcel; Alderliesten, Tanja

2017-07-01

Deformable image registration is typically formulated as an optimization problem involving a linearly weighted combination of terms that correspond to objectives of interest (e.g. similarity, deformation magnitude). The weights, along with multiple other parameters, need to be manually tuned for each application, a task currently addressed mainly via trial-and-error approaches. Such approaches can only be successful if there is a sensible interplay between parameters, objectives, and desired registration outcome. This, however, is not well established. To study this interplay, we use multi-objective optimization, where multiple solutions exist that represent the optimal trade-offs between the objectives, forming a so-called Pareto front. Here, we focus on weight tuning. To study the space a user has to navigate during manual weight tuning, we randomly sample multiple linear combinations. To understand how these combinations relate to desirability of registration outcome, we associate with each outcome a mean target registration error (TRE) based on expert-defined anatomical landmarks. Further, we employ a multi-objective evolutionary algorithm that optimizes the weight combinations, yielding a Pareto front of solutions, which can be directly navigated by the user. To study how the complexity of manual weight tuning changes depending on the registration problem, we consider an easy problem, prone-to-prone breast MR image registration, and a hard problem, prone-to-supine breast MR image registration. Lastly, we investigate how guidance information as an additional objective influences the prone-to-supine registration outcome. Results show that the interplay between weights, objectives, and registration outcome makes manual weight tuning feasible for the prone-to-prone problem, but very challenging for the harder prone-to-supine problem. Here, patient-specific, multi-objective weight optimization is needed, obtaining a mean TRE of 13.6 mm without guidance information reduced to 7.3 mm with guidance information, but also providing a Pareto front that exhibits an intuitively sensible interplay between weights, objectives, and registration outcome, allowing outcome selection.

Rapid Computation of Thermodynamic Properties over Multidimensional Nonbonded Parameter Spaces Using Adaptive Multistate Reweighting.

PubMed

Naden, Levi N; Shirts, Michael R

2016-04-12

We show how thermodynamic properties of molecular models can be computed over a large, multidimensional parameter space by combining multistate reweighting analysis with a linear basis function approach. This approach reduces the computational cost to estimate thermodynamic properties from molecular simulations for over 130,000 tested parameter combinations from over 1000 CPU years to tens of CPU days. This speed increase is achieved primarily by computing the potential energy as a linear combination of basis functions, computed from either modified simulation code or as the difference of energy between two reference states, which can be done without any simulation code modification. The thermodynamic properties are then estimated with the Multistate Bennett Acceptance Ratio (MBAR) as a function of multiple model parameters without the need to define a priori how the states are connected by a pathway. Instead, we adaptively sample a set of points in parameter space to create mutual configuration space overlap. The existence of regions of poor configuration space overlap are detected by analyzing the eigenvalues of the sampled states' overlap matrix. The configuration space overlap to sampled states is monitored alongside the mean and maximum uncertainty to determine convergence, as neither the uncertainty or the configuration space overlap alone is a sufficient metric of convergence. This adaptive sampling scheme is demonstrated by estimating with high precision the solvation free energies of charged particles of Lennard-Jones plus Coulomb functional form with charges between -2 and +2 and generally physical values of σij and ϵij in TIP3P water. We also compute entropy, enthalpy, and radial distribution functions of arbitrary unsampled parameter combinations using only the data from these sampled states and use the estimates of free energies over the entire space to examine the deviation of atomistic simulations from the Born approximation to the solvation free energy.

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

Baker, Kyri; Dall'Anese, Emiliano; Summers, Tyler

This paper outlines a data-driven, distributionally robust approach to solve chance-constrained AC optimal power flow problems in distribution networks. Uncertain forecasts for loads and power generated by photovoltaic (PV) systems are considered, with the goal of minimizing PV curtailment while meeting power flow and voltage regulation constraints. A data- driven approach is utilized to develop a distributionally robust conservative convex approximation of the chance-constraints; particularly, the mean and covariance matrix of the forecast errors are updated online, and leveraged to enforce voltage regulation with predetermined probability via Chebyshev-based bounds. By combining an accurate linear approximation of the AC power flowmore » equations with the distributionally robust chance constraint reformulation, the resulting optimization problem becomes convex and computationally tractable.« less

Nonlinear rovibrational polarization response of water vapor to ultrashort long-wave infrared pulses

NASA Astrophysics Data System (ADS)

Schuh, K.; Rosenow, P.; Kolesik, M.; Wright, E. M.; Koch, S. W.; Moloney, J. V.

2017-10-01

We study the rovibrational polarization response of water vapor using a fully correlated optical Bloch equation approach employing data from the HITRAN database. For a 10 -μ m long-wave infrared pulse the resulting linear response is negative, with a negative nonlinear response at intermediate intensities and a positive value at higher intensities. For a model atmosphere comprised of the electronic response of argon combined with the rovibrational response of water vapor this leads to a weakened positive nonlinear response at intermediate intensities. Propagation simulations using a simplified noncorrelated approach show the resultant reduction in the peak filament intensity sustained during filamentation due to the presence of the water vapor.

Efficient Translation of LTL Formulae into Buchi Automata

NASA Technical Reports Server (NTRS)

Giannakopoulou, Dimitra; Lerda, Flavio

2001-01-01

Model checking is a fully automated technique for checking that a system satisfies a set of required properties. With explicit-state model checkers, properties are typically defined in linear-time temporal logic (LTL), and are translated into B chi automata in order to be checked. This report presents how we have combined and improved existing techniques to obtain an efficient LTL to B chi automata translator. In particular, we optimize the core of existing tableau-based approaches to generate significantly smaller automata. Our approach has been implemented and is being released as part of the Java PathFinder software (JPF), an explicit state model checker under development at the NASA Ames Research Center.

Accuracy of 1H magnetic resonance spectroscopy for quantification of 2-hydroxyglutarate using linear combination and J-difference editing at 9.4T.

PubMed

Neuberger, Ulf; Kickingereder, Philipp; Helluy, Xavier; Fischer, Manuel; Bendszus, Martin; Heiland, Sabine

2017-12-01

Non-invasive detection of 2-hydroxyglutarate (2HG) by magnetic resonance spectroscopy is attractive since it is related to tumor metabolism. Here, we compare the detection accuracy of 2HG in a controlled phantom setting via widely used localized spectroscopy sequences quantified by linear combination of metabolite signals vs. a more complex approach applying a J-difference editing technique at 9.4T. Different phantoms, comprised out of a concentration series of 2HG and overlapping brain metabolites, were measured with an optimized point-resolved-spectroscopy sequence (PRESS) and an in-house developed J-difference editing sequence. The acquired spectra were post-processed with LCModel and a simulated metabolite set (PRESS) or with a quantification formula for J-difference editing. Linear regression analysis demonstrated a high correlation of real 2HG values with those measured with the PRESS method (adjusted R-squared: 0.700, p<0.001) as well as with those measured with the J-difference editing method (adjusted R-squared: 0.908, p<0.001). The regression model with the J-difference editing method however had a significantly higher explanatory value over the regression model with the PRESS method (p<0.0001). Moreover, with J-difference editing 2HG was discernible down to 1mM, whereas with the PRESS method 2HG values were not discernable below 2mM and with higher systematic errors, particularly in phantoms with high concentrations of N-acetyl-asparate (NAA) and glutamate (Glu). In summary, quantification of 2HG with linear combination of metabolite signals shows high systematic errors particularly at low 2HG concentration and high concentration of confounding metabolites such as NAA and Glu. In contrast, J-difference editing offers a more accurate quantification even at low 2HG concentrations, which outweighs the downsides of longer measurement time and more complex postprocessing. Copyright © 2017. Published by Elsevier GmbH.

Linear and Order Statistics Combiners for Pattern Classification

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Ghosh, Joydeep; Lau, Sonie (Technical Monitor)

2001-01-01

Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers. This chapter provides an analytical framework to quantify the improvements in classification results due to combining. The results apply to both linear combiners and order statistics combiners. We first show that to a first order approximation, the error rate obtained over and above the Bayes error rate, is directly proportional to the variance of the actual decision boundaries around the Bayes optimum boundary. Combining classifiers in output space reduces this variance, and hence reduces the 'added' error. If N unbiased classifiers are combined by simple averaging. the added error rate can be reduced by a factor of N if the individual errors in approximating the decision boundaries are uncorrelated. Expressions are then derived for linear combiners which are biased or correlated, and the effect of output correlations on ensemble performance is quantified. For order statistics based non-linear combiners, we derive expressions that indicate how much the median, the maximum and in general the i-th order statistic can improve classifier performance. The analysis presented here facilitates the understanding of the relationships among error rates, classifier boundary distributions, and combining in output space. Experimental results on several public domain data sets are provided to illustrate the benefits of combining and to support the analytical results.

A Systematic Approach to Sensor Selection for Aircraft Engine Health Estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Garg, Sanjay

2009-01-01

A systematic approach for selecting an optimal suite of sensors for on-board aircraft gas turbine engine health estimation is presented. The methodology optimally chooses the engine sensor suite and the model tuning parameter vector to minimize the Kalman filter mean squared estimation error in the engine s health parameters or other unmeasured engine outputs. This technique specifically addresses the underdetermined estimation problem where there are more unknown system health parameters representing degradation than available sensor measurements. This paper presents the theoretical estimation error equations, and describes the optimization approach that is applied to select the sensors and model tuning parameters to minimize these errors. Two different model tuning parameter vector selection approaches are evaluated: the conventional approach of selecting a subset of health parameters to serve as the tuning parameters, and an alternative approach that selects tuning parameters as a linear combination of all health parameters. Results from the application of the technique to an aircraft engine simulation are presented, and compared to those from an alternative sensor selection strategy.

Modelling interactions of acid–base balance and respiratory status in the toxicity of metal mixtures in the American oyster Crassostrea virginica

PubMed Central

Macey, Brett M.; Jenny, Matthew J.; Williams, Heidi R.; Thibodeaux, Lindy K.; Beal, Marion; Almeida, Jonas S.; Cunningham, Charles; Mancia, Annalaura; Warr, Gregory W.; Burge, Erin J.; Holland, A. Fred; Gross, Paul S.; Hikima, Sonomi; Burnett, Karen G.; Burnett, Louis; Chapman, Robert W.

2010-01-01

Heavy metals, such as copper, zinc and cadmium, represent some of the most common and serious pollutants in coastal estuaries. In the present study, we used a combination of linear and artificial neural network (ANN) modelling to detect and explore interactions among low-dose mixtures of these heavy metals and their impacts on fundamental physiological processes in tissues of the Eastern oyster, Crassostrea virginica. Animals were exposed to Cd (0.001–0.400 µM), Zn (0.001–3.059 µM) or Cu (0.002–0.787 µM), either alone or in combination for 1 to 27 days. We measured indicators of acid–base balance (hemolymph pH and total CO2), gas exchange (Po2), immunocompetence (total hemocyte counts, numbers of invasive bacteria), antioxidant status (glutathione, GSH), oxidative damage (lipid peroxidation; LPx), and metal accumulation in the gill and the hepatopancreas. Linear analysis showed that oxidative membrane damage from tissue accumulation of environmental metals was correlated with impaired acid–base balance in oysters. ANN analysis revealed interactions of metals with hemolymph acid–base chemistry in predicting oxidative damage that were not evident from linear analyses. These results highlight the usefulness of machine learning approaches, such as ANNs, for improving our ability to recognize and understand the effects of subacute exposure to contaminant mixtures. PMID:19958840

A sparse representation-based approach for copy-move image forgery detection in smooth regions

NASA Astrophysics Data System (ADS)

Abdessamad, Jalila; ElAdel, Asma; Zaied, Mourad

2017-03-01

Copy-move image forgery is the act of cloning a restricted region in the image and pasting it once or multiple times within that same image. This procedure intends to cover a certain feature, probably a person or an object, in the processed image or emphasize it through duplication. Consequences of this malicious operation can be unexpectedly harmful. Hence, the present paper proposes a new approach that automatically detects Copy-move Forgery (CMF). In particular, this work broaches a widely common open issue in CMF research literature that is detecting CMF within smooth areas. Indeed, the proposed approach represents the image blocks as a sparse linear combination of pre-learned bases (a mixture of texture and color-wise small patches) which allows a robust description of smooth patches. The reported experimental results demonstrate the effectiveness of the proposed approach in identifying the forged regions in CM attacks.

The LET Procedure for Prosthetic Myocontrol: Towards Multi-DOF Control Using Single-DOF Activations.

PubMed

Nowak, Markus; Castellini, Claudio

2016-01-01

Simultaneous and proportional myocontrol of dexterous hand prostheses is to a large extent still an open problem. With the advent of commercially and clinically available multi-fingered hand prostheses there are now more independent degrees of freedom (DOFs) in prostheses than can be effectively controlled using surface electromyography (sEMG), the current standard human-machine interface for hand amputees. In particular, it is uncertain, whether several DOFs can be controlled simultaneously and proportionally by exclusively calibrating the intended activation of single DOFs. The problem is currently solved by training on all required combinations. However, as the number of available DOFs grows, this approach becomes overly long and poses a high cognitive burden on the subject. In this paper we present a novel approach to overcome this problem. Multi-DOF activations are artificially modelled from single-DOF ones using a simple linear combination of sEMG signals, which are then added to the training set. This procedure, which we named LET (Linearly Enhanced Training), provides an augmented data set to any machine-learning-based intent detection system. In two experiments involving intact subjects, one offline and one online, we trained a standard machine learning approach using the full data set containing single- and multi-DOF activations as well as using the LET-augmented data set in order to evaluate the performance of the LET procedure. The results indicate that the machine trained on the latter data set obtains worse results in the offline experiment compared to the full data set. However, the online implementation enables the user to perform multi-DOF tasks with almost the same precision as single-DOF tasks without the need of explicitly training multi-DOF activations. Moreover, the parameters involved in the system are statistically uniform across subjects.

Landscape characteristics influencing the genetic structure of greater sage-grouse within the stronghold of their range: a holistic modeling approach

USGS Publications Warehouse

Row, Jeff R; Oyler-McCance, Sara J.; Fike, Jennifer; O'Donnell, Michael; Doherty, Kevin E.; Aldridge, Cameron L.; Bowen, Zachary H.; Fedy, Brad C.

2015-01-01

Given the significance of animal dispersal to population dynamics and geographic variability, understanding how dispersal is impacted by landscape patterns has major ecological and conservation importance. Speaking to the importance of dispersal, the use of linear mixed models to compare genetic differentiation with pairwise resistance derived from landscape resistance surfaces has presented new opportunities to disentangle the menagerie of factors behind effective dispersal across a given landscape. Here, we combine these approaches with novel resistance surface parameterization to determine how the distribution of high- and low-quality seasonal habitat and individual landscape components shape patterns of gene flow for the greater sage-grouse (Centrocercus urophasianus) across Wyoming. We found that pairwise resistance derived from the distribution of low-quality nesting and winter, but not summer, seasonal habitat had the strongest correlation with genetic differentiation. Although the patterns were not as strong as with habitat distribution, multivariate models with sagebrush cover and landscape ruggedness or forest cover and ruggedness similarly had a much stronger fit with genetic differentiation than an undifferentiated landscape. In most cases, landscape resistance surfaces transformed with 17.33-km-diameter moving windows were preferred, suggesting small-scale differences in habitat were unimportant at this large spatial extent. Despite the emergence of these overall patterns, there were differences in the selection of top models depending on the model selection criteria, suggesting research into the most appropriate criteria for landscape genetics is required. Overall, our results highlight the importance of differences in seasonal habitat preferences to patterns of gene flow and suggest the combination of habitat suitability modeling and linear mixed models with our resistance parameterization is a powerful approach to discerning the effects of landscape on gene flow.

Multi-disease analysis of maternal antibody decay using non-linear mixed models accounting for censoring.

PubMed

Goeyvaerts, Nele; Leuridan, Elke; Faes, Christel; Van Damme, Pierre; Hens, Niel

2015-09-10

Biomedical studies often generate repeated measures of multiple outcomes on a set of subjects. It may be of interest to develop a biologically intuitive model for the joint evolution of these outcomes while assessing inter-subject heterogeneity. Even though it is common for biological processes to entail non-linear relationships, examples of multivariate non-linear mixed models (MNMMs) are still fairly rare. We contribute to this area by jointly analyzing the maternal antibody decay for measles, mumps, rubella, and varicella, allowing for a different non-linear decay model for each infectious disease. We present a general modeling framework to analyze multivariate non-linear longitudinal profiles subject to censoring, by combining multivariate random effects, non-linear growth and Tobit regression. We explore the hypothesis of a common infant-specific mechanism underlying maternal immunity using a pairwise correlated random-effects approach and evaluating different correlation matrix structures. The implied marginal correlation between maternal antibody levels is estimated using simulations. The mean duration of passive immunity was less than 4 months for all diseases with substantial heterogeneity between infants. The maternal antibody levels against rubella and varicella were found to be positively correlated, while little to no correlation could be inferred for the other disease pairs. For some pairs, computational issues occurred with increasing correlation matrix complexity, which underlines the importance of further developing estimation methods for MNMMs. Copyright © 2015 John Wiley & Sons, Ltd.

Feedback control of combustion instabilities from within limit cycle oscillations using H∞ loop-shaping and the ν-gap metric

PubMed Central

Morgans, Aimee S.

2016-01-01

Combustion instabilities arise owing to a two-way coupling between acoustic waves and unsteady heat release. Oscillation amplitudes successively grow, until nonlinear effects cause saturation into limit cycle oscillations. Feedback control, in which an actuator modifies some combustor input in response to a sensor measurement, can suppress combustion instabilities. Linear feedback controllers are typically designed, using linear combustor models. However, when activated from within limit cycle, the linear model is invalid, and such controllers are not guaranteed to stabilize. This work develops a feedback control strategy guaranteed to stabilize from within limit cycle oscillations. A low-order model of a simple combustor, exhibiting the essential features of more complex systems, is presented. Linear plane acoustic wave modelling is combined with a weakly nonlinear describing function for the flame. The latter is determined numerically using a level set approach. Its implication is that the open-loop transfer function (OLTF) needed for controller design varies with oscillation level. The difference between the mean and the rest of the OLTFs is characterized using the ν-gap metric, providing the minimum required ‘robustness margin’ for an H∞ loop-shaping controller. Such controllers are designed and achieve stability both for linear fluctuations and from within limit cycle oscillations. PMID:27493558

Combinatorial therapy discovery using mixed integer linear programming.

PubMed

Pang, Kaifang; Wan, Ying-Wooi; Choi, William T; Donehower, Lawrence A; Sun, Jingchun; Pant, Dhruv; Liu, Zhandong

2014-05-15

Combinatorial therapies play increasingly important roles in combating complex diseases. Owing to the huge cost associated with experimental methods in identifying optimal drug combinations, computational approaches can provide a guide to limit the search space and reduce cost. However, few computational approaches have been developed for this purpose, and thus there is a great need of new algorithms for drug combination prediction. Here we proposed to formulate the optimal combinatorial therapy problem into two complementary mathematical algorithms, Balanced Target Set Cover (BTSC) and Minimum Off-Target Set Cover (MOTSC). Given a disease gene set, BTSC seeks a balanced solution that maximizes the coverage on the disease genes and minimizes the off-target hits at the same time. MOTSC seeks a full coverage on the disease gene set while minimizing the off-target set. Through simulation, both BTSC and MOTSC demonstrated a much faster running time over exhaustive search with the same accuracy. When applied to real disease gene sets, our algorithms not only identified known drug combinations, but also predicted novel drug combinations that are worth further testing. In addition, we developed a web-based tool to allow users to iteratively search for optimal drug combinations given a user-defined gene set. Our tool is freely available for noncommercial use at http://www.drug.liuzlab.org/. zhandong.liu@bcm.edu Supplementary data are available at Bioinformatics online.

Behavioral approach and avoidance in schizophrenia: an evaluation of motivational profiles.

PubMed

Felice Reddy, L; Green, Michael F; Rizzo, Shemra; Sugar, Catherine A; Blanchard, Jack J; Gur, Raquel E; Kring, Ann M; Horan, William P

2014-10-01

Schizophrenia is associated with motivational deficits that interfere with a wide range of goal directed activities. Despite their clinical importance, our current understanding of these motivational impairments is limited. Furthermore, different types of motivational problems are commonly seen among individuals within the broad diagnosis of schizophrenia. The goal of the current study was to examine whether clinically meaningful subgroups could be identified based on approach and avoidance motivational tendencies. We measured these tendencies in 151 individuals with schizophrenia. Although prior studies demonstrate elevated BIS sensitivity in schizophrenia at the overall group level, none have explored various combinations of BIS/BAS sensitivities within this disorder. Cluster analyses yielded five subgroups with different combinations of low, moderate, or high BIS and BAS. The subgroups had interpretable differences in clinically rated negative symptoms and self-reported anhedonia/socio-emotional attitudes, which were not detectable with the more commonly used linear BIS/BAS scores. Two of the subgroups had significantly elevated negative symptoms but different approach/avoidance profiles: one was characterized by markedly low BIS, low BAS and an overall lack of social approach motivation; the other had markedly high BIS but moderate BAS and elevated social avoidance motivation. The two subgroups with relatively good clinical functioning showed patterns of BAS greater than BIS. Our findings indicate that there are distinct motivational pathways that can lead to asociality in schizophrenia and highlight the value of considering profiles based on combined patterns of BIS and BAS in schizophrenia. Published by Elsevier B.V.

Numerical solution of the general coupled nonlinear Schrödinger equations on unbounded domains.

PubMed

Li, Hongwei; Guo, Yue

2017-12-01

The numerical solution of the general coupled nonlinear Schrödinger equations on unbounded domains is considered by applying the artificial boundary method in this paper. In order to design the local absorbing boundary conditions for the coupled nonlinear Schrödinger equations, we generalize the unified approach previously proposed [J. Zhang et al., Phys. Rev. E 78, 026709 (2008)PLEEE81539-375510.1103/PhysRevE.78.026709]. Based on the methodology underlying the unified approach, the original problem is split into two parts, linear and nonlinear terms, and we then achieve a one-way operator to approximate the linear term to make the wave out-going, and finally we combine the one-way operator with the nonlinear term to derive the local absorbing boundary conditions. Then we reduce the original problem into an initial boundary value problem on the bounded domain, which can be solved by the finite difference method. The stability of the reduced problem is also analyzed by introducing some auxiliary variables. Ample numerical examples are presented to verify the accuracy and effectiveness of our proposed method.

Finding Frequent Closed Itemsets in Sliding Window in Linear Time

NASA Astrophysics Data System (ADS)

Chen, Junbo; Zhou, Bo; Chen, Lu; Wang, Xinyu; Ding, Yiqun

One of the most well-studied problems in data mining is computing the collection of frequent itemsets in large transactional databases. Since the introduction of the famous Apriori algorithm [14], many others have been proposed to find the frequent itemsets. Among such algorithms, the approach of mining closed itemsets has raised much interest in data mining community. The algorithms taking this approach include TITANIC [8], CLOSET+[6], DCI-Closed [4], FCI-Stream [3], GC-Tree [15], TGC-Tree [16] etc. Among these algorithms, FCI-Stream, GC-Tree and TGC-Tree are online algorithms work under sliding window environments. By the performance evaluation in [16], GC-Tree [15] is the fastest one. In this paper, an improved algorithm based on GC-Tree is proposed, the computational complexity of which is proved to be a linear combination of the average transaction size and the average closed itemset size. The algorithm is based on the essential theorem presented in Sect. 4.2. Empirically, the new algorithm is several orders of magnitude faster than the state of art algorithm, GC-Tree.

Electrochemical approach for acute myocardial infarction diagnosis based on direct antibodies-free analysis of human blood plasma.

PubMed

Suprun, Elena V; Saveliev, Anatoly A; Evtugyn, Gennady A; Lisitsa, Alexander V; Bulko, Tatiana V; Shumyantseva, Victoria V; Archakov, Alexander I

2012-03-15

A novel direct antibodies-free electrochemical approach for acute myocardial infarction (AMI) diagnosis has been developed. For this purpose, a combination of the electrochemical assay of plasma samples with chemometrics was proposed. Screen printed carbon electrodes modified with didodecyldimethylammonium bromide were used for plasma charactrerization by cyclic (CV) and square wave voltammetry and square wave (SWV) voltammetry. It was shown that the cathodic peak in voltammograms at about -250 mV vs. Ag/AgCl can be associated with AMI. In parallel tests, cardiac myoglobin and troponin I, the AMI biomarkers, were determined in each sample by RAMP immunoassay. The applicability of the electrochemical testing for AMI diagnostics was confirmed by statistical methods: generalized linear model (GLM), linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA), artificial neural net (multi-layer perception, MLP), and support vector machine (SVM), all of which were created to obtain the "True-False" distribution prediction where "True" and "False" are, respectively, positive and negative decision about an illness event. Copyright © 2011 Elsevier B.V. All rights reserved.

Evaluating the environmental fate of pharmaceuticals using a level III model based on poly-parameter linear free energy relationships.

PubMed

Zukowska, Barbara; Breivik, Knut; Wania, Frank

2006-04-15

We recently proposed how to expand the applicability of multimedia models towards polar organic chemicals by expressing environmental phase partitioning with the help of poly-parameter linear free energy relationships (PP-LFERs). Here we elaborate on this approach by applying it to three pharmaceutical substances. A PP-LFER-based version of a Level III fugacity model calculates overall persistence, concentrations and intermedia fluxes of polar and non-polar organic chemicals between air, water, soil and sediments at steady-state. Illustrative modeling results for the pharmaceuticals within a defined coastal region are presented and discussed. The model results are highly sensitive to the degradation rate in water and the equilibrium partitioning between organic carbon and water, suggesting that an accurate description of this particular partitioning equilibrium is essential in order to obtain reliable predictions of environmental fate. The PP-LFER based modeling approach furthermore illustrates that the greatest mobility in aqueous phases may be experienced by pharmaceuticals that combines a small molecular size with strong H-acceptor properties.

Analysis of the unusual wavelength dependence of the first hyperpolarizability of porphyrin derivatives

NASA Astrophysics Data System (ADS)

De Mey, K.; Clays, K.; Therien, Michael J.; Beratan, David N.; Asselberghs, Inge

2010-08-01

Successfully predicting the frequency dispersion of electronic hyperpolarizabilities is an unresolved challenge in materials science and electronic structure theory. It has been shown1 that the generalized Thomas-Kuhn sum rules combined with linear absorption data and measured hyperpolarizabilities at one or two frequencies, may be used to predict the entire frequency-dependent electronic hyperpolarizability spectrum. This treatment includes two- and threelevel contributions that arise from the lowest two or three excited state manifolds, enabling us to describe the unusual observed frequency dispersion of the dynamic hyperpolarizability in high oscillator strength M-PZn chromophores, where (porphinato)zinc(II) (PZn) and metal(II)polypyridyl (M) units are connected via an ethyne unit that aligns the high oscillator strength transition dipoles of these components in a head-to-tail arrangement. Importantly, this approach provides a quantitative scheme to use linear optical absorption spectra and very few individual hyperpolarizability values to predict the entire frequency-dependent nonlinear optical response. In addition we provide here experimental dynamic hyperpolarizability values determined by hyper-Rayleigh scattering that underscore the validity of our approach.

Technical note: Combining quantile forecasts and predictive distributions of streamflows

NASA Astrophysics Data System (ADS)

Bogner, Konrad; Liechti, Katharina; Zappa, Massimiliano

2017-11-01

The enhanced availability of many different hydro-meteorological modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future streamflow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple streamflow forecast information will be aggregated based on several different predictive distributions, and quantile forecasts. For this combination the Bayesian model averaging (BMA) approach, the non-homogeneous Gaussian regression (NGR), also known as the ensemble model output statistic (EMOS) techniques, and a novel method called Beta-transformed linear pooling (BLP) will be applied. By the help of the quantile score (QS) and the continuous ranked probability score (CRPS), the combination results for the Sihl River in Switzerland with about 5 years of forecast data will be compared and the differences between the raw and optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resource management.

Ultrafast Fiber Bragg Grating Interrogation for Sensing in Detonation and Shock Wave Experiments.

PubMed

Rodriguez, George; Gilbertson, Steve M

2017-01-27

Chirped fiber Bragg grating (CFBG) sensors coupled to high speed interrogation systems are described as robust diagnostic approaches to monitoring shock wave and detonation front propagation tracking events for use in high energy density shock physics applications. Taking advantage of the linear distributed spatial encoding of the spectral band in single-mode CFBGs, embedded fiber systems and associated photonic interrogation methodologies are shown as an effective approach to sensing shock and detonation-driven loading processes along the CFBG length. Two approaches, one that detects spectral changes in the integrated spectrum of the CFBG and another coherent pulse interrogation approach that fully resolves its spectral response, shows that 100-MHz-1-GHz interrogation rates are possible with spatial resolution along the CFBG in the 50 µm to sub-millimeter range depending on the combination of CFBG parameters (i.e., length, chirp rate, spectrum) and interrogator design specifics. Results from several dynamic tests are used to demonstrate the performance of these high speed systems for shock and detonation propagation tracking under strong and weak shock pressure loading: (1) linear detonation front tracking in the plastic bonded explosive (PBX) PBX-9501; (2) tracking of radial decaying shock with crossover to non-destructive CFBG response; (3) shock wave tracking along an aluminum cylinder wall under weak loading accompanied by dynamic strain effects in the CFBG sensor.

Ultrafast Fiber Bragg Grating Interrogation for Sensing in Detonation and Shock Wave Experiments

PubMed Central

Rodriguez, George; Gilbertson, Steve M.

2017-01-01

Chirped fiber Bragg grating (CFBG) sensors coupled to high speed interrogation systems are described as robust diagnostic approaches to monitoring shock wave and detonation front propagation tracking events for use in high energy density shock physics applications. Taking advantage of the linear distributed spatial encoding of the spectral band in single-mode CFBGs, embedded fiber systems and associated photonic interrogation methodologies are shown as an effective approach to sensing shock and detonation-driven loading processes along the CFBG length. Two approaches, one that detects spectral changes in the integrated spectrum of the CFBG and another coherent pulse interrogation approach that fully resolves its spectral response, shows that 100-MHz–1-GHz interrogation rates are possible with spatial resolution along the CFBG in the 50 μm to sub-millimeter range depending on the combination of CFBG parameters (i.e., length, chirp rate, spectrum) and interrogator design specifics. Results from several dynamic tests are used to demonstrate the performance of these high speed systems for shock and detonation propagation tracking under strong and weak shock pressure loading: (1) linear detonation front tracking in the plastic bonded explosive (PBX) PBX-9501; (2) tracking of radial decaying shock with crossover to non-destructive CFBG response; (3) shock wave tracking along an aluminum cylinder wall under weak loading accompanied by dynamic strain effects in the CFBG sensor. PMID:28134819

Ultrafast Fiber Bragg Grating Interrogation for Sensing in Detonation and Shock Wave Experiments

DOE PAGES

Rodriguez, George; Gilbertson, Steve Michael

2017-01-27

Chirped fiber Bragg grating (CFBG) sensors coupled to high speed interrogation systems are described as robust diagnostic approaches to monitoring shock wave and detonation front propagation tracking events for use in high energy density shock physics applications. Taking advantage of the linear distributed spatial encoding of the spectral band in single-mode CFBGs, embedded fiber systems and associated photonic interrogation methodologies are shown as an effective approach to sensing shock and detonation-driven loading processes along the CFBG length. Two approaches, one that detects spectral changes in the integrated spectrum of the CFBG and another coherent pulse interrogation approach that fully resolvesmore » its spectral response, shows that 100-MHz–1-GHz interrogation rates are possible with spatial resolution along the CFBG in the 50 µm to sub-millimeter range depending on the combination of CFBG parameters (i.e., length, chirp rate, spectrum) and interrogator design specifics. In conclusion, results from several dynamic tests are used to demonstrate the performance of these high speed systems for shock and detonation propagation tracking under strong and weak shock pressure loading: (1) linear detonation front tracking in the plastic bonded explosive (PBX) PBX-9501; (2) tracking of radial decaying shock with crossover to non-destructive CFBG response; (3) shock wave tracking along an aluminum cylinder wall under weak loading accompanied by dynamic strain effects in the CFBG sensor.« less

Learning accurate and interpretable models based on regularized random forests regression

PubMed Central

2014-01-01

Background Many biology related research works combine data from multiple sources in an effort to understand the underlying problems. It is important to find and interpret the most important information from these sources. Thus it will be beneficial to have an effective algorithm that can simultaneously extract decision rules and select critical features for good interpretation while preserving the prediction performance. Methods In this study, we focus on regression problems for biological data where target outcomes are continuous. In general, models constructed from linear regression approaches are relatively easy to interpret. However, many practical biological applications are nonlinear in essence where we can hardly find a direct linear relationship between input and output. Nonlinear regression techniques can reveal nonlinear relationship of data, but are generally hard for human to interpret. We propose a rule based regression algorithm that uses 1-norm regularized random forests. The proposed approach simultaneously extracts a small number of rules from generated random forests and eliminates unimportant features. Results We tested the approach on some biological data sets. The proposed approach is able to construct a significantly smaller set of regression rules using a subset of attributes while achieving prediction performance comparable to that of random forests regression. Conclusion It demonstrates high potential in aiding prediction and interpretation of nonlinear relationships of the subject being studied. PMID:25350120

Aeroelastic System Development Using Proper Orthogonal Decomposition and Volterra Theory

NASA Technical Reports Server (NTRS)

Lucia, David J.; Beran, Philip S.; Silva, Walter A.

2003-01-01

This research combines Volterra theory and proper orthogonal decomposition (POD) into a hybrid methodology for reduced-order modeling of aeroelastic systems. The out-come of the method is a set of linear ordinary differential equations (ODEs) describing the modal amplitudes associated with both the structural modes and the POD basis functions for the uid. For this research, the structural modes are sine waves of varying frequency, and the Volterra-POD approach is applied to the fluid dynamics equations. The structural modes are treated as forcing terms which are impulsed as part of the uid model realization. Using this approach, structural and uid operators are coupled into a single aeroelastic operator. This coupling converts a free boundary uid problem into an initial value problem, while preserving the parameter (or parameters) of interest for sensitivity analysis. The approach is applied to an elastic panel in supersonic cross ow. The hybrid Volterra-POD approach provides a low-order uid model in state-space form. The linear uid model is tightly coupled with a nonlinear panel model using an implicit integration scheme. The resulting aeroelastic model provides correct limit-cycle oscillation prediction over a wide range of panel dynamic pressure values. Time integration of the reduced-order aeroelastic model is four orders of magnitude faster than the high-order solution procedure developed for this research using traditional uid and structural solvers.

New Approach To Hour-By-Hour Weather Forecast

NASA Astrophysics Data System (ADS)

Liao, Q. Q.; Wang, B.

2017-12-01

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

Quadratic correlation filters for optical correlators

NASA Astrophysics Data System (ADS)

Mahalanobis, Abhijit; Muise, Robert R.; Vijaya Kumar, Bhagavatula V. K.

2003-08-01

Linear correlation filters have been implemented in optical correlators and successfully used for a variety of applications. The output of an optical correlator is usually sensed using a square law device (such as a CCD array) which forces the output to be the squared magnitude of the desired correlation. It is however not a traditional practice to factor the effect of the square-law detector in the design of the linear correlation filters. In fact, the input-output relationship of an optical correlator is more accurately modeled as a quadratic operation than a linear operation. Quadratic correlation filters (QCFs) operate directly on the image data without the need for feature extraction or segmentation. In this sense, the QCFs retain the main advantages of conventional linear correlation filters while offering significant improvements in other respects. Not only is more processing required to detect peaks in the outputs of multiple linear filters, but choosing a winner among them is an error prone task. In contrast, all channels in a QCF work together to optimize the same performance metric and produce a combined output that leads to considerable simplification of the post-processing. In this paper, we propose a novel approach to the design of quadratic correlation based on the Fukunaga Koontz transform. Although quadratic filters are known to be optimum when the data is Gaussian, it is expected that they will perform as well as or better than linear filters in general. Preliminary performance results are provided that show that quadratic correlation filters perform better than their linear counterparts.

In-depth investigation of enzymatic hydrolysis of biomass wastes based on three major components: Cellulose, hemicellulose and lignin.

PubMed

Lin, Lili; Yan, Rong; Liu, Yongqiang; Jiang, Wenju

2010-11-01

The artificial biomass based on three biomass components (cellulose, hemicellulose and lignin) were developed on the basis of a simplex-lattice approach. Together with a natural biomass sample, they were employed in enzymatic hydrolysis researches. Different enzyme combines of two commercial enzymes (ACCELLERASE 1500 and OPTIMASH BG) showed a potential to hydrolyze hemicellulose completely. Negligible interactions among the three components were observed, and the used enzyme ACCELLERASE 1500 was proven to be weak lignin-binding. On this basis, a multiple linear-regression equation was established for predicting the reducing sugar yield based on the component proportions in a biomass. The hemicellulose and cellulose in a biomass sample were found to have different contributions in staged hydrolysis at different time periods. Furthermore, the hydrolysis of rice straw was conducted to validate the computation approach through considerations of alkaline solution pretreatment and combined enzymes function, so as to understand better the nature of biomass hydrolysis, from the aspect of three biomass components.

Fully integrated aerodynamic/dynamic optimization of helicopter rotor blades

NASA Technical Reports Server (NTRS)

Walsh, Joanne L.; Lamarsh, William J., II; Adelman, Howard M.

1992-01-01

This paper describes a fully integrated aerodynamic/dynamic optimization procedure for helicopter rotor blades. The procedure combines performance and dynamics analyses with a general purpose optimizer. The procedure minimizes a linear combination of power required (in hover, forward flight, and maneuver) and vibratory hub shear. The design variables include pretwist, taper initiation, taper ratio, root chord, blade stiffnesses, tuning masses, and tuning mass locations. Aerodynamic constraints consist of limits on power required in hover, forward flight and maneuver; airfoil section stall; drag divergence Mach number; minimum tip chord; and trim. Dynamic constraints are on frequencies, minimum autorotational inertia, and maximum blade weight. The procedure is demonstrated for two cases. In the first case the objective function involves power required (in hover, forward flight, and maneuver) and dynamics. The second case involves only hover power and dynamics. The designs from the integrated procedure are compared with designs from a sequential optimization approach in which the blade is first optimized for performance and then for dynamics. In both cases, the integrated approach is superior.

Fully integrated aerodynamic/dynamic optimization of helicopter rotor blades

NASA Technical Reports Server (NTRS)

Walsh, Joanne L.; Lamarsh, William J., II; Adelman, Howard M.

1992-01-01

A fully integrated aerodynamic/dynamic optimization procedure is described for helicopter rotor blades. The procedure combines performance and dynamic analyses with a general purpose optimizer. The procedure minimizes a linear combination of power required (in hover, forward flight, and maneuver) and vibratory hub shear. The design variables include pretwist, taper initiation, taper ratio, root chord, blade stiffnesses, tuning masses, and tuning mass locations. Aerodynamic constraints consist of limits on power required in hover, forward flight and maneuvers; airfoil section stall; drag divergence Mach number; minimum tip chord; and trim. Dynamic constraints are on frequencies, minimum autorotational inertia, and maximum blade weight. The procedure is demonstrated for two cases. In the first case, the objective function involves power required (in hover, forward flight and maneuver) and dynamics. The second case involves only hover power and dynamics. The designs from the integrated procedure are compared with designs from a sequential optimization approach in which the blade is first optimized for performance and then for dynamics. In both cases, the integrated approach is superior.

Quantiles for Finite Mixtures of Normal Distributions

ERIC Educational Resources Information Center

Rahman, Mezbahur; Rahman, Rumanur; Pearson, Larry M.

2006-01-01

Quantiles for finite mixtures of normal distributions are computed. The difference between a linear combination of independent normal random variables and a linear combination of independent normal densities is emphasized. (Contains 3 tables and 1 figure.)

Prediction of high-dimensional states subject to respiratory motion: a manifold learning approach

NASA Astrophysics Data System (ADS)

Liu, Wenyang; Sawant, Amit; Ruan, Dan

2016-07-01

The development of high-dimensional imaging systems in image-guided radiotherapy provides important pathways to the ultimate goal of real-time full volumetric motion monitoring. Effective motion management during radiation treatment usually requires prediction to account for system latency and extra signal/image processing time. It is challenging to predict high-dimensional respiratory motion due to the complexity of the motion pattern combined with the curse of dimensionality. Linear dimension reduction methods such as PCA have been used to construct a linear subspace from the high-dimensional data, followed by efficient predictions on the lower-dimensional subspace. In this study, we extend such rationale to a more general manifold and propose a framework for high-dimensional motion prediction with manifold learning, which allows one to learn more descriptive features compared to linear methods with comparable dimensions. Specifically, a kernel PCA is used to construct a proper low-dimensional feature manifold, where accurate and efficient prediction can be performed. A fixed-point iterative pre-image estimation method is used to recover the predicted value in the original state space. We evaluated and compared the proposed method with a PCA-based approach on level-set surfaces reconstructed from point clouds captured by a 3D photogrammetry system. The prediction accuracy was evaluated in terms of root-mean-squared-error. Our proposed method achieved consistent higher prediction accuracy (sub-millimeter) for both 200 ms and 600 ms lookahead lengths compared to the PCA-based approach, and the performance gain was statistically significant.

Development and validation of a general approach to predict and quantify the synergism of anti-cancer drugs using experimental design and artificial neural networks.

PubMed

Pivetta, Tiziana; Isaia, Francesco; Trudu, Federica; Pani, Alessandra; Manca, Matteo; Perra, Daniela; Amato, Filippo; Havel, Josef

2013-10-15

The combination of two or more drugs using multidrug mixtures is a trend in the treatment of cancer. The goal is to search for a synergistic effect and thereby reduce the required dose and inhibit the development of resistance. An advanced model-free approach for data exploration and analysis, based on artificial neural networks (ANN) and experimental design is proposed to predict and quantify the synergism of drugs. The proposed method non-linearly correlates the concentrations of drugs with the cytotoxicity of the mixture, providing the possibility of choosing the optimal drug combination that gives the maximum synergism. The use of ANN allows for the prediction of the cytotoxicity of each combination of drugs in the chosen concentration interval. The method was validated by preparing and experimentally testing the combinations with the predicted highest synergistic effect. In all cases, the data predicted by the network were experimentally confirmed. The method was applied to several binary mixtures of cisplatin and [Cu(1,10-orthophenanthroline)2(H2O)](ClO4)2, Cu(1,10-orthophenanthroline)(H2O)2(ClO4)2 or [Cu(1,10-orthophenanthroline)2(imidazolidine-2-thione)](ClO4)2. The cytotoxicity of the two drugs, alone and in combination, was determined against human acute T-lymphoblastic leukemia cells (CCRF-CEM). For all systems, a synergistic effect was found for selected combinations. © 2013 Elsevier B.V. All rights reserved.

First-order symmetry-adapted perturbation theory for multiplet splittings.

PubMed

Patkowski, Konrad; Żuchowski, Piotr S; Smith, Daniel G A

2018-04-28

We present a symmetry-adapted perturbation theory (SAPT) for the interaction of two high-spin open-shell molecules (described by their restricted open-shell Hartree-Fock determinants) resulting in low-spin states of the complex. The previously available SAPT formalisms, except for some system-specific studies for few-electron complexes, were restricted to the high-spin state of the interacting system. Thus, the new approach provides, for the first time, a SAPT-based estimate of the splittings between different spin states of the complex. We have derived and implemented the lowest-order SAPT term responsible for these splittings, that is, the first-order exchange energy. We show that within the so-called S 2 approximation commonly used in SAPT (neglecting effects that vanish as fourth or higher powers of intermolecular overlap integrals), the first-order exchange energies for all multiplets are linear combinations of two matrix elements: a diagonal exchange term that determines the spin-averaged effect and a spin-flip term responsible for the splittings between the states. The numerical factors in this linear combination are determined solely by the Clebsch-Gordan coefficients: accordingly, the S 2 approximation implies a Heisenberg Hamiltonian picture with a single coupling strength parameter determining all the splittings. The new approach is cast into both molecular-orbital and atomic-orbital expressions: the latter enable an efficient density-fitted implementation. We test the newly developed formalism on several open-shell complexes ranging from diatomic systems (Li⋯H, Mn⋯Mn, …) to the phenalenyl dimer.

First-order symmetry-adapted perturbation theory for multiplet splittings

NASA Astrophysics Data System (ADS)

Patkowski, Konrad; Żuchowski, Piotr S.; Smith, Daniel G. A.

2018-04-01

We present a symmetry-adapted perturbation theory (SAPT) for the interaction of two high-spin open-shell molecules (described by their restricted open-shell Hartree-Fock determinants) resulting in low-spin states of the complex. The previously available SAPT formalisms, except for some system-specific studies for few-electron complexes, were restricted to the high-spin state of the interacting system. Thus, the new approach provides, for the first time, a SAPT-based estimate of the splittings between different spin states of the complex. We have derived and implemented the lowest-order SAPT term responsible for these splittings, that is, the first-order exchange energy. We show that within the so-called S2 approximation commonly used in SAPT (neglecting effects that vanish as fourth or higher powers of intermolecular overlap integrals), the first-order exchange energies for all multiplets are linear combinations of two matrix elements: a diagonal exchange term that determines the spin-averaged effect and a spin-flip term responsible for the splittings between the states. The numerical factors in this linear combination are determined solely by the Clebsch-Gordan coefficients: accordingly, the S2 approximation implies a Heisenberg Hamiltonian picture with a single coupling strength parameter determining all the splittings. The new approach is cast into both molecular-orbital and atomic-orbital expressions: the latter enable an efficient density-fitted implementation. We test the newly developed formalism on several open-shell complexes ranging from diatomic systems (Li⋯H, Mn⋯Mn, …) to the phenalenyl dimer.

Techniques for Single System Integration of Elastic Simulation Features

NASA Astrophysics Data System (ADS)

Mitchell, Nathan M.

Techniques for simulating the behavior of elastic objects have matured considerably over the last several decades, tackling diverse problems from non-linear models for incompressibility to accurate self-collisions. Alongside these contributions, advances in parallel hardware design and algorithms have made simulation more efficient and affordable than ever before. However, prior research often has had to commit to design choices that compromise certain simulation features to better optimize others, resulting in a fragmented landscape of solutions. For complex, real-world tasks, such as virtual surgery, a holistic approach is desirable, where complex behavior, performance, and ease of modeling are supported equally. This dissertation caters to this goal in the form of several interconnected threads of investigation, each of which contributes a piece of an unified solution. First, it will be demonstrated how various non-linear materials can be combined with lattice deformers to yield simulations with behavioral richness and a high potential for parallelism. This potential will be exploited to show how a hybrid solver approach based on large macroblocks can accelerate the convergence of these deformers. Further extensions of the lattice concept with non-manifold topology will allow for efficient processing of self-collisions and topology change. Finally, these concepts will be explored in the context of a case study on virtual plastic surgery, demonstrating a real-world problem space where these ideas can be combined to build an expressive authoring tool, allowing surgeons to record procedures digitally for future reference or education.

PSF mapping-based correction of eddy-current-induced distortions in diffusion-weighted echo-planar imaging.

PubMed

In, Myung-Ho; Posnansky, Oleg; Speck, Oliver

2016-05-01

To accurately correct diffusion-encoding direction-dependent eddy-current-induced geometric distortions in diffusion-weighted echo-planar imaging (DW-EPI) and to minimize the calibration time at 7 Tesla (T). A point spread function (PSF) mapping based eddy-current calibration method is newly presented to determine eddy-current-induced geometric distortions even including nonlinear eddy-current effects within the readout acquisition window. To evaluate the temporal stability of eddy-current maps, calibration was performed four times within 3 months. Furthermore, spatial variations of measured eddy-current maps versus their linear superposition were investigated to enable correction in DW-EPIs with arbitrary diffusion directions without direct calibration. For comparison, an image-based eddy-current correction method was additionally applied. Finally, this method was combined with a PSF-based susceptibility-induced distortion correction approach proposed previously to correct both susceptibility and eddy-current-induced distortions in DW-EPIs. Very fast eddy-current calibration in a three-dimensional volume is possible with the proposed method. The measured eddy-current maps are very stable over time and very similar maps can be obtained by linear superposition of principal-axes eddy-current maps. High resolution in vivo brain results demonstrate that the proposed method allows more efficient eddy-current correction than the image-based method. The combination of both PSF-based approaches allows distortion-free images, which permit reliable analysis in diffusion tensor imaging applications at 7T. © 2015 Wiley Periodicals, Inc.

Population response to climate change: linear vs. non-linear modeling approaches.

PubMed

Ellis, Alicia M; Post, Eric

2004-03-31

Research on the ecological consequences of global climate change has elicited a growing interest in the use of time series analysis to investigate population dynamics in a changing climate. Here, we compare linear and non-linear models describing the contribution of climate to the density fluctuations of the population of wolves on Isle Royale, Michigan from 1959 to 1999. The non-linear self excitatory threshold autoregressive (SETAR) model revealed that, due to differences in the strength and nature of density dependence, relatively small and large populations may be differentially affected by future changes in climate. Both linear and non-linear models predict a decrease in the population of wolves with predicted changes in climate. Because specific predictions differed between linear and non-linear models, our study highlights the importance of using non-linear methods that allow the detection of non-linearity in the strength and nature of density dependence. Failure to adopt a non-linear approach to modelling population response to climate change, either exclusively or in addition to linear approaches, may compromise efforts to quantify ecological consequences of future warming.

An SPM8-based approach for attenuation correction combining segmentation and nonrigid template formation: application to simultaneous PET/MR brain imaging.

PubMed

Izquierdo-Garcia, David; Hansen, Adam E; Förster, Stefan; Benoit, Didier; Schachoff, Sylvia; Fürst, Sebastian; Chen, Kevin T; Chonde, Daniel B; Catana, Ciprian

2014-11-01

We present an approach for head MR-based attenuation correction (AC) based on the Statistical Parametric Mapping 8 (SPM8) software, which combines segmentation- and atlas-based features to provide a robust technique to generate attenuation maps (μ maps) from MR data in integrated PET/MR scanners. Coregistered anatomic MR and CT images of 15 glioblastoma subjects were used to generate the templates. The MR images from these subjects were first segmented into 6 tissue classes (gray matter, white matter, cerebrospinal fluid, bone, soft tissue, and air), which were then nonrigidly coregistered using a diffeomorphic approach. A similar procedure was used to coregister the anatomic MR data for a new subject to the template. Finally, the CT-like images obtained by applying the inverse transformations were converted to linear attenuation coefficients to be used for AC of PET data. The method was validated on 16 new subjects with brain tumors (n = 12) or mild cognitive impairment (n = 4) who underwent CT and PET/MR scans. The μ maps and corresponding reconstructed PET images were compared with those obtained using the gold standard CT-based approach and the Dixon-based method available on the Biograph mMR scanner. Relative change (RC) images were generated in each case, and voxel- and region-of-interest-based analyses were performed. The leave-one-out cross-validation analysis of the data from the 15 atlas-generation subjects showed small errors in brain linear attenuation coefficients (RC, 1.38% ± 4.52%) compared with the gold standard. Similar results (RC, 1.86% ± 4.06%) were obtained from the analysis of the atlas-validation datasets. The voxel- and region-of-interest-based analysis of the corresponding reconstructed PET images revealed quantification errors of 3.87% ± 5.0% and 2.74% ± 2.28%, respectively. The Dixon-based method performed substantially worse (the mean RC values were 13.0% ± 10.25% and 9.38% ± 4.97%, respectively). Areas closer to the skull showed the largest improvement. We have presented an SPM8-based approach for deriving the head μ map from MR data to be used for PET AC in integrated PET/MR scanners. Its implementation is straightforward and requires only the morphologic data acquired with a single MR sequence. The method is accurate and robust, combining the strengths of both segmentation- and atlas-based approaches while minimizing their drawbacks. © 2014 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Distributed optical fiber-based monitoring approach of spatial seepage behavior in dike engineering

NASA Astrophysics Data System (ADS)

Su, Huaizhi; Ou, Bin; Yang, Lifu; Wen, Zhiping

2018-07-01

The failure caused by seepage is the most common one in dike engineering. As to the characteristics of seepage in dike, such as longitudinal extension engineering, the randomness, strong concealment and small initial quantity order, by means of distributed fiber temperature sensor system (DTS), adopting an improved optical fiber layer layout scheme, the location of initial interpolation point of the saturation line is obtained. With the barycentric Lagrange interpolation collocation method (BLICM), the infiltrated surface of dike full-section is generated. Combined with linear optical fiber monitoring seepage method, BLICM is applied in an engineering case, which shows that a real-time seepage monitoring technique is presented in full-section of dike based on the combination method.

Linearized self-consistent GW approach satisfying the Ward identity

NASA Astrophysics Data System (ADS)

Kuwahara, Riichi; Ohno, Kaoru

2014-09-01

We propose a linearized self-consistent GW approach satisfying the Ward identity. The vertex function derived from the Ward-Takahashi identity in the limit of q =0 and ω -ω'=0 is included in the self-energy and the polarization function as a consequence of the linearization of the quasiparticle equation. Due to the energy dependence of the self-energy, the Hamiltonian is a non-Hermitian operator and quasiparticle states are nonorthonormal and linearly dependent. However, the linearized quasiparticle states recover orthonormality and fulfill the completeness condition. This approach is very efficient, and the resulting quasiparticle energies are greatly improved compared to the nonlinearized self-consistent GW approach, although its computational cost is not much increased. We show the results for atoms and dimers of Li and Na compared with other approaches. We also propose convenient ways to calculate the Luttinger-Ward functional Φ based on a plasmon-pole model and calculate the total energy for the ground state. As a result, we conclude that the linearization improves overall behaviors in the self-consistent GW approach.

Koopman Operator Framework for Time Series Modeling and Analysis

NASA Astrophysics Data System (ADS)

Surana, Amit

2018-01-01

We propose an interdisciplinary framework for time series classification, forecasting, and anomaly detection by combining concepts from Koopman operator theory, machine learning, and linear systems and control theory. At the core of this framework is nonlinear dynamic generative modeling of time series using the Koopman operator which is an infinite-dimensional but linear operator. Rather than working with the underlying nonlinear model, we propose two simpler linear representations or model forms based on Koopman spectral properties. We show that these model forms are invariants of the generative model and can be readily identified directly from data using techniques for computing Koopman spectral properties without requiring the explicit knowledge of the generative model. We also introduce different notions of distances on the space of such model forms which is essential for model comparison/clustering. We employ the space of Koopman model forms equipped with distance in conjunction with classical machine learning techniques to develop a framework for automatic feature generation for time series classification. The forecasting/anomaly detection framework is based on using Koopman model forms along with classical linear systems and control approaches. We demonstrate the proposed framework for human activity classification, and for time series forecasting/anomaly detection in power grid application.

A neural network approach to job-shop scheduling.

PubMed

Zhou, D N; Cherkassky, V; Baldwin, T R; Olson, D E

1991-01-01

A novel analog computational network is presented for solving NP-complete constraint satisfaction problems, i.e. job-shop scheduling. In contrast to most neural approaches to combinatorial optimization based on quadratic energy cost function, the authors propose to use linear cost functions. As a result, the network complexity (number of neurons and the number of resistive interconnections) grows only linearly with problem size, and large-scale implementations become possible. The proposed approach is related to the linear programming network described by D.W. Tank and J.J. Hopfield (1985), which also uses a linear cost function for a simple optimization problem. It is shown how to map a difficult constraint-satisfaction problem onto a simple neural net in which the number of neural processors equals the number of subjobs (operations) and the number of interconnections grows linearly with the total number of operations. Simulations show that the authors' approach produces better solutions than existing neural approaches to job-shop scheduling, i.e. the traveling salesman problem-type Hopfield approach and integer linear programming approach of J.P.S. Foo and Y. Takefuji (1988), in terms of the quality of the solution and the network complexity.

Stochastic static fault slip inversion from geodetic data with non-negativity and bounds constraints

NASA Astrophysics Data System (ADS)

Nocquet, J.-M.

2018-04-01

Despite surface displacements observed by geodesy are linear combinations of slip at faults in an elastic medium, determining the spatial distribution of fault slip remains a ill-posed inverse problem. A widely used approach to circumvent the illness of the inversion is to add regularization constraints in terms of smoothing and/or damping so that the linear system becomes invertible. However, the choice of regularization parameters is often arbitrary, and sometimes leads to significantly different results. Furthermore, the resolution analysis is usually empirical and cannot be made independently of the regularization. The stochastic approach of inverse problems (Tarantola & Valette 1982; Tarantola 2005) provides a rigorous framework where the a priori information about the searched parameters is combined with the observations in order to derive posterior probabilities of the unkown parameters. Here, I investigate an approach where the prior probability density function (pdf) is a multivariate Gaussian function, with single truncation to impose positivity of slip or double truncation to impose positivity and upper bounds on slip for interseismic modeling. I show that the joint posterior pdf is similar to the linear untruncated Gaussian case and can be expressed as a Truncated Multi-Variate Normal (TMVN) distribution. The TMVN form can then be used to obtain semi-analytical formulas for the single, two-dimensional or n-dimensional marginal pdf. The semi-analytical formula involves the product of a Gaussian by an integral term that can be evaluated using recent developments in TMVN probabilities calculations (e.g. Genz & Bretz 2009). Posterior mean and covariance can also be efficiently derived. I show that the Maximum Posterior (MAP) can be obtained using a Non-Negative Least-Squares algorithm (Lawson & Hanson 1974) for the single truncated case or using the Bounded-Variable Least-Squares algorithm (Stark & Parker 1995) for the double truncated case. I show that the case of independent uniform priors can be approximated using TMVN. The numerical equivalence to Bayesian inversions using Monte Carlo Markov Chain (MCMC) sampling is shown for a synthetic example and a real case for interseismic modeling in Central Peru. The TMVN method overcomes several limitations of the Bayesian approach using MCMC sampling. First, the need of computer power is largely reduced. Second, unlike Bayesian MCMC based approach, marginal pdf, mean, variance or covariance are obtained independently one from each other. Third, the probability and cumulative density functions can be obtained with any density of points. Finally, determining the Maximum Posterior (MAP) is extremely fast.

Experimental design and data analysis of Ago-RIP-Seq experiments for the identification of microRNA targets.

PubMed

Tichy, Diana; Pickl, Julia Maria Anna; Benner, Axel; Sültmann, Holger

2017-03-31

The identification of microRNA (miRNA) target genes is crucial for understanding miRNA function. Many methods for the genome-wide miRNA target identification have been developed in recent years; however, they have several limitations including the dependence on low-confident prediction programs and artificial miRNA manipulations. Ago-RNA immunoprecipitation combined with high-throughput sequencing (Ago-RIP-Seq) is a promising alternative. However, appropriate statistical data analysis algorithms taking into account the experimental design and the inherent noise of such experiments are largely lacking.Here, we investigate the experimental design for Ago-RIP-Seq and examine biostatistical methods to identify de novo miRNA target genes. Statistical approaches considered are either based on a negative binomial model fit to the read count data or applied to transformed data using a normal distribution-based generalized linear model. We compare them by a real data simulation study using plasmode data sets and evaluate the suitability of the approaches to detect true miRNA targets by sensitivity and false discovery rates. Our results suggest that simple approaches like linear regression models on (appropriately) transformed read count data are preferable. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures

PubMed Central

Ngendahimana, David K.; Fagerholm, Cara L.; Sun, Jiayang; Bruckman, Laura S.

2017-01-01

Accelerated weathering exposures were performed on poly(ethylene-terephthalate) (PET) films. Longitudinal multi-level predictive models as a function of PET grades and exposure types were developed for the change in yellowness index (YI) and haze (%). Exposures with similar change in YI were modeled using a linear fixed-effects modeling approach. Due to the complex nature of haze formation, measurement uncertainty, and the differences in the samples’ responses, the change in haze (%) depended on individual samples’ responses and a linear mixed-effects modeling approach was used. When compared to fixed-effects models, the addition of random effects in the haze formation models significantly increased the variance explained. For both modeling approaches, diagnostic plots confirmed independence and homogeneity with normally distributed residual errors. Predictive R2 values for true prediction error and predictive power of the models demonstrated that the models were not subject to over-fitting. These models enable prediction under pre-defined exposure conditions for a given exposure time (or photo-dosage in case of UV light exposure). PET degradation under cyclic exposures combining UV light and condensing humidity is caused by photolytic and hydrolytic mechanisms causing yellowing and haze formation. Quantitative knowledge of these degradation pathways enable cross-correlation of these lab-based exposures with real-world conditions for service life prediction. PMID:28498875

Fast global image smoothing based on weighted least squares.

PubMed

Min, Dongbo; Choi, Sunghwan; Lu, Jiangbo; Ham, Bumsub; Sohn, Kwanghoon; Do, Minh N

2014-12-01

This paper presents an efficient technique for performing a spatially inhomogeneous edge-preserving image smoothing, called fast global smoother. Focusing on sparse Laplacian matrices consisting of a data term and a prior term (typically defined using four or eight neighbors for 2D image), our approach efficiently solves such global objective functions. In particular, we approximate the solution of the memory-and computation-intensive large linear system, defined over a d-dimensional spatial domain, by solving a sequence of 1D subsystems. Our separable implementation enables applying a linear-time tridiagonal matrix algorithm to solve d three-point Laplacian matrices iteratively. Our approach combines the best of two paradigms, i.e., efficient edge-preserving filters and optimization-based smoothing. Our method has a comparable runtime to the fast edge-preserving filters, but its global optimization formulation overcomes many limitations of the local filtering approaches. Our method also achieves high-quality results as the state-of-the-art optimization-based techniques, but runs ∼10-30 times faster. Besides, considering the flexibility in defining an objective function, we further propose generalized fast algorithms that perform Lγ norm smoothing (0 < γ < 2) and support an aggregated (robust) data term for handling imprecise data constraints. We demonstrate the effectiveness and efficiency of our techniques in a range of image processing and computer graphics applications.

Modeling first impressions from highly variable facial images

PubMed Central

Vernon, Richard J. W.; Sutherland, Clare A. M.; Young, Andrew W.; Hartley, Tom

2014-01-01

First impressions of social traits, such as trustworthiness or dominance, are reliably perceived in faces, and despite their questionable validity they can have considerable real-world consequences. We sought to uncover the information driving such judgments, using an attribute-based approach. Attributes (physical facial features) were objectively measured from feature positions and colors in a database of highly variable “ambient” face photographs, and then used as input for a neural network to model factor dimensions (approachability, youthful-attractiveness, and dominance) thought to underlie social attributions. A linear model based on this approach was able to account for 58% of the variance in raters’ impressions of previously unseen faces, and factor-attribute correlations could be used to rank attributes by their importance to each factor. Reversing this process, neural networks were then used to predict facial attributes and corresponding image properties from specific combinations of factor scores. In this way, the factors driving social trait impressions could be visualized as a series of computer-generated cartoon face-like images, depicting how attributes change along each dimension. This study shows that despite enormous variation in ambient images of faces, a substantial proportion of the variance in first impressions can be accounted for through linear changes in objectively defined features. PMID:25071197

Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst

PubMed Central

2013-01-01

Background Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals. Methods Emotional ECG data was obtained from sixty participants by inducing the six basic emotional states (happiness, sadness, fear, disgust, surprise and neutral) using audio-visual stimuli. The non-linear feature ‘Hurst’ was computed using Rescaled Range Statistics (RRS) and Finite Variance Scaling (FVS) methods. New Hurst features were proposed by combining the existing RRS and FVS methods with Higher Order Statistics (HOS). The features were then classified using four classifiers – Bayesian Classifier, Regression Tree, K- nearest neighbor and Fuzzy K-nearest neighbor. Seventy percent of the features were used for training and thirty percent for testing the algorithm. Results Analysis of Variance (ANOVA) conveyed that Hurst and the proposed features were statistically significant (p < 0.001). Hurst computed using RRS and FVS methods showed similar classification accuracy. The features obtained by combining FVS and HOS performed better with a maximum accuracy of 92.87% and 76.45% for classifying the six emotional states using random and subject independent validation respectively. Conclusions The results indicate that the combination of non-linear analysis and HOS tend to capture the finer emotional changes that can be seen in healthy ECG data. This work can be further fine tuned to develop a real time system. PMID:23680041

A square root ensemble Kalman filter application to a motor-imagery brain-computer interface.

PubMed

Kamrunnahar, M; Schiff, S J

2011-01-01

We here investigated a non-linear ensemble Kalman filter (SPKF) application to a motor imagery brain computer interface (BCI). A square root central difference Kalman filter (SR-CDKF) was used as an approach for brain state estimation in motor imagery task performance, using scalp electroencephalography (EEG) signals. Healthy human subjects imagined left vs. right hand movements and tongue vs. bilateral toe movements while scalp EEG signals were recorded. Offline data analysis was conducted for training the model as well as for decoding the imagery movements. Preliminary results indicate the feasibility of this approach with a decoding accuracy of 78%-90% for the hand movements and 70%-90% for the tongue-toes movements. Ongoing research includes online BCI applications of this approach as well as combined state and parameter estimation using this algorithm with different system dynamic models.

Magnetic resonance imaging-guided attenuation correction of positron emission tomography data in PET/MRI

PubMed Central

Izquierdo-Garcia, David; Catana, Ciprian

2018-01-01

Synopsis Attenuation correction (AC) is one of the most important challenges in the recently introduced combined positron emission tomography/magnetic resonance imaging (PET/MR) scanners. PET/MR AC (MR-AC) approaches aim to develop methods that allow accurate estimation of the linear attenuation coefficients (LACs) of the tissues and other components located in the PET field of view (FoV). MR-AC methods can be divided into three main categories: segmentation-, atlas- and PET-based. This review aims to provide a comprehensive list of the state of the art MR-AC approaches as well as their pros and cons. The main sources of artifacts such as body-truncation, metallic implants and hardware correction will be presented. Finally, this review will discuss the current status of MR-AC approaches for clinical applications. PMID:26952727

Smith predictor based-sliding mode controller for integrating processes with elevated deadtime.

PubMed

Camacho, Oscar; De la Cruz, Francisco

2004-04-01

An approach to control integrating processes with elevated deadtime using a Smith predictor sliding mode controller is presented. A PID sliding surface and an integrating first-order plus deadtime model have been used to synthesize the controller. Since the performance of existing controllers with a Smith predictor decrease in the presence of modeling errors, this paper presents a simple approach to combining the Smith predictor with the sliding mode concept, which is a proven, simple, and robust procedure. The proposed scheme has a set of tuning equations as a function of the characteristic parameters of the model. For implementation of our proposed approach, computer based industrial controllers that execute PID algorithms can be used. The performance and robustness of the proposed controller are compared with the Matausek-Micić scheme for linear systems using simulations.

Potential Fifty Percent Reduction in Saturation Diving Decompression Time Using a Combination of Intermittent Recompression and Exercise

NASA Technical Reports Server (NTRS)

Gernhardt, Michael I.; Abercromby, Andrew; Conklin, Johnny

2007-01-01

Conventional saturation decompression protocols use linear decompression rates that become progressively slower at shallower depths, consistent with free gas phase control vs. dissolved gas elimination kinetics. If decompression is limited by control of free gas phase, linear decompression is an inefficient strategy. The NASA prebreathe reduction program demonstrated that exercise during O2 prebreathe resulted in a 50% reduction (2 h vs. 4 h) in the saturation decompression time from 14.7 to 4.3 psi and a significant reduction in decompression sickness (DCS: 0 vs. 23.7%). Combining exercise with intermittent recompression, which controls gas phase growth and eliminates supersaturation before exercising, may enable more efficient saturation decompression schedules. A tissue bubble dynamics model (TBDM) was used in conjunction with a NASA exercise prebreathe model (NEPM) that relates tissue inert gas exchange rate constants to exercise (ml O2/kg-min), to develop a schedule for decompression from helium saturation at 400 fsw. The models provide significant prediction (p < 0.001) and goodness of fit with 430 cases of DCS in 6437 laboratory dives for TBDM (p = 0.77) and with 22 cases of DCS in 159 altitude exposures for NEPM (p = 0.70). The models have also been used operationally in over 25,000 dives (TBDM) and 40 spacewalks (NEPM). The standard U.S. Navy (USN) linear saturation decompression schedule from saturation at 400 fsw required 114.5 h with a maximum Bubble Growth Index (BGI(sub max)) of 17.5. Decompression using intermittent recompression combined with two 10 min exercise periods (75% VO2 (sub peak)) per day required 54.25 h (BGI(sub max): 14.7). Combined intermittent recompression and exercise resulted in a theoretical 53% (2.5 day) reduction in decompression time and theoretically lower DCS risk compared to the standard USN decompression schedule. These results warrant future decompression trials to evaluate the efficacy of this approach.

Induction linear accelerators

NASA Astrophysics Data System (ADS)

Birx, Daniel

1992-03-01

Among the family of particle accelerators, the Induction Linear Accelerator is the best suited for the acceleration of high current electron beams. Because the electromagnetic radiation used to accelerate the electron beam is not stored in the cavities but is supplied by transmission lines during the beam pulse it is possible to utilize very low Q (typically<10) structures and very large beam pipes. This combination increases the beam breakup limited maximum currents to of order kiloamperes. The micropulse lengths of these machines are measured in 10's of nanoseconds and duty factors as high as 10-4 have been achieved. Until recently the major problem with these machines has been associated with the pulse power drive. Beam currents of kiloamperes and accelerating potentials of megavolts require peak power drives of gigawatts since no energy is stored in the structure. The marriage of liner accelerator technology and nonlinear magnetic compressors has produced some unique capabilities. It now appears possible to produce electron beams with average currents measured in amperes, peak currents in kiloamperes and gradients exceeding 1 MeV/meter, with power efficiencies approaching 50%. The nonlinear magnetic compression technology has replaced the spark gap drivers used on earlier accelerators with state-of-the-art all-solid-state SCR commutated compression chains. The reliability of these machines is now approaching 1010 shot MTBF. In the following paper we will briefly review the historical development of induction linear accelerators and then discuss the design considerations.

Prediction of aquatic toxicity mode of action using linear discriminant and random forest models.

PubMed

Martin, Todd M; Grulke, Christopher M; Young, Douglas M; Russom, Christine L; Wang, Nina Y; Jackson, Crystal R; Barron, Mace G

2013-09-23

The ability to determine the mode of action (MOA) for a diverse group of chemicals is a critical part of ecological risk assessment and chemical regulation. However, existing MOA assignment approaches in ecotoxicology have been limited to a relatively few MOAs, have high uncertainty, or rely on professional judgment. In this study, machine based learning algorithms (linear discriminant analysis and random forest) were used to develop models for assigning aquatic toxicity MOA. These methods were selected since they have been shown to be able to correlate diverse data sets and provide an indication of the most important descriptors. A data set of MOA assignments for 924 chemicals was developed using a combination of high confidence assignments, international consensus classifications, ASTER (ASessment Tools for the Evaluation of Risk) predictions, and weight of evidence professional judgment based an assessment of structure and literature information. The overall data set was randomly divided into a training set (75%) and a validation set (25%) and then used to develop linear discriminant analysis (LDA) and random forest (RF) MOA assignment models. The LDA and RF models had high internal concordance and specificity and were able to produce overall prediction accuracies ranging from 84.5 to 87.7% for the validation set. These results demonstrate that computational chemistry approaches can be used to determine the acute toxicity MOAs across a large range of structures and mechanisms.

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

PubMed

MacNab, Ying C

2016-08-01

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

BinQuasi: a peak detection method for ChIP-sequencing data with biological replicates.

PubMed

Goren, Emily; Liu, Peng; Wang, Chao; Wang, Chong

2018-04-19

ChIP-seq experiments that are aimed at detecting DNA-protein interactions require biological replication to draw inferential conclusions, however there is no current consensus on how to analyze ChIP-seq data with biological replicates. Very few methodologies exist for the joint analysis of replicated ChIP-seq data, with approaches ranging from combining the results of analyzing replicates individually to joint modeling of all replicates. Combining the results of individual replicates analyzed separately can lead to reduced peak classification performance compared to joint modeling. Currently available methods for joint analysis may fail to control the false discovery rate at the nominal level. We propose BinQuasi, a peak caller for replicated ChIP-seq data, that jointly models biological replicates using a generalized linear model framework and employs a one-sided quasi-likelihood ratio test to detect peaks. When applied to simulated data and real datasets, BinQuasi performs favorably compared to existing methods, including better control of false discovery rate than existing joint modeling approaches. BinQuasi offers a flexible approach to joint modeling of replicated ChIP-seq data which is preferable to combining the results of replicates analyzed individually. Source code is freely available for download at https://cran.r-project.org/package=BinQuasi, implemented in R. pliu@iastate.edu or egoren@iastate.edu. Supplementary material is available at Bioinformatics online.

Protein linear indices of the 'macromolecular pseudograph alpha-carbon atom adjacency matrix' in bioinformatics. Part 1: prediction of protein stability effects of a complete set of alanine substitutions in Arc repressor.

PubMed

Marrero-Ponce, Yovani; Medina-Marrero, Ricardo; Castillo-Garit, Juan A; Romero-Zaldivar, Vicente; Torrens, Francisco; Castro, Eduardo A

2005-04-15

A novel approach to bio-macromolecular design from a linear algebra point of view is introduced. A protein's total (whole protein) and local (one or more amino acid) linear indices are a new set of bio-macromolecular descriptors of relevance to protein QSAR/QSPR studies. These amino-acid level biochemical descriptors are based on the calculation of linear maps on Rn[f k(xmi):Rn-->Rn] in canonical basis. These bio-macromolecular indices are calculated from the kth power of the macromolecular pseudograph alpha-carbon atom adjacency matrix. Total linear indices are linear functional on Rn. That is, the kth total linear indices are linear maps from Rn to the scalar R[f k(xm):Rn-->R]. Thus, the kth total linear indices are calculated by summing the amino-acid linear indices of all amino acids in the protein molecule. A study of the protein stability effects for a complete set of alanine substitutions in the Arc repressor illustrates this approach. A quantitative model that discriminates near wild-type stability alanine mutants from the reduced-stability ones in a training series was obtained. This model permitted the correct classification of 97.56% (40/41) and 91.67% (11/12) of proteins in the training and test set, respectively. It shows a high Matthews correlation coefficient (MCC=0.952) for the training set and an MCC=0.837 for the external prediction set. Additionally, canonical regression analysis corroborated the statistical quality of the classification model (Rcanc=0.824). This analysis was also used to compute biological stability canonical scores for each Arc alanine mutant. On the other hand, the linear piecewise regression model compared favorably with respect to the linear regression one on predicting the melting temperature (tm) of the Arc alanine mutants. The linear model explains almost 81% of the variance of the experimental tm (R=0.90 and s=4.29) and the LOO press statistics evidenced its predictive ability (q2=0.72 and scv=4.79). Moreover, the TOMOCOMD-CAMPS method produced a linear piecewise regression (R=0.97) between protein backbone descriptors and tm values for alanine mutants of the Arc repressor. A break-point value of 51.87 degrees C characterized two mutant clusters and coincided perfectly with the experimental scale. For this reason, we can use the linear discriminant analysis and piecewise models in combination to classify and predict the stability of the mutant Arc homodimers. These models also permitted the interpretation of the driving forces of such folding process, indicating that topologic/topographic protein backbone interactions control the stability profile of wild-type Arc and its alanine mutants.

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

Romano, J.D.; Woan, G.

Data from the Laser Interferometer Space Antenna (LISA) is expected to be dominated by frequency noise from its lasers. However, the noise from any one laser appears more than once in the data and there are combinations of the data that are insensitive to this noise. These combinations, called time delay interferometry (TDI) variables, have received careful study and point the way to how LISA data analysis may be performed. Here we approach the problem from the direction of statistical inference, and show that these variables are a direct consequence of a principal component analysis of the problem. We presentmore » a formal analysis for a simple LISA model and show that there are eigenvectors of the noise covariance matrix that do not depend on laser frequency noise. Importantly, these orthogonal basis vectors correspond to linear combinations of TDI variables. As a result we show that the likelihood function for source parameters using LISA data can be based on TDI combinations of the data without loss of information.« less

Aether: leveraging linear programming for optimal cloud computing in genomics

PubMed Central

Luber, Jacob M; Tierney, Braden T; Cofer, Evan M; Patel, Chirag J

2018-01-01

Abstract Motivation Across biology, we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities. Results Here, we present Aether (http://aether.kosticlab.org), an intuitive, easy-to-use, cost-effective and scalable framework that uses linear programming to optimally bid on and deploy combinations of underutilized cloud computing resources. Our approach simultaneously minimizes the cost of data analysis and provides an easy transition from users’ existing HPC pipelines. Availability and implementation Data utilized are available at https://pubs.broadinstitute.org/diabimmune and with EBI SRA accession ERP005989. Source code is available at (https://github.com/kosticlab/aether). Examples, documentation and a tutorial are available at http://aether.kosticlab.org. Contact chirag_patel@hms.harvard.edu or aleksandar.kostic@joslin.harvard.edu Supplementary information Supplementary data are available at Bioinformatics online. PMID:29228186

Clustering performance comparison using K-means and expectation maximization algorithms.

PubMed

Jung, Yong Gyu; Kang, Min Soo; Heo, Jun

2014-11-14

Clustering is an important means of data mining based on separating data categories by similar features. Unlike the classification algorithm, clustering belongs to the unsupervised type of algorithms. Two representatives of the clustering algorithms are the K -means and the expectation maximization (EM) algorithm. Linear regression analysis was extended to the category-type dependent variable, while logistic regression was achieved using a linear combination of independent variables. To predict the possibility of occurrence of an event, a statistical approach is used. However, the classification of all data by means of logistic regression analysis cannot guarantee the accuracy of the results. In this paper, the logistic regression analysis is applied to EM clusters and the K -means clustering method for quality assessment of red wine, and a method is proposed for ensuring the accuracy of the classification results.

Experimental investigation of a four-qubit linear-optical quantum logic circuit

NASA Astrophysics Data System (ADS)

Stárek, R.; Mičuda, M.; Miková, M.; Straka, I.; Dušek, M.; Ježek, M.; Fiurášek, J.

2016-09-01

We experimentally demonstrate and characterize a four-qubit linear-optical quantum logic circuit. Our robust and versatile scheme exploits encoding of two qubits into polarization and path degrees of single photons and involves two crossed inherently stable interferometers. This approach allows us to design a complex quantum logic circuit that combines a genuine four-qubit C3Z gate and several two-qubit and single-qubit gates. The C3Z gate introduces a sign flip if and only if all four qubits are in the computational state |1>. We verify high-fidelity performance of this central four-qubit gate using Hofmann bounds on quantum gate fidelity and Monte Carlo fidelity sampling. We also experimentally demonstrate that the quantum logic circuit can generate genuine multipartite entanglement and we certify the entanglement with the use of suitably tailored entanglement witnesses.

Integrated approach to multimodal media content analysis

NASA Astrophysics Data System (ADS)

Zhang, Tong; Kuo, C.-C. Jay

1999-12-01

In this work, we present a system for the automatic segmentation, indexing and retrieval of audiovisual data based on the combination of audio, visual and textural content analysis. The video stream is demultiplexed into audio, image and caption components. Then, a semantic segmentation of the audio signal based on audio content analysis is conducted, and each segment is indexed as one of the basic audio types. The image sequence is segmented into shots based on visual information analysis, and keyframes are extracted from each shot. Meanwhile, keywords are detected from the closed caption. Index tables are designed for both linear and non-linear access to the video. It is shown by experiments that the proposed methods for multimodal media content analysis are effective. And that the integrated framework achieves satisfactory results for video information filtering and retrieval.

Experimental investigation of a four-qubit linear-optical quantum logic circuit.

PubMed

Stárek, R; Mičuda, M; Miková, M; Straka, I; Dušek, M; Ježek, M; Fiurášek, J

2016-09-20

We experimentally demonstrate and characterize a four-qubit linear-optical quantum logic circuit. Our robust and versatile scheme exploits encoding of two qubits into polarization and path degrees of single photons and involves two crossed inherently stable interferometers. This approach allows us to design a complex quantum logic circuit that combines a genuine four-qubit C(3)Z gate and several two-qubit and single-qubit gates. The C(3)Z gate introduces a sign flip if and only if all four qubits are in the computational state |1〉. We verify high-fidelity performance of this central four-qubit gate using Hofmann bounds on quantum gate fidelity and Monte Carlo fidelity sampling. We also experimentally demonstrate that the quantum logic circuit can generate genuine multipartite entanglement and we certify the entanglement with the use of suitably tailored entanglement witnesses.

Probabilistic risk assessment for CO2 storage in geological formations: robust design and support for decision making under uncertainty

NASA Astrophysics Data System (ADS)

Oladyshkin, Sergey; Class, Holger; Helmig, Rainer; Nowak, Wolfgang

2010-05-01

CO2 storage in geological formations is currently being discussed intensively as a technology for mitigating CO2 emissions. However, any large-scale application requires a thorough analysis of the potential risks. Current numerical simulation models are too expensive for probabilistic risk analysis and for stochastic approaches based on brute-force repeated simulation. Even single deterministic simulations may require parallel high-performance computing. The multiphase flow processes involved are too non-linear for quasi-linear error propagation and other simplified stochastic tools. As an alternative approach, we propose a massive stochastic model reduction based on the probabilistic collocation method. The model response is projected onto a orthogonal basis of higher-order polynomials to approximate dependence on uncertain parameters (porosity, permeability etc.) and design parameters (injection rate, depth etc.). This allows for a non-linear propagation of model uncertainty affecting the predicted risk, ensures fast computation and provides a powerful tool for combining design variables and uncertain variables into one approach based on an integrative response surface. Thus, the design task of finding optimal injection regimes explicitly includes uncertainty, which leads to robust designs of the non-linear system that minimize failure probability and provide valuable support for risk-informed management decisions. We validate our proposed stochastic approach by Monte Carlo simulation using a common 3D benchmark problem (Class et al. Computational Geosciences 13, 2009). A reasonable compromise between computational efforts and precision was reached already with second-order polynomials. In our case study, the proposed approach yields a significant computational speedup by a factor of 100 compared to Monte Carlo simulation. We demonstrate that, due to the non-linearity of the flow and transport processes during CO2 injection, including uncertainty in the analysis leads to a systematic and significant shift of predicted leakage rates towards higher values compared with deterministic simulations, affecting both risk estimates and the design of injection scenarios. This implies that, neglecting uncertainty can be a strong simplification for modeling CO2 injection, and the consequences can be stronger than when neglecting several physical phenomena (e.g. phase transition, convective mixing, capillary forces etc.). The authors would like to thank the German Research Foundation (DFG) for financial support of the project within the Cluster of Excellence in Simulation Technology (EXC 310/1) at the University of Stuttgart. Keywords: polynomial chaos; CO2 storage; multiphase flow; porous media; risk assessment; uncertainty; integrative response surfaces

Humidity-corrected Arrhenius equation: The reference condition approach.

PubMed

Naveršnik, Klemen; Jurečič, Rok

2016-03-16

Accelerated and stress stability data is often used to predict shelf life of pharmaceuticals. Temperature, combined with humidity accelerates chemical decomposition and the Arrhenius equation is used to extrapolate accelerated stability results to long-term stability. Statistical estimation of the humidity-corrected Arrhenius equation is not straightforward due to its non-linearity. A two stage nonlinear fitting approach is used in practice, followed by a prediction stage. We developed a single-stage statistical procedure, called the reference condition approach, which has better statistical properties (less collinearity, direct estimation of uncertainty, narrower prediction interval) and is significantly easier to use, compared to the existing approaches. Our statistical model was populated with data from a 35-day stress stability study on a laboratory batch of vitamin tablets and required mere 30 laboratory assay determinations. The stability prediction agreed well with the actual 24-month long term stability of the product. The approach has high potential to assist product formulation, specification setting and stability statements. Copyright © 2016 Elsevier B.V. All rights reserved.

Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach

NASA Astrophysics Data System (ADS)

Chiadamrong, N.; Piyathanavong, V.

2017-12-01

Models that aim to optimize the design of supply chain networks have gained more interest in the supply chain literature. Mixed-integer linear programming and discrete-event simulation are widely used for such an optimization problem. We present a hybrid approach to support decisions for supply chain network design using a combination of analytical and discrete-event simulation models. The proposed approach is based on iterative procedures until the difference between subsequent solutions satisfies the pre-determined termination criteria. The effectiveness of proposed approach is illustrated by an example, which shows closer to optimal results with much faster solving time than the results obtained from the conventional simulation-based optimization model. The efficacy of this proposed hybrid approach is promising and can be applied as a powerful tool in designing a real supply chain network. It also provides the possibility to model and solve more realistic problems, which incorporate dynamism and uncertainty.

Optimization techniques for integrating spatial data

USGS Publications Warehouse

Herzfeld, U.C.; Merriam, D.F.

1995-01-01

Two optimization techniques ta predict a spatial variable from any number of related spatial variables are presented. The applicability of the two different methods for petroleum-resource assessment is tested in a mature oil province of the Midcontinent (USA). The information on petroleum productivity, usually not directly accessible, is related indirectly to geological, geophysical, petrographical, and other observable data. This paper presents two approaches based on construction of a multivariate spatial model from the available data to determine a relationship for prediction. In the first approach, the variables are combined into a spatial model by an algebraic map-comparison/integration technique. Optimal weights for the map comparison function are determined by the Nelder-Mead downhill simplex algorithm in multidimensions. Geologic knowledge is necessary to provide a first guess of weights to start the automatization, because the solution is not unique. In the second approach, active set optimization for linear prediction of the target under positivity constraints is applied. Here, the procedure seems to select one variable from each data type (structure, isopachous, and petrophysical) eliminating data redundancy. Automating the determination of optimum combinations of different variables by applying optimization techniques is a valuable extension of the algebraic map-comparison/integration approach to analyzing spatial data. Because of the capability of handling multivariate data sets and partial retention of geographical information, the approaches can be useful in mineral-resource exploration. ?? 1995 International Association for Mathematical Geology.

Linear regression crash prediction models : issues and proposed solutions.

DOT National Transportation Integrated Search

2010-05-01

The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...

Out-Phased Array Linearized Signaling (OPALS): A Practical Approach to Physical Layer Encryption

DTIC Science & Technology

2015-10-26

Out-Phased Array Linearized Signaling ( OPALS ): A Practical Approach to Physical Layer Encryption Eric Tollefson, Bruce R. Jordan Jr., and Joseph D... OPALS ) which provides a practical approach to physical-layer encryption through spatial masking. Our approach modifies just the transmitter to employ...of the channel. With Out-Phased Array Linearized Signaling ( OPALS ), we propose a new masking technique that has some advantages of each of the

Integrating multiple molecular sources into a clinical risk prediction signature by extracting complementary information.

PubMed

Hieke, Stefanie; Benner, Axel; Schlenl, Richard F; Schumacher, Martin; Bullinger, Lars; Binder, Harald

2016-08-30

High-throughput technology allows for genome-wide measurements at different molecular levels for the same patient, e.g. single nucleotide polymorphisms (SNPs) and gene expression. Correspondingly, it might be beneficial to also integrate complementary information from different molecular levels when building multivariable risk prediction models for a clinical endpoint, such as treatment response or survival. Unfortunately, such a high-dimensional modeling task will often be complicated by a limited overlap of molecular measurements at different levels between patients, i.e. measurements from all molecular levels are available only for a smaller proportion of patients. We propose a sequential strategy for building clinical risk prediction models that integrate genome-wide measurements from two molecular levels in a complementary way. To deal with partial overlap, we develop an imputation approach that allows us to use all available data. This approach is investigated in two acute myeloid leukemia applications combining gene expression with either SNP or DNA methylation data. After obtaining a sparse risk prediction signature e.g. from SNP data, an automatically selected set of prognostic SNPs, by componentwise likelihood-based boosting, imputation is performed for the corresponding linear predictor by a linking model that incorporates e.g. gene expression measurements. The imputed linear predictor is then used for adjustment when building a prognostic signature from the gene expression data. For evaluation, we consider stability, as quantified by inclusion frequencies across resampling data sets. Despite an extremely small overlap in the application example with gene expression and SNPs, several genes are seen to be more stably identified when taking the (imputed) linear predictor from the SNP data into account. In the application with gene expression and DNA methylation, prediction performance with respect to survival also indicates that the proposed approach might work well. We consider imputation of linear predictor values to be a feasible and sensible approach for dealing with partial overlap in complementary integrative analysis of molecular measurements at different levels. More generally, these results indicate that a complementary strategy for integrating different molecular levels can result in more stable risk prediction signatures, potentially providing a more reliable insight into the underlying biology.

Tight-binding calculation of single-band and generalized Wannier functions of graphene

NASA Astrophysics Data System (ADS)

Ribeiro, Allan Victor; Bruno-Alfonso, Alexys

Recent work has shown that a tight-binding approach associated with Wannier functions (WFs) provides an intuitive physical image of the electronic structure of graphene. Regarding the case of graphene, Marzari et al. displayed the calculated WFs and presented a comparison between the Wannier-interpolated bands and the bands generated by using the density-functional code. Jung and MacDonald provided a tight-binding model for the π-bands of graphene that involves maximally localized Wannier functions (MLWFs). The mixing of the bands yields better localized WFs. In the present work, the MLWFs of graphene are calculated by combining the Quantum-ESPRESSO code and tight-binding approach. The MLWFs of graphene are calculated from the Bloch functions obtained through a tight binding approach that includes interactions and overlapping obtained by partially fitting the DFT bands. The phase of the Bloch functions of each band is appropriately chosen to produce MLWFs. The same thing applies to the coefficients of their linear combination in the generalized case. The method allows for an intuitive understanding of the maximally localized WFs of graphene and shows excellent agreement with the literature. Moreover, it provides accurate results at reduced computational cost.

Combination of large and small basis sets in electronic structure calculations on large systems

NASA Astrophysics Data System (ADS)

Røeggen, Inge; Gao, Bin

2018-04-01

Two basis sets—a large and a small one—are associated with each nucleus of the system. Each atom has its own separate one-electron basis comprising the large basis set of the atom in question and the small basis sets for the partner atoms in the complex. The perturbed atoms in molecules and solids model is at core of the approach since it allows for the definition of perturbed atoms in a system. It is argued that this basis set approach should be particularly useful for periodic systems. Test calculations are performed on one-dimensional arrays of H and Li atoms. The ground-state energy per atom in the linear H array is determined versus bond length.

Quantum corrections for the cubic Galileon in the covariant language

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

Saltas, Ippocratis D.; Vitagliano, Vincenzo, E-mail: isaltas@fc.ul.pt, E-mail: vincenzo.vitagliano@ist.utl.pt

We present for the first time an explicit exposition of quantum corrections within the cubic Galileon theory including the effect of quantum gravity, in a background- and gauge-invariant manner, employing the field-reparametrisation approach of the covariant effective action at 1-loop. We show that the consideration of gravitational effects in combination with the non-linear derivative structure of the theory reveals new interactions at the perturbative level, which manifest themselves as higher-operators in the associated effective action, which' relevance is controlled by appropriate ratios of the cosmological vacuum and the Galileon mass scale. The significance and concept of the covariant approach inmore » this context is discussed, while all calculations are explicitly presented.« less

A Density Functional Approach to Polarizable Models: A Kim-Gordon-Response Density Interaction Potential for Molecular Simulations

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

Tabacchi, G; Hutter, J; Mundy, C

2005-04-07

A combined linear response--frozen electron density model has been implemented in a molecular dynamics scheme derived from an extended Lagrangian formalism. This approach is based on a partition of the electronic charge distribution into a frozen region described by Kim-Gordon theory, and a response contribution determined by the instaneous ionic configuration of the system. The method is free from empirical pair-potentials and the parameterization protocol involves only calculations on properly chosen subsystems. They apply this method to a series of alkali halides in different physical phases and are able to reproduce experimental structural and thermodynamic properties with an accuracy comparablemore » to Kohn-Sham density functional calculations.« less

The nuclear electric quadrupole moment of copper.

PubMed

Santiago, Régis Tadeu; Teodoro, Tiago Quevedo; Haiduke, Roberto Luiz Andrade

2014-06-21

The nuclear electric quadrupole moment (NQM) of the (63)Cu nucleus was determined from an indirect approach by combining accurate experimental nuclear quadrupole coupling constants (NQCCs) with relativistic Dirac-Coulomb coupled cluster calculations of the electric field gradient (EFG). The data obtained at the highest level of calculation, DC-CCSD-T, from 14 linear molecules containing the copper atom give rise to an indicated NQM of -198(10) mbarn. Such result slightly deviates from the previously accepted standard value given by the muonic method, -220(15) mbarn, although the error bars are superimposed.

Plunge waveforms from inspiralling binary black holes.

PubMed

Baker, J; Brügmann, B; Campanelli, M; Lousto, C O; Takahashi, R

2001-09-17

We study the coalescence of nonspinning binary black holes from near the innermost stable circular orbit down to the final single rotating black hole. We use a technique that combines the full numerical approach to solve the Einstein equations, applied in the truly nonlinear regime, and linearized perturbation theory around the final distorted single black hole at later times. We compute the plunge waveforms, which present a non-negligible signal lasting for t approximately 100M showing early nonlinear ringing, and we obtain estimates for the total gravitational energy and angular momentum radiated.

Design of integrated pitch axis for autopilot/autothrottle and integrated lateral axis for autopilot/yaw damper for NASA TSRV airplane using integral LQG methodology

NASA Technical Reports Server (NTRS)

Kaminer, Isaac; Benson, Russell A.; Coleman, Edward E.; Ebrahimi, Yaghoob S.

1990-01-01

Two designs are presented for control systems for the NASA Transport System Research Vehicle (TSRV) using integral Linear Quadratic Gaussian (LQG) methodology. The first is an integrated longitudinal autopilot/autothrottle design and the second design is an integrated lateral autopilot/yaw damper/sideslip controller design. It is shown that a systematic top-down approach to a complex design problem combined with proper application of modern control synthesis techniques yields a satisfactory solution in a reasonable period of time.

Electronic states with nontrivial topology in Dirac materials

NASA Astrophysics Data System (ADS)

Turkevich, R. V.; Perov, A. A.; Protogenov, A. P.; Chulkov, E. V.

2017-08-01

The theoretical studies of phase states with a linear dispersion of the spectrum of low-energy electron excitations have been reviewed. Some main properties and methods of experimental study of these states in socalled Dirac materials have been discussed in detail. The results of modern studies of symmetry-protected electronic states with nontrivial topology have been reported. Combination of approaches based on geometry with homotopic topology methods and results of condensed matter physics makes it possible to clarify new features of topological insulators, as well as Dirac and Weyl semimetals.

Field by field hybrid upwind splitting methods

NASA Technical Reports Server (NTRS)

Coquel, Frederic; Liou, Meng-Sing

1993-01-01

A new and general approach to upwind splitting is presented. The design principle combines the robustness of flux vector splitting schemes in the capture of nonlinear waves and the accuracy of some flux difference splitting schemes in the resolution of linear waves. The new schemes are derived following a general hybridization technique performed directly at the basic level of the field by field decomposition involved in FDS methods. The scheme does not use a spatial switch to be tuned up according to the local smoothness of the approximate solution.

Boundary-element modelling of dynamics in external poroviscoelastic problems

NASA Astrophysics Data System (ADS)

Igumnov, L. A.; Litvinchuk, S. Yu; Ipatov, A. A.; Petrov, A. N.

2018-04-01

A problem of a spherical cavity in porous media is considered. Porous media are assumed to be isotropic poroelastic or isotropic poroviscoelastic. The poroviscoelastic formulation is treated as a combination of Biot’s theory of poroelasticity and elastic-viscoelastic correspondence principle. Such viscoelastic models as Kelvin–Voigt, Standard linear solid, and a model with weakly singular kernel are considered. Boundary field study is employed with the help of the boundary element method. The direct approach is applied. The numerical scheme is based on the collocation method, regularized boundary integral equation, and Radau stepped scheme.

Axial and Torsional Load-Type Sequencing in Cumulative Fatigue: Low Amplitude Followed by High Amplitude Loading

NASA Technical Reports Server (NTRS)

Bonacuse, Peter J.; Kalluri, Sreeramesh

2001-01-01

The experiments described herein were performed to determine whether damage imposed by axial loading interacts with damage imposed by torsional loading. This paper is a follow on to a study that investigated effects of load-type sequencing on the cumulative fatigue behavior of a cobalt base superalloy, Haynes 188 at 538 C Both the current and the previous study were used to test the applicability of cumulative fatigue damage models to conditions where damage is imposed by different loading modes. In the previous study, axial and torsional two load level cumulative fatigue experiments were conducted, in varied combinations, with the low-cycle fatigue (high amplitude loading) applied first. In present study, the high-cycle fatigue (low amplitude loading) is applied initially. As in the previous study, four sequences (axial/axial, torsion/torsion, axial/torsion, and torsion/axial) of two load level cumulative fatigue experiments were performed. The amount of fatigue damage contributed by each of the imposed loads was estimated by both the Palmgren-Miner linear damage rule (LDR) and the non-linear damage curve approach (DCA). Life predictions for the various cumulative loading combinations are compared with experimental results.

Siting MSW landfill using weighted linear combination and analytical hierarchy process (AHP) methodology in GIS environment (case study: Karaj).

PubMed

Moeinaddini, Mazaher; Khorasani, Nematollah; Danehkar, Afshin; Darvishsefat, Ali Asghar; Zienalyan, Mehdi

2010-05-01

Selection of landfill site is a complex process and needs many diverse criteria. The purpose of this paper is to evaluate the suitability of the studied site as landfill for MSW in Karaj. Using weighted linear combination (WLC) method and spatial cluster analysis (SCA), suitable sites for allocation of landfill for a 20-year period were identified. For analyzing spatial auto-correlation of the land suitability map layer (LSML), Maron's I was used. Finally, using the analytical hierarchy process (AHP), the most preferred alternative for the landfill siting was identified. Main advantages of AHP are: relative ease of handling multiple criteria, easy to understand and effective handling of both qualitative and quantitative data. As a result, 6% of the study area is suitable for landfill siting and third alternative was identified as the most preferred for siting MSW landfill by AHP. The ranking of alternatives were obtained only by applying the WLC approach showed different results from the AHP. The WLC should be used only for the identification of alternatives and the AHP is used for prioritization. We suggest the employed procedure for other similar regions. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

HYDRORECESSION: A toolbox for streamflow recession analysis

NASA Astrophysics Data System (ADS)

Arciniega, S.

2015-12-01

Streamflow recession curves are hydrological signatures allowing to study the relationship between groundwater storage and baseflow and/or low flows at the catchment scale. Recent studies have showed that streamflow recession analysis can be quite sensitive to the combination of different models, extraction techniques and parameter estimation methods. In order to better characterize streamflow recession curves, new methodologies combining multiple approaches have been recommended. The HYDRORECESSION toolbox, presented here, is a Matlab graphical user interface developed to analyse streamflow recession time series with the support of different tools allowing to parameterize linear and nonlinear storage-outflow relationships through four of the most useful recession models (Maillet, Boussinesq, Coutagne and Wittenberg). The toolbox includes four parameter-fitting techniques (linear regression, lower envelope, data binning and mean squared error) and three different methods to extract hydrograph recessions segments (Vogel, Brutsaert and Aksoy). In addition, the toolbox has a module that separates the baseflow component from the observed hydrograph using the inverse reservoir algorithm. Potential applications provided by HYDRORECESSION include model parameter analysis, hydrological regionalization and classification, baseflow index estimates, catchment-scale recharge and low-flows modelling, among others. HYDRORECESSION is freely available for non-commercial and academic purposes.

The brain adjusts grip forces differently according to gravity and inertia: a parabolic flight experiment

PubMed Central

White, Olivier

2015-01-01

In everyday life, one of the most frequent activities involves accelerating and decelerating an object held in precision grip. In many contexts, humans scale and synchronize their grip force (GF), normal to the finger/object contact, in anticipation of the expected tangential load force (LF), resulting from the combination of the gravitational and the inertial forces. In many contexts, GF and LF are linearly coupled. A few studies have examined how we adjust the parameters–gain and offset–of this linear relationship. However, the question remains open as to how the brain adjusts GF regardless of whether LF is generated by different combinations of weight and inertia. Here, we designed conditions to generate equivalent magnitudes of LF by independently varying mass and movement frequency. In a control experiment, we directly manipulated gravity in parabolic flights, while other factors remained constant. We show with a simple computational approach that, to adjust GF, the brain is sensitive to how LFs are produced at the fingertips. This provides clear evidence that the analysis of the origin of LF is performed centrally, and not only at the periphery. PMID:25717293

Influence of smooth temperature variation on hotspot ignition

NASA Astrophysics Data System (ADS)

Reinbacher, Fynn; Regele, Jonathan David

2018-01-01

Autoignition in thermally stratified reactive mixtures originates in localised hotspots. The ignition behaviour is often characterised using linear temperature gradients and more recently constant temperature plateaus combined with temperature gradients. Acoustic timescale characterisation of plateau regions has been successfully used to characterise the type of mechanical disturbance that will be created from a plateau core ignition. This work combines linear temperature gradients with superelliptic cores in order to more accurately account for a local temperature maximum of finite size and the smooth temperature variation contained inside realistic hotspot centres. A one-step Arrhenius reaction is used to model a H2-air reactive mixture. Using the superelliptic approach a range of behaviours for temperature distributions are investigated by varying the temperature profile between the gradient only and plateau and gradient bounding cases. Each superelliptic case is compared to a respective plateau and gradient case where simple acoustic timescale characterisation may be performed. It is shown that hot spots equivalent with excitation-to-acoustic timescale ratios sufficiently greater than unity exhibit behaviour very similar to a simple plateau-gradient model. However, for larger hot spots with timescale ratios sufficiently less than unity the reaction behaviour is highly dependent on the smooth temperature profile contained within the core region.

Hyperspectral and multispectral data fusion based on linear-quadratic nonnegative matrix factorization

NASA Astrophysics Data System (ADS)

Benhalouche, Fatima Zohra; Karoui, Moussa Sofiane; Deville, Yannick; Ouamri, Abdelaziz

2017-04-01

This paper proposes three multisharpening approaches to enhance the spatial resolution of urban hyperspectral remote sensing images. These approaches, related to linear-quadratic spectral unmixing techniques, use a linear-quadratic nonnegative matrix factorization (NMF) multiplicative algorithm. These methods begin by unmixing the observable high-spectral/low-spatial resolution hyperspectral and high-spatial/low-spectral resolution multispectral images. The obtained high-spectral/high-spatial resolution features are then recombined, according to the linear-quadratic mixing model, to obtain an unobservable multisharpened high-spectral/high-spatial resolution hyperspectral image. In the first designed approach, hyperspectral and multispectral variables are independently optimized, once they have been coherently initialized. These variables are alternately updated in the second designed approach. In the third approach, the considered hyperspectral and multispectral variables are jointly updated. Experiments, using synthetic and real data, are conducted to assess the efficiency, in spatial and spectral domains, of the designed approaches and of linear NMF-based approaches from the literature. Experimental results show that the designed methods globally yield very satisfactory spectral and spatial fidelities for the multisharpened hyperspectral data. They also prove that these methods significantly outperform the used literature approaches.

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.

Modelling strong seismic ground motion: three-dimensional loading path versus wavefield polarization

NASA Astrophysics Data System (ADS)

Santisi d'Avila, Maria Paola; Lenti, Luca; Semblat, Jean-François

2012-09-01

Seismic waves due to strong earthquakes propagating in surficial soil layers may both reduce soil stiffness and increase the energy dissipation into the soil. To investigate seismic wave amplification in such cases, past studies have been devoted to one-directional shear wave propagation in a soil column (1D-propagation) considering one motion component only (1C-polarization). Three independent purely 1C computations may be performed ('1D-1C' approach) and directly superimposed in the case of weak motions (linear behaviour). This research aims at studying local site effects by considering seismic wave propagation in a 1-D soil profile accounting for the influence of the 3-D loading path and non-linear hysteretic behaviour of the soil. In the proposed '1D-3C' approach, the three components (3C-polarization) of the incident wave are simultaneously propagated into a horizontal multilayered soil. A 3-D non-linear constitutive relation for the soil is implemented in the framework of the Finite Element Method in the time domain. The complex rheology of soils is modelled by mean of a multisurface cyclic plasticity model of the Masing-Prandtl-Ishlinskii-Iwan type. The great advantage of this choice is that the only data needed to describe the model is the modulus reduction curve. A parametric study is carried out to characterize the changes in the seismic motion of the surficial layers due to both incident wavefield properties and soil non-linearities. The numerical simulations show a seismic response depending on several parameters such as polarization of seismic waves, material elastic and dynamic properties, as well as on the impedance contrast between layers and frequency content and oscillatory character of the input motion. The 3-D loading path due to the 3C-polarization leads to multi-axial stress interaction that reduces soil strength and increases non-linear effects. The non-linear behaviour of the soil may have beneficial or detrimental effects on the seismic response at the free surface, depending on the energy dissipation rate. Free surface time histories, stress-strain hysteresis loops and in-depth profiles of octahedral stress and strain are estimated for each soil column. The combination of three separate 1D-1C non-linear analyses is compared to the proposed 1D-3C approach, evidencing the influence of the 3C-polarization and the 3-D loading path on strong seismic motions.

BDNF gene delivery within and beyond templated agarose multi-channel guidance scaffolds enhances peripheral nerve regeneration

NASA Astrophysics Data System (ADS)

Gao, Mingyong; Lu, Paul; Lynam, Dan; Bednark, Bridget; Campana, W. Marie; Sakamoto, Jeff; Tuszynski, Mark

2016-12-01

Objective. We combined implantation of multi-channel templated agarose scaffolds with growth factor gene delivery to examine whether this combinatorial treatment can enhance peripheral axonal regeneration through long sciatic nerve gaps. Approach. 15 mm long scaffolds were templated into highly organized, strictly linear channels, mimicking the linear organization of natural nerves into fascicles of related function. Scaffolds were filled with syngeneic bone marrow stromal cells (MSCs) secreting the growth factor brain derived neurotrophic factor (BDNF), and lentiviral vectors expressing BDNF were injected into the sciatic nerve segment distal to the scaffold implantation site. Main results. Twelve weeks after injury, scaffolds supported highly linear regeneration of host axons across the 15 mm lesion gap. The incorporation of BDNF-secreting cells into scaffolds significantly increased axonal regeneration, and additional injection of viral vectors expressing BDNF into the distal segment of the transected nerve significantly enhanced axonal regeneration beyond the lesion. Significance. Combinatorial treatment with multichannel bioengineered scaffolds and distal growth factor delivery significantly improves peripheral nerve repair, rivaling the gold standard of autografts.

Mixed models, linear dependency, and identification in age-period-cohort models.

PubMed

O'Brien, Robert M

2017-07-20

This paper examines the identification problem in age-period-cohort models that use either linear or categorically coded ages, periods, and cohorts or combinations of these parameterizations. These models are not identified using the traditional fixed effect regression model approach because of a linear dependency between the ages, periods, and cohorts. However, these models can be identified if the researcher introduces a single just identifying constraint on the model coefficients. The problem with such constraints is that the results can differ substantially depending on the constraint chosen. Somewhat surprisingly, age-period-cohort models that specify one or more of ages and/or periods and/or cohorts as random effects are identified. This is the case without introducing an additional constraint. I label this identification as statistical model identification and show how statistical model identification comes about in mixed models and why which effects are treated as fixed and which are treated as random can substantially change the estimates of the age, period, and cohort effects. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Transgender women of color: discrimination and depression symptoms

PubMed Central

Jefferson, Kevin; Neilands, Torsten B.; Sevelius, Jae

2014-01-01

Purpose Trans women of color contend with multiple marginalizations; the purpose of this study is to examine associations between experiencing discriminatory (racist/transphobic) events and depression symptoms. It uses a categorical measure of combined discrimination, and examines a protective association of transgender identity on depression symptoms. Design/methodology/approach Data from a subset of trans women of color participants in the Sheroes study were analyzed with linear and logistic regression. Associations of depression symptoms with racist and transphobic events, combined discrimination, coping self-efficacy, and transgender identity were assessed with odds ratios. Findings Exposure to discriminatory events and combined discrimination positively associated with depression symptom odds. Increased transgender identity associated with increased coping self-efficacy, which negatively associated with depression symptom odds. Research limitations/implications Cross-sectional study data prohibits inferring causality; results support conducting longitudinal research on discrimination’s health effects, and research on transgender identity. Results also support operationalizing intersectionality in health research. The study’s categorical approach to combined discrimination may be replicable in studies with hard to reach populations and small sample sizes. Practical implications Health programs could pursue psychosocial interventions and anti-discrimination campaigns. Interventions might advocate increasing participants’ coping self-efficacy while providing space to explore and develop social identity. Social implications There is a need for policy and health programs to center trans women of color concerns. Originality/value This study examines combined discrimination and identity in relation to depression symptoms among trans women of color, an underserved population. Paper type Research paper PMID:25346778

An efficient linear-scaling CCSD(T) method based on local natural orbitals.

PubMed

Rolik, Zoltán; Szegedy, Lóránt; Ladjánszki, István; Ladóczki, Bence; Kállay, Mihály

2013-09-07

An improved version of our general-order local coupled-cluster (CC) approach [Z. Rolik and M. Kállay, J. Chem. Phys. 135, 104111 (2011)] and its efficient implementation at the CC singles and doubles with perturbative triples [CCSD(T)] level is presented. The method combines the cluster-in-molecule approach of Li and co-workers [J. Chem. Phys. 131, 114109 (2009)] with frozen natural orbital (NO) techniques. To break down the unfavorable fifth-power scaling of our original approach a two-level domain construction algorithm has been developed. First, an extended domain of localized molecular orbitals (LMOs) is assembled based on the spatial distance of the orbitals. The necessary integrals are evaluated and transformed in these domains invoking the density fitting approximation. In the second step, for each occupied LMO of the extended domain a local subspace of occupied and virtual orbitals is constructed including approximate second-order Mo̸ller-Plesset NOs. The CC equations are solved and the perturbative corrections are calculated in the local subspace for each occupied LMO using a highly-efficient CCSD(T) code, which was optimized for the typical sizes of the local subspaces. The total correlation energy is evaluated as the sum of the individual contributions. The computation time of our approach scales linearly with the system size, while its memory and disk space requirements are independent thereof. Test calculations demonstrate that currently our method is one of the most efficient local CCSD(T) approaches and can be routinely applied to molecules of up to 100 atoms with reasonable basis sets.

On approaches to analyze the sensitivity of simulated hydrologic fluxes to model parameters in the community land model

DOE PAGES

Bao, Jie; Hou, Zhangshuan; Huang, Maoyi; ...

2015-12-04

Here, effective sensitivity analysis approaches are needed to identify important parameters or factors and their uncertainties in complex Earth system models composed of multi-phase multi-component phenomena and multiple biogeophysical-biogeochemical processes. In this study, the impacts of 10 hydrologic parameters in the Community Land Model on simulations of runoff and latent heat flux are evaluated using data from a watershed. Different metrics, including residual statistics, the Nash-Sutcliffe coefficient, and log mean square error, are used as alternative measures of the deviations between the simulated and field observed values. Four sensitivity analysis (SA) approaches, including analysis of variance based on the generalizedmore » linear model, generalized cross validation based on the multivariate adaptive regression splines model, standardized regression coefficients based on a linear regression model, and analysis of variance based on support vector machine, are investigated. Results suggest that these approaches show consistent measurement of the impacts of major hydrologic parameters on response variables, but with differences in the relative contributions, particularly for the secondary parameters. The convergence behaviors of the SA with respect to the number of sampling points are also examined with different combinations of input parameter sets and output response variables and their alternative metrics. This study helps identify the optimal SA approach, provides guidance for the calibration of the Community Land Model parameters to improve the model simulations of land surface fluxes, and approximates the magnitudes to be adjusted in the parameter values during parametric model optimization.« less

A Generic Multiple Reaction Monitoring Based Approach for Plant Flavonoids Profiling Using a Triple Quadrupole Linear Ion Trap Mass Spectrometry

NASA Astrophysics Data System (ADS)

Yan, Zhixiang; Lin, Ge; Ye, Yang; Wang, Yitao; Yan, Ru

2014-06-01

Flavonoids are one of the largest classes of plant secondary metabolites serving a variety of functions in plants and associating with a number of health benefits for humans. Typically, they are co-identified with many other secondary metabolites using untargeted metabolomics. The limited data quality of untargeted workflow calls for a shift from the breadth-first to the depth-first screening strategy when a specific biosynthetic pathway is focused on. Here we introduce a generic multiple reaction monitoring (MRM)-based approach for flavonoids profiling in plants using a hybrid triple quadrupole linear ion trap (QTrap) mass spectrometer. The approach includes four steps: (1) preliminary profiling of major aglycones by multiple ion monitoring triggered enhanced product ion scan (MIM-EPI); (2) glycones profiling by precursor ion triggered EPI scan (PI-EPI) of major aglycones; (3) comprehensive aglycones profiling by combining MIM-EPI and neutral loss triggered EPI scan (NL-EPI) of major glycone; (4) in-depth flavonoids profiling by MRM-EPI with elaborated MRM transitions. Particularly, incorporation of the NH3 loss and sugar elimination proved to be very informative and confirmative for flavonoids screening. This approach was applied for profiling flavonoids in Astragali radix ( Huangqi), a famous herb widely used for medicinal and nutritional purposes in China. In total, 421 flavonoids were tentatively characterized, among which less than 40 have been previously reported in this medicinal plant. This MRM-based approach provides versatility and sensitivity that required for flavonoids profiling in plants and serves as a useful tool for plant metabolomics.

A three-dimensional FEM-DEM technique for predicting the evolution of fracture in geomaterials and concrete

NASA Astrophysics Data System (ADS)

Zárate, Francisco; Cornejo, Alejandro; Oñate, Eugenio

2018-07-01

This paper extends to three dimensions (3D), the computational technique developed by the authors in 2D for predicting the onset and evolution of fracture in a finite element mesh in a simple manner based on combining the finite element method and the discrete element method (DEM) approach (Zárate and Oñate in Comput Part Mech 2(3):301-314, 2015). Once a crack is detected at an element edge, discrete elements are generated at the adjacent element vertexes and a simple DEM mechanism is considered in order to follow the evolution of the crack. The combination of the DEM with simple four-noded linear tetrahedron elements correctly captures the onset of fracture and its evolution, as shown in several 3D examples of application.

Plate and butt-weld stresses beyond elastic limit, material and structural modeling

NASA Technical Reports Server (NTRS)

Verderaime, V.

1991-01-01

Ultimate safety factors of high performance structures depend on stress behavior beyond the elastic limit, a region not too well understood. An analytical modeling approach was developed to gain fundamental insights into inelastic responses of simple structural elements. Nonlinear material properties were expressed in engineering stresses and strains variables and combined with strength of material stress and strain equations similar to numerical piece-wise linear method. Integrations are continuous which allows for more detailed solutions. Included with interesting results are the classical combined axial tension and bending load model and the strain gauge conversion to stress beyond the elastic limit. Material discontinuity stress factors in butt-welds were derived. This is a working-type document with analytical methods and results applicable to all industries of high reliability structures.

Research on biophysical evaluation of the human vestibular system

NASA Technical Reports Server (NTRS)

Young, L. R.

1974-01-01

The human vestibular function was studied by the combined approach of advanced measurement and mathematical modelling. Fundamental measurements of some physical properties of endolymph and perilymph, combined with nystagmus measurements and fluid mechanical analysis of semicircular canal function furthered the theory of canal mechanical response to angular acceleration, caloric stimulation and relating linear acceleration. The effects of adaptation seen at low frequency angular stimulation were studied and modelled to remove some shortcomings of the torsion pendulum models. Otolith function was also studied experimentally and analytically, leading to a new set of models for subjective orientation. Applications to special problems of space, including the case of rotating spacecraft were investigated and the interaction of visual and vestibular cues and their relation to proprioceptive information was explored relative to postural control.

Ranking Forestry Investments With Parametric Linear Programming

Treesearch

Paul A. Murphy

1976-01-01

Parametric linear programming is introduced as a technique for ranking forestry investments under multiple constraints; it combines the advantages of simple tanking and linear programming as capital budgeting tools.

New approach for simulating groundwater flow in discrete fracture network

NASA Astrophysics Data System (ADS)

Fang, H.; Zhu, J.

2017-12-01

In this study, we develop a new approach to calculate groundwater flowrate and hydraulic head distribution in two-dimensional discrete fracture network (DFN) where both laminar and turbulent flows co-exist in individual fractures. The cubic law is used to calculate hydraulic head distribution and flow behaviors in fractures where flow is laminar, while the Forchheimer's law is used to quantify turbulent flow behaviors. Reynolds number is used to distinguish flow characteristics in individual fractures. The combination of linear and non-linear equations is solved iteratively to determine flowrates in all fractures and hydraulic heads at all intersections. We examine potential errors in both flowrate and hydraulic head from the approach of uniform flow assumption. Applying the cubic law in all fractures regardless of actual flow conditions overestimates the flowrate when turbulent flow may exist while applying the Forchheimer's law indiscriminately underestimate the flowrate when laminar flows exist in the network. The contrast of apertures of large and small fractures in the DFN has significant impact on the potential errors of using only the cubic law or the Forchheimer's law. Both the cubic law and Forchheimer's law simulate similar hydraulic head distributions as the main difference between these two approaches lies in predicting different flowrates. Fracture irregularity does not significantly affect the potential errors from using only the cubic law or the Forchheimer's law if network configuration remains similar. Relative density of fractures does not significantly affect the relative performance of the cubic law and Forchheimer's law.

Development and parallelization of a direct numerical simulation to study the formation and transport of nanoparticle clusters in a viscous fluid

NASA Astrophysics Data System (ADS)

Sloan, Gregory James

The direct numerical simulation (DNS) offers the most accurate approach to modeling the behavior of a physical system, but carries an enormous computation cost. There exists a need for an accurate DNS to model the coupled solid-fluid system seen in targeted drug delivery (TDD), nanofluid thermal energy storage (TES), as well as other fields where experiments are necessary, but experiment design may be costly. A parallel DNS can greatly reduce the large computation times required, while providing the same results and functionality of the serial counterpart. A D2Q9 lattice Boltzmann method approach was implemented to solve the fluid phase. The use of domain decomposition with message passing interface (MPI) parallelism resulted in an algorithm that exhibits super-linear scaling in testing, which may be attributed to the caching effect. Decreased performance on a per-node basis for a fixed number of processes confirms this observation. A multiscale approach was implemented to model the behavior of nanoparticles submerged in a viscous fluid, and used to examine the mechanisms that promote or inhibit clustering. Parallelization of this model using a masterworker algorithm with MPI gives less-than-linear speedup for a fixed number of particles and varying number of processes. This is due to the inherent inefficiency of the master-worker approach. Lastly, these separate simulations are combined, and two-way coupling is implemented between the solid and fluid.

A systematic approach to designing statistically powerful heteroscedastic 2 × 2 factorial studies while minimizing financial costs.

PubMed

Jan, Show-Li; Shieh, Gwowen

2016-08-31

The 2 × 2 factorial design is widely used for assessing the existence of interaction and the extent of generalizability of two factors where each factor had only two levels. Accordingly, research problems associated with the main effects and interaction effects can be analyzed with the selected linear contrasts. To correct for the potential heterogeneity of variance structure, the Welch-Satterthwaite test is commonly used as an alternative to the t test for detecting the substantive significance of a linear combination of mean effects. This study concerns the optimal allocation of group sizes for the Welch-Satterthwaite test in order to minimize the total cost while maintaining adequate power. The existing method suggests that the optimal ratio of sample sizes is proportional to the ratio of the population standard deviations divided by the square root of the ratio of the unit sampling costs. Instead, a systematic approach using optimization technique and screening search is presented to find the optimal solution. Numerical assessments revealed that the current allocation scheme generally does not give the optimal solution. Alternatively, the suggested approaches to power and sample size calculations give accurate and superior results under various treatment and cost configurations. The proposed approach improves upon the current method in both its methodological soundness and overall performance. Supplementary algorithms are also developed to aid the usefulness and implementation of the recommended technique in planning 2 × 2 factorial designs.

Semi-blind Bayesian inference of CMB map and power spectrum

NASA Astrophysics Data System (ADS)

Vansyngel, Flavien; Wandelt, Benjamin D.; Cardoso, Jean-François; Benabed, Karim

2016-04-01

We present a new blind formulation of the cosmic microwave background (CMB) inference problem. The approach relies on a phenomenological model of the multifrequency microwave sky without the need for physical models of the individual components. For all-sky and high resolution data, it unifies parts of the analysis that had previously been treated separately such as component separation and power spectrum inference. We describe an efficient sampling scheme that fully explores the component separation uncertainties on the inferred CMB products such as maps and/or power spectra. External information about individual components can be incorporated as a prior giving a flexible way to progressively and continuously introduce physical component separation from a maximally blind approach. We connect our Bayesian formalism to existing approaches such as Commander, spectral mismatch independent component analysis (SMICA), and internal linear combination (ILC), and discuss possible future extensions.

Predicting catalyst-support interactions between metal nanoparticles and amorphous silica supports

NASA Astrophysics Data System (ADS)

Ewing, Christopher S.; Veser, Götz; McCarthy, Joseph J.; Lambrecht, Daniel S.; Johnson, J. Karl

2016-10-01

Metal-support interactions significantly affect the stability and activity of supported catalytic nanoparticles (NPs), yet there is no simple and reliable method for estimating NP-support interactions, especially for amorphous supports. We present an approach for rapid prediction of catalyst-support interactions between Pt NPs and amorphous silica supports for NPs of various sizes and shapes. We use density functional theory calculations of 13 atom Pt clusters on model amorphous silica supports to determine linear correlations relating catalyst properties to NP-support interactions. We show that these correlations can be combined with fast discrete element method simulations to predict adhesion energy and NP net charge for NPs of larger sizes and different shapes. Furthermore, we demonstrate that this approach can be successfully transferred to Pd, Au, Ni, and Fe NPs. This approach can be used to quickly screen stability and net charge transfer and leads to a better fundamental understanding of catalyst-support interactions.

A square root ensemble Kalman filter application to a motor-imagery brain-computer interface

PubMed Central

Kamrunnahar, M.; Schiff, S. J.

2017-01-01

We here investigated a non-linear ensemble Kalman filter (SPKF) application to a motor imagery brain computer interface (BCI). A square root central difference Kalman filter (SR-CDKF) was used as an approach for brain state estimation in motor imagery task performance, using scalp electroencephalography (EEG) signals. Healthy human subjects imagined left vs. right hand movements and tongue vs. bilateral toe movements while scalp EEG signals were recorded. Offline data analysis was conducted for training the model as well as for decoding the imagery movements. Preliminary results indicate the feasibility of this approach with a decoding accuracy of 78%–90% for the hand movements and 70%–90% for the tongue-toes movements. Ongoing research includes online BCI applications of this approach as well as combined state and parameter estimation using this algorithm with different system dynamic models. PMID:22255799

A multi-domain spectral method for time-fractional differential equations

NASA Astrophysics Data System (ADS)

Chen, Feng; Xu, Qinwu; Hesthaven, Jan S.

2015-07-01

This paper proposes an approach for high-order time integration within a multi-domain setting for time-fractional differential equations. Since the kernel is singular or nearly singular, two main difficulties arise after the domain decomposition: how to properly account for the history/memory part and how to perform the integration accurately. To address these issues, we propose a novel hybrid approach for the numerical integration based on the combination of three-term-recurrence relations of Jacobi polynomials and high-order Gauss quadrature. The different approximations used in the hybrid approach are justified theoretically and through numerical examples. Based on this, we propose a new multi-domain spectral method for high-order accurate time integrations and study its stability properties by identifying the method as a generalized linear method. Numerical experiments confirm hp-convergence for both time-fractional differential equations and time-fractional partial differential equations.

Predictability of Extreme Climate Events via a Complex Network Approach

NASA Astrophysics Data System (ADS)

Muhkin, D.; Kurths, J.

2017-12-01

We analyse climate dynamics from a complex network approach. This leads to an inverse problem: Is there a backbone-like structure underlying the climate system? For this we propose a method to reconstruct and analyze a complex network from data generated by a spatio-temporal dynamical system. This approach enables us to uncover relations to global circulation patterns in oceans and atmosphere. This concept is then applied to Monsoon data; in particular, we develop a general framework to predict extreme events by combining a non-linear synchronization technique with complex networks. Applying this method, we uncover a new mechanism of extreme floods in the eastern Central Andes which could be used for operational forecasts. Moreover, we analyze the Indian Summer Monsoon (ISM) and identify two regions of high importance. By estimating an underlying critical point, this leads to an improved prediction of the onset of the ISM; this scheme was successful in 2016 and 2017.

Multi-image encryption based on synchronization of chaotic lasers and iris authentication

NASA Astrophysics Data System (ADS)

Banerjee, Santo; Mukhopadhyay, Sumona; Rondoni, Lamberto

2012-07-01

A new technique of transmitting encrypted combinations of gray scaled and chromatic images using chaotic lasers derived from Maxwell-Bloch's equations has been proposed. This novel scheme utilizes the general method of solution of a set of linear equations to transmit similar sized heterogeneous images which are a combination of monochrome and chromatic images. The chaos encrypted gray scaled images are concatenated along the three color planes resulting in color images. These are then transmitted over a secure channel along with a cover image which is an iris scan. The entire cryptology is augmented with an iris-based authentication scheme. The secret messages are retrieved once the authentication is successful. The objective of our work is briefly outlined as (a) the biometric information is the iris which is encrypted before transmission, (b) the iris is used for personal identification and verifying for message integrity, (c) the information is transmitted securely which are colored images resulting from a combination of gray images, (d) each of the images transmitted are encrypted through chaos based cryptography, (e) these encrypted multiple images are then coupled with the iris through linear combination of images before being communicated over the network. The several layers of encryption together with the ergodicity and randomness of chaos render enough confusion and diffusion properties which guarantee a fool-proof approach in achieving secure communication as demonstrated by exhaustive statistical methods. The result is vital from the perspective of opening a fundamental new dimension in multiplexing and simultaneous transmission of several monochromatic and chromatic images along with biometry based authentication and cryptography.

A refinement of the combination equations for evaporation

USGS Publications Warehouse

Milly, P.C.D.

1991-01-01

Most combination equations for evaporation rely on a linear expansion of the saturation vapor-pressure curve around the air temperature. Because the temperature at the surface may differ from this temperature by several degrees, and because the saturation vapor-pressure curve is nonlinear, this approximation leads to a certain degree of error in those evaporation equations. It is possible, however, to introduce higher-order polynomial approximations for the saturation vapor-pressure curve and to derive a family of explicit equations for evaporation, having any desired degree of accuracy. Under the linear approximation, the new family of equations for evaporation reduces, in particular cases, to the combination equations of H. L. Penman (Natural evaporation from open water, bare soil and grass, Proc. R. Soc. London, Ser. A193, 120-145, 1948) and of subsequent workers. Comparison of the linear and quadratic approximations leads to a simple approximate expression for the error associated with the linear case. Equations based on the conventional linear approximation consistently underestimate evaporation, sometimes by a substantial amount. ?? 1991 Kluwer Academic Publishers.

Pleiotropy Analysis of Quantitative Traits at Gene Level by Multivariate Functional Linear Models

PubMed Central

Wang, Yifan; Liu, Aiyi; Mills, James L.; Boehnke, Michael; Wilson, Alexander F.; Bailey-Wilson, Joan E.; Xiong, Momiao; Wu, Colin O.; Fan, Ruzong

2015-01-01

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilks’s Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. PMID:25809955

Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models.

PubMed

Wang, Yifan; Liu, Aiyi; Mills, James L; Boehnke, Michael; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao; Wu, Colin O; Fan, Ruzong

2015-05-01

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. © 2015 WILEY PERIODICALS, INC.

Topology Counts: Force Distributions in Circular Spring Networks.

PubMed

Heidemann, Knut M; Sageman-Furnas, Andrew O; Sharma, Abhinav; Rehfeldt, Florian; Schmidt, Christoph F; Wardetzky, Max

2018-02-09

Filamentous polymer networks govern the mechanical properties of many biological materials. Force distributions within these networks are typically highly inhomogeneous, and, although the importance of force distributions for structural properties is well recognized, they are far from being understood quantitatively. Using a combination of probabilistic and graph-theoretical techniques, we derive force distributions in a model system consisting of ensembles of random linear spring networks on a circle. We show that characteristic quantities, such as the mean and variance of the force supported by individual springs, can be derived explicitly in terms of only two parameters: (i) average connectivity and (ii) number of nodes. Our analysis shows that a classical mean-field approach fails to capture these characteristic quantities correctly. In contrast, we demonstrate that network topology is a crucial determinant of force distributions in an elastic spring network. Our results for 1D linear spring networks readily generalize to arbitrary dimensions.

An experimental and analytical investigation of stall effects on flap-lag stability in forward flight

NASA Technical Reports Server (NTRS)

Nagabhushanam, J.; Gaonkar, Gopal H.; Mcnulty, Michael J.

1987-01-01

Experiments have been performed with a 1.62 m diameter hingeless rotor in a wind tunnel to investigate flap-lag stability of isolated rotors in forward flight. The three-bladed rotor model closely approaches the simple theoretical concept of a hingeless rotor as a set of rigid, articulated flap-lag blades with offset and spring restrained flap and lag hinges. Lag regressing mode stability data was obtained for advance ratios as high as 0.55 for various combinations of collective pitch and shaft angle. The prediction includes quasi-steady stall effects on rotor trim and Floquet stability analyses. Correlation between data and prediction is presented and is compared with that of an earlier study based on a linear theory without stall effects. While the results with stall effects show marked differences from the linear theory results, the stall theory still falls short of adequate agreement with the experimental data.

Conformal drug delivery and instantaneous monitoring based on an inverse synthesis method at a diagnostic ultrasound platform

NASA Astrophysics Data System (ADS)

Xu, Shanshan; Zong, Yujin; Liu, Xiaodong; Lu, Mingzhu; Wan, Mingxi

2017-03-01

In this paper, based on a programmable diagnostic ultrasound scanner, a combined approach was proposed, in which a variable-sized focal region wherein the acoustic pressure is above the ultrasound contrast agents (UCA) fragmentation threshold is synthesized by reasonably matching the excitation voltage and the transmit aperture of the linear array at 5MHz, the UCAs' temporal and spatial distribution before and after the microbubbles fragmentation is monitored using the plane-wave transmission and reception at 400Hz and, simultaneously, the broadband noise emission during the microbubbles fragmentation is extracted using the backscattering of focused release bursts (destruction pulse) themselves on the linear array. Then, acquired radio frequency (RF) data are processed to draw parameters which can be correlated with the indicator of broadband noise emission level, namely inertial cavitation dose (ICD) and microbubble fragmentation efficiency, namely decay rate of microbubbles.

Programmable multimode quantum networks

PubMed Central

Armstrong, Seiji; Morizur, Jean-François; Janousek, Jiri; Hage, Boris; Treps, Nicolas; Lam, Ping Koy; Bachor, Hans-A.

2012-01-01

Entanglement between large numbers of quantum modes is the quintessential resource for future technologies such as the quantum internet. Conventionally, the generation of multimode entanglement in optics requires complex layouts of beamsplitters and phase shifters in order to transform the input modes into entangled modes. Here we report the highly versatile and efficient generation of various multimode entangled states with the ability to switch between different linear optics networks in real time. By defining our modes to be combinations of different spatial regions of one beam, we may use just one pair of multi-pixel detectors in order to measure multiple entangled modes. We programme virtual networks that are fully equivalent to the physical linear optics networks they are emulating. We present results for N=2 up to N=8 entangled modes here, including N=2, 3, 4 cluster states. Our approach introduces the highly sought after attributes of flexibility and scalability to multimode entanglement. PMID:22929783

Tunable microwave generation based on frequency quadrupling

NASA Astrophysics Data System (ADS)

Liu, Yu-Lei; Liang, Jun; Li, Xuan; Xiao, Nan; Yuan, Xiao-Gang

2018-07-01

To generate linearly chirped microwave signals with large frequency tunable range, a photonic approach is proposed. A dual-output dual-parallel Mach-Zehnder modulator followed by a polarisation beam combiner and an optical filter are utilised to generate orthogonally polarised ± second-order optical sidebands. A polarisation modulator is employed to achieve phase modulation of the two wavelengths. The balanced detection is applied to suppress the distortion and background noise. The central frequency of the generated signal is four times that of the local oscillator frequency. Simulation results show that a linear pulse is produced with time-bandwidth as well as a compression ratio for the pulse of 11 and 9.3 respectively. Moreover, a peak-to-sidelobe ratio of 7.4 dB is generated. The system has both good reconfigurability and tunability, and its frequency can be continuously adjusted from about 10 GHz to as much as 50 GHz in principle.

Controllability of Free-piston Stirling Engine/linear Alternator Driving a Dynamic Load

NASA Technical Reports Server (NTRS)

Kankam, M. David; Rauch, Jeffrey S.

1994-01-01

This paper presents the dynamic behavior of a Free-Piston Stirling Engine/linear alternator (FPSE/LA) driving a single-phase fractional horse-power induction motor. The controllability and dynamic stability of the system are discussed by means of sensitivity effects of variations in system parameters, engine controller, operating conditions, and mechanical loading on the induction motor. The approach used expands on a combined mechanical and thermodynamic formulation employed in a previous paper. The application of state-space technique and frequency domain analysis enhances understanding of the dynamic interactions. Engine-alternator parametric sensitivity studies, similar to those of the previous paper, are summarized. Detailed discussions are provided for parametric variations which relate to the engine controller and system operating conditions. The results suggest that the controllability of a FPSE-based power system is enhanced by proper operating conditions and built-in controls.

Automated discrimination of dementia spectrum disorders using extreme learning machine and structural T1 MRI features.

PubMed

Jongin Kim; Boreom Lee

2017-07-01

The classification of neuroimaging data for the diagnosis of Alzheimer's Disease (AD) is one of the main research goals of the neuroscience and clinical fields. In this study, we performed extreme learning machine (ELM) classifier to discriminate the AD, mild cognitive impairment (MCI) from normal control (NC). We compared the performance of ELM with that of a linear kernel support vector machine (SVM) for 718 structural MRI images from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The data consisted of normal control, MCI converter (MCI-C), MCI non-converter (MCI-NC), and AD. We employed SVM-based recursive feature elimination (RFE-SVM) algorithm to find the optimal subset of features. In this study, we found that the RFE-SVM feature selection approach in combination with ELM shows the superior classification accuracy to that of linear kernel SVM for structural T1 MRI data.

Probabilistic vs linear blending approaches to shared control for wheelchair driving.

PubMed

Ezeh, Chinemelu; Trautman, Pete; Devigne, Louise; Bureau, Valentin; Babel, Marie; Carlson, Tom

2017-07-01

Some people with severe mobility impairments are unable to operate powered wheelchairs reliably and effectively, using commercially available interfaces. This has sparked a body of research into "smart wheelchairs", which assist users to drive safely and create opportunities for them to use alternative interfaces. Various "shared control" techniques have been proposed to provide an appropriate level of assistance that is satisfactory and acceptable to the user. Most shared control techniques employ a traditional strategy called linear blending (LB), where the user's commands and wheelchair's autonomous commands are combined in some proportion. In this paper, however, we implement a more generalised form of shared control called probabilistic shared control (PSC). This probabilistic formulation improves the accuracy of modelling the interaction between the user and the wheelchair by taking into account uncertainty in the interaction. In this paper, we demonstrate the practical success of PSC over LB in terms of safety, particularly for novice users.

Neural-Based Compensation of Nonlinearities in an Airplane Longitudinal Model with Dynamic-Inversion Control

PubMed Central

Li, YuHui; Jin, FeiTeng

2017-01-01

The inversion design approach is a very useful tool for the complex multiple-input-multiple-output nonlinear systems to implement the decoupling control goal, such as the airplane model and spacecraft model. In this work, the flight control law is proposed using the neural-based inversion design method associated with the nonlinear compensation for a general longitudinal model of the airplane. First, the nonlinear mathematic model is converted to the equivalent linear model based on the feedback linearization theory. Then, the flight control law integrated with this inversion model is developed to stabilize the nonlinear system and relieve the coupling effect. Afterwards, the inversion control combined with the neural network and nonlinear portion is presented to improve the transient performance and attenuate the uncertain effects on both external disturbances and model errors. Finally, the simulation results demonstrate the effectiveness of this controller. PMID:29410680

LONG RANGE REGULATION OF V(D)J RECOMBINATION

PubMed Central

Proudhon, Charlotte; Hao, Bingtao; Raviram, Ramya; Chaumeil, Julie; Skok, Jane A.

2015-01-01

Given their essential role in adaptive immunity, antigen receptor loci have been the focus of analysis for many years and are among a handful of the most well studied genes in the genome. Their investigation led initially to a detailed knowledge of linear structure and characterization of regulatory elements that confer control of their rearrangement and expression. However, advances in DNA FISH and imaging combined with new molecular approaches that interrogate chromosome conformation have led to a growing appreciation that linear structure is only one aspect of gene regulation and in more recent years the focus has switched to analyzing the impact of locus conformation and nuclear organization on control of recombination. Despite decades of work and intense effort from numerous labs we are still left with an incomplete picture of how antigen receptor loci are regulated. This chapter summarizes our advances to date and points to areas that need further investigation. PMID:26477367

Integral Sliding Mode Fault-Tolerant Control for Uncertain Linear Systems Over Networks With Signals Quantization.

PubMed

Hao, Li-Ying; Park, Ju H; Ye, Dan

2017-09-01

In this paper, a new robust fault-tolerant compensation control method for uncertain linear systems over networks is proposed, where only quantized signals are assumed to be available. This approach is based on the integral sliding mode (ISM) method where two kinds of integral sliding surfaces are constructed. One is the continuous-state-dependent surface with the aim of sliding mode stability analysis and the other is the quantization-state-dependent surface, which is used for ISM controller design. A scheme that combines the adaptive ISM controller and quantization parameter adjustment strategy is then proposed. Through utilizing H ∞ control analytical technique, once the system is in the sliding mode, the nature of performing disturbance attenuation and fault tolerance from the initial time can be found without requiring any fault information. Finally, the effectiveness of our proposed ISM control fault-tolerant schemes against quantization errors is demonstrated in the simulation.

A dimensionally split Cartesian cut cell method for hyperbolic conservation laws

NASA Astrophysics Data System (ADS)

Gokhale, Nandan; Nikiforakis, Nikos; Klein, Rupert

2018-07-01

We present a dimensionally split method for solving hyperbolic conservation laws on Cartesian cut cell meshes. The approach combines local geometric and wave speed information to determine a novel stabilised cut cell flux, and we provide a full description of its three-dimensional implementation in the dimensionally split framework of Klein et al. [1]. The convergence and stability of the method are proved for the one-dimensional linear advection equation, while its multi-dimensional numerical performance is investigated through the computation of solutions to a number of test problems for the linear advection and Euler equations. When compared to the cut cell flux of Klein et al., it was found that the new flux alleviates the problem of oscillatory boundary solutions produced by the former at higher Courant numbers, and also enables the computation of more accurate solutions near stagnation points. Being dimensionally split, the method is simple to implement and extends readily to multiple dimensions.

Analytical and numerical treatment of resistive drift instability in a plasma slab

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

Mirnov, V. V., E-mail: vvmirnov@wisc.edu; Sauppe, J. P.; Hegna, C. C.

An analytic approach combining the effect of equilibrium diamagnetic flows and the finite ionsound gyroradius associated with electron−ion decoupling and kinetic Alfvén wave dispersion is derived to study resistive drift instabilities in a plasma slab. Linear numerical computations using the NIMROD code are performed with cold ions and hot electrons in a plasma slab with a doubly periodic box bounded by two perfectly conducting walls. A linearly unstable resistive drift mode is observed in computations with a growth rate that is consistent with the analytic dispersion relation. The resistive drift mode is expected to be suppressed by magnetic shear inmore » unbounded domains, but the mode is observed in numerical computations with and without magnetic shear. In the slab model, the finite slab thickness and the perfectly conducting boundary conditions are likely to account for the lack of suppression.« less

Experimental investigation of a four-qubit linear-optical quantum logic circuit

PubMed Central

Stárek, R.; Mičuda, M.; Miková, M.; Straka, I.; Dušek, M.; Ježek, M.; Fiurášek, J.

2016-01-01

We experimentally demonstrate and characterize a four-qubit linear-optical quantum logic circuit. Our robust and versatile scheme exploits encoding of two qubits into polarization and path degrees of single photons and involves two crossed inherently stable interferometers. This approach allows us to design a complex quantum logic circuit that combines a genuine four-qubit C3Z gate and several two-qubit and single-qubit gates. The C3Z gate introduces a sign flip if and only if all four qubits are in the computational state |1〉. We verify high-fidelity performance of this central four-qubit gate using Hofmann bounds on quantum gate fidelity and Monte Carlo fidelity sampling. We also experimentally demonstrate that the quantum logic circuit can generate genuine multipartite entanglement and we certify the entanglement with the use of suitably tailored entanglement witnesses. PMID:27647176

Parallel robot for micro assembly with integrated innovative optical 3D-sensor

NASA Astrophysics Data System (ADS)

Hesselbach, Juergen; Ispas, Diana; Pokar, Gero; Soetebier, Sven; Tutsch, Rainer

2002-10-01

Recent advances in the fields of MEMS and MOEMS often require precise assembly of very small parts with an accuracy of a few microns. In order to meet this demand, a new approach using a robot based on parallel mechanisms in combination with a novel 3D-vision system has been chosen. The planar parallel robot structure with 2 DOF provides a high resolution in the XY-plane. It carries two additional serial axes for linear and rotational movement in/about z direction. In order to achieve high precision as well as good dynamic capabilities, the drive concept for the parallel (main) axes incorporates air bearings in combination with a linear electric servo motors. High accuracy position feedback is provided by optical encoders with a resolution of 0.1 μm. To allow for visualization and visual control of assembly processes, a camera module fits into the hollow tool head. It consists of a miniature CCD camera and a light source. In addition a modular gripper support is integrated into the tool head. To increase the accuracy a control loop based on an optoelectronic sensor will be implemented. As a result of an in-depth analysis of different approaches a photogrammetric system using one single camera and special beam-splitting optics was chosen. A pattern of elliptical marks is applied to the surfaces of workpiece and gripper. Using a model-based recognition algorithm the image processing software identifies the gripper and the workpiece and determines their relative position. A deviation vector is calculated and fed into the robot control to guide the gripper.

Importance of Vibronic Effects in the UV-Vis Spectrum of the 7,7,8,8-Tetracyanoquinodimethane Anion.

PubMed

Tapavicza, Enrico; Furche, Filipp; Sundholm, Dage

2016-10-11

We present a computational method for simulating vibronic absorption spectra in the ultraviolet-visible (UV-vis) range and apply it to the 7,7,8,8-tetracyanoquinodimethane anion (TCNQ - ), which has been used as a ligand in black absorbers. Gaussian broadening of vertical electronic excitation energies of TCNQ - from linear-response time-dependent density functional theory produces only one band, which is qualitatively incorrect. Thus, the harmonic vibrational modes of the two lowest doublet states were computed, and the vibronic UV-vis spectrum was simulated using the displaced harmonic oscillator approximation, the frequency-shifted harmonic oscillator approximation, and the full Duschinsky formalism. An efficient real-time generating function method was implemented to avoid the exponential complexity of conventional Franck-Condon approaches to vibronic spectra. The obtained UV-vis spectra for TCNQ - agree well with experiment; the Duschinsky rotation is found to have only a minor effect on the spectrum. Born-Oppenheimer molecular dynamics simulations combined with calculations of the electronic excitation energies for a large number of molecular structures were also used for simulating the UV-vis spectrum. The Born-Oppenheimer molecular dynamics simulations yield a broadening of the energetically lowest peak in the absorption spectrum, but additional vibrational bands present in the experimental and simulated quantum harmonic oscillator spectra are not observed in the molecular dynamics simulations. Our results underline the importance of vibronic effects for the UV-vis spectrum of TCNQ - , and they establish an efficient method for obtaining vibronic spectra using a combination of linear-response time-dependent density functional theory and a real-time generating function approach.

A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: I. Robust Linear Optimization and Robust Mixed Integer Linear Optimization

PubMed Central

Li, Zukui; Ding, Ran; Floudas, Christodoulos A.

2011-01-01

Robust counterpart optimization techniques for linear optimization and mixed integer linear optimization problems are studied in this paper. Different uncertainty sets, including those studied in literature (i.e., interval set; combined interval and ellipsoidal set; combined interval and polyhedral set) and new ones (i.e., adjustable box; pure ellipsoidal; pure polyhedral; combined interval, ellipsoidal, and polyhedral set) are studied in this work and their geometric relationship is discussed. For uncertainty in the left hand side, right hand side, and objective function of the optimization problems, robust counterpart optimization formulations induced by those different uncertainty sets are derived. Numerical studies are performed to compare the solutions of the robust counterpart optimization models and applications in refinery production planning and batch process scheduling problem are presented. PMID:21935263

Galerkin finite difference Laplacian operators on isolated unstructured triangular meshes by linear combinations

NASA Technical Reports Server (NTRS)

Baumeister, Kenneth J.

1990-01-01

The Galerkin weighted residual technique using linear triangular weight functions is employed to develop finite difference formulae in Cartesian coordinates for the Laplacian operator on isolated unstructured triangular grids. The weighted residual coefficients associated with the weak formulation of the Laplacian operator along with linear combinations of the residual equations are used to develop the algorithm. The algorithm was tested for a wide variety of unstructured meshes and found to give satisfactory results.

An evaluation of alternative selection indexes for a non-linear profit trait approaching its economic optimum.

PubMed

Martin-Collado, D; Byrne, T J; Visser, B; Amer, P R

2016-12-01

This study used simulation to evaluate the performance of alternative selection index configurations in the context of a breeding programme where a trait with a non-linear economic value is approaching an economic optimum. The simulation used a simple population structure that approximately mimics selection in dual purpose sheep flocks in New Zealand (NZ). In the NZ dual purpose sheep population, number of lambs born is a genetic trait that is approaching an economic optimum, while genetically correlated growth traits have linear economic values and are not approaching any optimum. The predominant view among theoretical livestock geneticists is that the optimal approach to select for nonlinear profit traits is to use a linear selection index and to update it regularly. However, there are some nonlinear index approaches that have not been evaluated. This study assessed the efficiency of the following four alternative selection index approaches in terms of genetic progress relative to each other: (i) a linear index, (ii) a linear index updated regularly, (iii) a nonlinear (quadratic) index, and (iv) a NLF index (nonlinear index below the optimum and then flat). The NLF approach does not reward or penalize animals for additional genetic merit beyond the trait optimum. It was found to be at least comparable in efficiency to the approach of regularly updating the linear index with short (15 year) and long (30 year) time frames. The relative efficiency of this approach was slightly reduced when the current average value of the nonlinear trait was close to the optimum. Finally, practical issues of industry application of indexes are considered and some potential practical benefits of efficient deployment of a NLF index in highly heterogeneous industries (breeds, flocks and production environments) such as in the NZ dual purpose sheep population are discussed. © 2016 Blackwell Verlag GmbH.

Bounding the electrostatic free energies associated with linear continuum models of molecular solvation.

PubMed

Bardhan, Jaydeep P; Knepley, Matthew G; Anitescu, Mihai

2009-03-14

The importance of electrostatic interactions in molecular biology has driven extensive research toward the development of accurate and efficient theoretical and computational models. Linear continuum electrostatic theory has been surprisingly successful, but the computational costs associated with solving the associated partial differential equations (PDEs) preclude the theory's use in most dynamical simulations. Modern generalized-Born models for electrostatics can reproduce PDE-based calculations to within a few percent and are extremely computationally efficient but do not always faithfully reproduce interactions between chemical groups. Recent work has shown that a boundary-integral-equation formulation of the PDE problem leads naturally to a new approach called boundary-integral-based electrostatics estimation (BIBEE) to approximate electrostatic interactions. In the present paper, we prove that the BIBEE method can be used to rigorously bound the actual continuum-theory electrostatic free energy. The bounds are validated using a set of more than 600 proteins. Detailed numerical results are presented for structures of the peptide met-enkephalin taken from a molecular-dynamics simulation. These bounds, in combination with our demonstration that the BIBEE methods accurately reproduce pairwise interactions, suggest a new approach toward building a highly accurate yet computationally tractable electrostatic model.

Using direct current resistivity sounding and geostatistics to aid in hydrogeological studies in the Choshuichi alluvial fan, Taiwan.

PubMed

Yang, Chieh-Hou; Lee, Wei-Feng

2002-01-01

Ground water reservoirs in the Choshuichi alluvial fan, central western Taiwan, were investigated using direct-current (DC) resistivity soundings at 190 locations, combined with hydrogeological measurements from 37 wells. In addition, attempts were made to calculate aquifer transmissivity from both surface DC resistivity measurements and geostatistically derived predictions of aquifer properties. DC resistivity sounding data are highly correlated to the hydraulic parameters in the Choshuichi alluvial fan. By estimating the spatial distribution of hydraulic conductivity from the kriged well data and the cokriged thickness of the correlative aquifer from both resistivity sounding data and well information, the transmissivity of the aquifer at each location can be obtained from the product of kriged hydraulic conductivity and computed thickness of the geoelectric layer. Thus, the spatial variation of the transmissivities in the study area is obtained. Our work is more comparable to Ahmed et al. (1988) than to the work of Niwas and Singhal (1981). The first "constraint" from Niwas and Singhal's work is a result of their use of linear regression. The geostatistical approach taken here (and by Ahmed et al. [1988]) is a natural improvement on the linear regression approach.

Bounding the electrostatic free energies associated with linear continuum models of molecular solvation

NASA Astrophysics Data System (ADS)

Bardhan, Jaydeep P.; Knepley, Matthew G.; Anitescu, Mihai

2009-03-01

The importance of electrostatic interactions in molecular biology has driven extensive research toward the development of accurate and efficient theoretical and computational models. Linear continuum electrostatic theory has been surprisingly successful, but the computational costs associated with solving the associated partial differential equations (PDEs) preclude the theory's use in most dynamical simulations. Modern generalized-Born models for electrostatics can reproduce PDE-based calculations to within a few percent and are extremely computationally efficient but do not always faithfully reproduce interactions between chemical groups. Recent work has shown that a boundary-integral-equation formulation of the PDE problem leads naturally to a new approach called boundary-integral-based electrostatics estimation (BIBEE) to approximate electrostatic interactions. In the present paper, we prove that the BIBEE method can be used to rigorously bound the actual continuum-theory electrostatic free energy. The bounds are validated using a set of more than 600 proteins. Detailed numerical results are presented for structures of the peptide met-enkephalin taken from a molecular-dynamics simulation. These bounds, in combination with our demonstration that the BIBEE methods accurately reproduce pairwise interactions, suggest a new approach toward building a highly accurate yet computationally tractable electrostatic model.

Adaptive linear rank tests for eQTL studies

PubMed Central

Szymczak, Silke; Scheinhardt, Markus O.; Zeller, Tanja; Wild, Philipp S.; Blankenberg, Stefan; Ziegler, Andreas

2013-01-01

Expression quantitative trait loci (eQTL) studies are performed to identify single-nucleotide polymorphisms that modify average expression values of genes, proteins, or metabolites, depending on the genotype. As expression values are often not normally distributed, statistical methods for eQTL studies should be valid and powerful in these situations. Adaptive tests are promising alternatives to standard approaches, such as the analysis of variance or the Kruskal–Wallis test. In a two-stage procedure, skewness and tail length of the distributions are estimated and used to select one of several linear rank tests. In this study, we compare two adaptive tests that were proposed in the literature using extensive Monte Carlo simulations of a wide range of different symmetric and skewed distributions. We derive a new adaptive test that combines the advantages of both literature-based approaches. The new test does not require the user to specify a distribution. It is slightly less powerful than the locally most powerful rank test for the correct distribution and at least as powerful as the maximin efficiency robust rank test. We illustrate the application of all tests using two examples from different eQTL studies. PMID:22933317

Adaptive linear rank tests for eQTL studies.

PubMed

Szymczak, Silke; Scheinhardt, Markus O; Zeller, Tanja; Wild, Philipp S; Blankenberg, Stefan; Ziegler, Andreas

2013-02-10

Expression quantitative trait loci (eQTL) studies are performed to identify single-nucleotide polymorphisms that modify average expression values of genes, proteins, or metabolites, depending on the genotype. As expression values are often not normally distributed, statistical methods for eQTL studies should be valid and powerful in these situations. Adaptive tests are promising alternatives to standard approaches, such as the analysis of variance or the Kruskal-Wallis test. In a two-stage procedure, skewness and tail length of the distributions are estimated and used to select one of several linear rank tests. In this study, we compare two adaptive tests that were proposed in the literature using extensive Monte Carlo simulations of a wide range of different symmetric and skewed distributions. We derive a new adaptive test that combines the advantages of both literature-based approaches. The new test does not require the user to specify a distribution. It is slightly less powerful than the locally most powerful rank test for the correct distribution and at least as powerful as the maximin efficiency robust rank test. We illustrate the application of all tests using two examples from different eQTL studies. Copyright © 2012 John Wiley & Sons, Ltd.

Generalized linear and generalized additive models in studies of species distributions: Setting the scene

USGS Publications Warehouse

Guisan, Antoine; Edwards, T.C.; Hastie, T.

2002-01-01

An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001. We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling. ?? 2002 Elsevier Science B.V. All rights reserved.

Computational principles underlying recognition of acoustic signals in grasshoppers and crickets.

PubMed

Ronacher, Bernhard; Hennig, R Matthias; Clemens, Jan

2015-01-01

Grasshoppers and crickets independently evolved hearing organs and acoustic communication. They differ considerably in the organization of their auditory pathways, and the complexity of their songs, which are essential for mate attraction. Recent approaches aimed at describing the behavioral preference functions of females in both taxa by a simple modeling framework. The basic structure of the model consists of three processing steps: (1) feature extraction with a bank of 'LN models'-each containing a linear filter followed by a nonlinearity, (2) temporal integration, and (3) linear combination. The specific properties of the filters and nonlinearities were determined using a genetic learning algorithm trained on a large set of different song features and the corresponding behavioral response scores. The model showed an excellent prediction of the behavioral responses to the tested songs. Most remarkably, in both taxa the genetic algorithm found Gabor-like functions as the optimal filter shapes. By slight modifications of Gabor filters several types of preference functions could be modeled, which are observed in different cricket species. Furthermore, this model was able to explain several so far enigmatic results in grasshoppers. The computational approach offered a remarkably simple framework that can account for phenotypically rather different preference functions across several taxa.

Discovery of Boolean metabolic networks: integer linear programming based approach.

PubMed

Qiu, Yushan; Jiang, Hao; Ching, Wai-Ki; Cheng, Xiaoqing

2018-04-11

Traditional drug discovery methods focused on the efficacy of drugs rather than their toxicity. However, toxicity and/or lack of efficacy are produced when unintended targets are affected in metabolic networks. Thus, identification of biological targets which can be manipulated to produce the desired effect with minimum side-effects has become an important and challenging topic. Efficient computational methods are required to identify the drug targets while incurring minimal side-effects. In this paper, we propose a graph-based computational damage model that summarizes the impact of enzymes on compounds in metabolic networks. An efficient method based on Integer Linear Programming formalism is then developed to identify the optimal enzyme-combination so as to minimize the side-effects. The identified target enzymes for known successful drugs are then verified by comparing the results with those in the existing literature. Side-effects reduction plays a crucial role in the study of drug development. A graph-based computational damage model is proposed and the theoretical analysis states the captured problem is NP-completeness. The proposed approaches can therefore contribute to the discovery of drug targets. Our developed software is available at " http://hkumath.hku.hk/~wkc/APBC2018-metabolic-network.zip ".

Binding free energy predictions of farnesoid X receptor (FXR) agonists using a linear interaction energy (LIE) approach with reliability estimation: application to the D3R Grand Challenge 2

NASA Astrophysics Data System (ADS)

Rifai, Eko Aditya; van Dijk, Marc; Vermeulen, Nico P. E.; Geerke, Daan P.

2018-01-01

Computational protein binding affinity prediction can play an important role in drug research but performing efficient and accurate binding free energy calculations is still challenging. In the context of phase 2 of the Drug Design Data Resource (D3R) Grand Challenge 2 we used our automated eTOX ALLIES approach to apply the (iterative) linear interaction energy (LIE) method and we evaluated its performance in predicting binding affinities for farnesoid X receptor (FXR) agonists. Efficiency was obtained by our pre-calibrated LIE models and molecular dynamics (MD) simulations at the nanosecond scale, while predictive accuracy was obtained for a small subset of compounds. Using our recently introduced reliability estimation metrics, we could classify predictions with higher confidence by featuring an applicability domain (AD) analysis in combination with protein-ligand interaction profiling. The outcomes of and agreement between our AD and interaction-profile analyses to distinguish and rationalize the performance of our predictions highlighted the relevance of sufficiently exploring protein-ligand interactions during training and it demonstrated the possibility to quantitatively and efficiently evaluate if this is achieved by using simulation data only.

Algebraic Approaches for Scalable End-to-End Monitoring and Diagnosis

NASA Astrophysics Data System (ADS)

Zhao, Yao; Chen, Yan

The rigidity of the Internet architecture led to flourish in the research of end-to-end based systems. In this chapter, we describe a linear algebra-based end-to-end monitoring and diagnosis system. We first propose a tomography-based overlay monitoring system (TOM). Given n end hosts, TOM selectively monitors a basis set of O(nlogn) paths out of all n(n - 1) end-to-end paths. Any end-to-end path can be written as a unique linear combination of paths in the basis set. Consequently, by monitoring loss rates for the paths in the basis set, TOM infers loss rates for all end-to-end paths. Furthermore, leveraging on the scalable measurements from the TOM system, we propose the Least-biased End-to-End Network Diagnosis (in short, LEND) system. We define a minimal identifiable link sequence (MILS) as a link sequence of minimal length whose properties can be uniquely identified from end-to-end measurements. LEND applies an algebraic approach to find out the MILSes and infers the properties of the MILSes efficiently. This also means LEND system achieves the finest diagnosis granularity under the least biased statistical assumptions.

Cross-entropy embedding of high-dimensional data using the neural gas model.

PubMed

Estévez, Pablo A; Figueroa, Cristián J; Saito, Kazumi

2005-01-01

A cross-entropy approach to mapping high-dimensional data into a low-dimensional space embedding is presented. The method allows to project simultaneously the input data and the codebook vectors, obtained with the Neural Gas (NG) quantizer algorithm, into a low-dimensional output space. The aim of this approach is to preserve the relationship defined by the NG neighborhood function for each pair of input and codebook vectors. A cost function based on the cross-entropy between input and output probabilities is minimized by using a Newton-Raphson method. The new approach is compared with Sammon's non-linear mapping (NLM) and the hierarchical approach of combining a vector quantizer such as the self-organizing feature map (SOM) or NG with the NLM recall algorithm. In comparison with these techniques, our method delivers a clear visualization of both data points and codebooks, and it achieves a better mapping quality in terms of the topology preservation measure q(m).

Phase Space Dissimilarity Measures for Structural Health Monitoring

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

Bubacz, Jacob A; Chmielewski, Hana T; Pape, Alexander E

A novel method for structural health monitoring (SHM), known as the Phase Space Dissimilarity Measures (PSDM) approach, is proposed and developed. The patented PSDM approach has already been developed and demonstrated for a variety of equipment and biomedical applications. Here, we investigate SHM of bridges via analysis of time serial accelerometer measurements. This work has four aspects. The first is algorithm scalability, which was found to scale linearly from one processing core to four cores. Second, the same data are analyzed to determine how the use of the PSDM approach affects sensor placement. We found that a relatively low-density placementmore » sufficiently captures the dynamics of the structure. Third, the same data are analyzed by unique combinations of accelerometer axes (vertical, longitudinal, and lateral with respect to the bridge) to determine how the choice of axes affects the analysis. The vertical axis is found to provide satisfactory SHM data. Fourth, statistical methods were investigated to validate the PSDM approach for this application, yielding statistically significant results.« less

Lattice dynamics approach to determine the dependence of the time-of-flight of transversal polarized acoustic waves on external stress

NASA Astrophysics Data System (ADS)

Tarar, K. S.; Pluta, M.; Amjad, U.; Grill, W.

2011-04-01

Based on the lattice dynamics approach the dependence of the time-of-flight (TOF) on stress has been modeled for transversal polarized acoustic waves. The relevant dispersion relation is derived from the appropriate mass-spring model together with the dependencies on the restoring forces including the effect of externally applied stress. The lattice dynamics approach can also be interpreted as a discrete and strictly periodic lumped circuit. In that case the modeling represents a finite element approach. In both cases the properties relevant for wavelengths large with respect to the periodic structure can be derived from the respective limit relating also to low frequencies. The model representing a linear chain with stiffness to shear and additional stiffness introduced by extensional stress is presented and compared to existing models, which so far represent each only one of the effects treated here in combination. For a string this effect is well known from musical instruments. The counteracting effects are discussed and compared to experimental results.

Image-guided filtering for improving photoacoustic tomographic image reconstruction.

PubMed

Awasthi, Navchetan; Kalva, Sandeep Kumar; Pramanik, Manojit; Yalavarthy, Phaneendra K

2018-06-01

Several algorithms exist to solve the photoacoustic image reconstruction problem depending on the expected reconstructed image features. These reconstruction algorithms promote typically one feature, such as being smooth or sharp, in the output image. Combining these features using a guided filtering approach was attempted in this work, which requires an input and guiding image. This approach act as a postprocessing step to improve commonly used Tikhonov or total variational regularization method. The result obtained from linear backprojection was used as a guiding image to improve these results. Using both numerical and experimental phantom cases, it was shown that the proposed guided filtering approach was able to improve (as high as 11.23 dB) the signal-to-noise ratio of the reconstructed images with the added advantage being computationally efficient. This approach was compared with state-of-the-art basis pursuit deconvolution as well as standard denoising methods and shown to outperform them. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Efficient Implementation of an Optimal Interpolator for Large Spatial Data Sets

NASA Technical Reports Server (NTRS)

Memarsadeghi, Nargess; Mount, David M.

2007-01-01

Interpolating scattered data points is a problem of wide ranging interest. A number of approaches for interpolation have been proposed both from theoretical domains such as computational geometry and in applications' fields such as geostatistics. Our motivation arises from geological and mining applications. In many instances data can be costly to compute and are available only at nonuniformly scattered positions. Because of the high cost of collecting measurements, high accuracy is required in the interpolants. One of the most popular interpolation methods in this field is called ordinary kriging. It is popular because it is a best linear unbiased estimator. The price for its statistical optimality is that the estimator is computationally very expensive. This is because the value of each interpolant is given by the solution of a large dense linear system. In practice, kriging problems have been solved approximately by restricting the domain to a small local neighborhood of points that lie near the query point. Determining the proper size for this neighborhood is a solved by ad hoc methods, and it has been shown that this approach leads to undesirable discontinuities in the interpolant. Recently a more principled approach to approximating kriging has been proposed based on a technique called covariance tapering. This process achieves its efficiency by replacing the large dense kriging system with a much sparser linear system. This technique has been applied to a restriction of our problem, called simple kriging, which is not unbiased for general data sets. In this paper we generalize these results by showing how to apply covariance tapering to the more general problem of ordinary kriging. Through experimentation we demonstrate the space and time efficiency and accuracy of approximating ordinary kriging through the use of covariance tapering combined with iterative methods for solving large sparse systems. We demonstrate our approach on large data sizes arising both from synthetic sources and from real applications.

Integrative approach for inference of gene regulatory networks using lasso-based random featuring and application to psychiatric disorders.

PubMed

Kim, Dongchul; Kang, Mingon; Biswas, Ashis; Liu, Chunyu; Gao, Jean

2016-08-10

Inferring gene regulatory networks is one of the most interesting research areas in the systems biology. Many inference methods have been developed by using a variety of computational models and approaches. However, there are two issues to solve. First, depending on the structural or computational model of inference method, the results tend to be inconsistent due to innately different advantages and limitations of the methods. Therefore the combination of dissimilar approaches is demanded as an alternative way in order to overcome the limitations of standalone methods through complementary integration. Second, sparse linear regression that is penalized by the regularization parameter (lasso) and bootstrapping-based sparse linear regression methods were suggested in state of the art methods for network inference but they are not effective for a small sample size data and also a true regulator could be missed if the target gene is strongly affected by an indirect regulator with high correlation or another true regulator. We present two novel network inference methods based on the integration of three different criteria, (i) z-score to measure the variation of gene expression from knockout data, (ii) mutual information for the dependency between two genes, and (iii) linear regression-based feature selection. Based on these criterion, we propose a lasso-based random feature selection algorithm (LARF) to achieve better performance overcoming the limitations of bootstrapping as mentioned above. In this work, there are three main contributions. First, our z score-based method to measure gene expression variations from knockout data is more effective than similar criteria of related works. Second, we confirmed that the true regulator selection can be effectively improved by LARF. Lastly, we verified that an integrative approach can clearly outperform a single method when two different methods are effectively jointed. In the experiments, our methods were validated by outperforming the state of the art methods on DREAM challenge data, and then LARF was applied to inferences of gene regulatory network associated with psychiatric disorders.

Data mining graphene: Correlative analysis of structure and electronic degrees of freedom in graphenic monolayers with defects

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

Ziatdinov, Maxim A.; Fujii, Shintaro; Kiguchi, Manabu

The link between changes in the material crystal structure and its mechanical, electronic, magnetic, and optical functionalities known as the structure-property relationship is the cornerstone of the contemporary materials science research. The recent advances in scanning transmission electron and scanning probe microscopies (STEM and SPM) have opened an unprecedented path towards examining the materials structure property relationships on the single-impurity and atomic-configuration levels. Lacking, however, are the statistics-based approaches for cross-correlation of structure and property variables obtained in different information channels of the STEM and SPM experiments. Here we have designed an approach based on a combination of sliding windowmore » Fast Fourier Transform, Pearson correlation matrix, linear and kernel canonical correlation, to study a relationship between lattice distortions and electron scattering from the SPM data on graphene with defects. Our analysis revealed that the strength of coupling to strain is altered between different scattering channels which can explain coexistence of several quasiparticle interference patterns in the nanoscale regions of interest. In addition, the application of the kernel functions allowed us extracting a non-linear component of the relationship between the lattice strain and scattering intensity in graphene. Lastly, the outlined approach can be further utilized to analyzing correlations in various multi-modal imaging techniques where the information of interest is spatially distributed and has usually a complex multidimensional nature.« less

Myostatin inhibitors in sports drug testing: Detection of myostatin-neutralizing antibodies in plasma/serum by affinity purification and Western blotting.

PubMed

Walpurgis, Katja; Thomas, Andreas; Schänzer, Wilhelm; Thevis, Mario

2016-02-01

Myostatin is a key regulator of skeletal muscle growth and inhibition of its signaling pathway results in an increased muscle mass and function. The aim of this study was to develop a qualitative detection assay for myostatin-neutralizing antibodies for doping control purposes by using immunological approaches. To detect different types of myostatin-neutralizing antibodies irrespective of their amino acid sequence, an immunological assay specific for antibodies directed against myostatin and having a human Fc domain was established. Affinity purification and Western blotting strategies were combined to allow extracting and identifying relevant analytes from 200 μL of plasma/serum in a non-targeted approach. The assay was characterized regarding specificity, linearity, precision, robustness, and recovery. The assay was found to be highly specific, robust, and linear from 0.1 to 1 μg/mL. The precision was successfully specified at three different concentrations and the recovery of the affinity purification was 58%. Within this study, an immunological detection assay for myostatin-neutralizing antibodies present in plasma/serum specimens was developed and successfully characterized. The presented approach can easily be modified to include other therapeutic antibodies and serves as proof-of-concept for the detection of antibody-based myostatin inhibitors in doping control samples. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Data mining graphene: Correlative analysis of structure and electronic degrees of freedom in graphenic monolayers with defects

DOE PAGES

Ziatdinov, Maxim A.; Fujii, Shintaro; Kiguchi, Manabu; ...

2016-11-09

The link between changes in the material crystal structure and its mechanical, electronic, magnetic, and optical functionalities known as the structure-property relationship is the cornerstone of the contemporary materials science research. The recent advances in scanning transmission electron and scanning probe microscopies (STEM and SPM) have opened an unprecedented path towards examining the materials structure property relationships on the single-impurity and atomic-configuration levels. Lacking, however, are the statistics-based approaches for cross-correlation of structure and property variables obtained in different information channels of the STEM and SPM experiments. Here we have designed an approach based on a combination of sliding windowmore » Fast Fourier Transform, Pearson correlation matrix, linear and kernel canonical correlation, to study a relationship between lattice distortions and electron scattering from the SPM data on graphene with defects. Our analysis revealed that the strength of coupling to strain is altered between different scattering channels which can explain coexistence of several quasiparticle interference patterns in the nanoscale regions of interest. In addition, the application of the kernel functions allowed us extracting a non-linear component of the relationship between the lattice strain and scattering intensity in graphene. Lastly, the outlined approach can be further utilized to analyzing correlations in various multi-modal imaging techniques where the information of interest is spatially distributed and has usually a complex multidimensional nature.« less

Total synthesis of feglymycin based on a linear/convergent hybrid approach using micro-flow amide bond formation

NASA Astrophysics Data System (ADS)

Fuse, Shinichiro; Mifune, Yuto; Nakamura, Hiroyuki; Tanaka, Hiroshi

2016-11-01

Feglymycin is a naturally occurring, anti-HIV and antimicrobial 13-mer peptide that includes highly racemizable 3,5-dihydroxyphenylglycines (Dpgs). Here we describe the total synthesis of feglymycin based on a linear/convergent hybrid approach. Our originally developed micro-flow amide bond formation enabled highly racemizable peptide chain elongation based on a linear approach that was previously considered impossible. Our developed approach will enable the practical preparation of biologically active oligopeptides that contain highly racemizable amino acids, which are attractive drug candidates.

Polychaete functional diversity in shallow habitats: Shelter from the storm

NASA Astrophysics Data System (ADS)

Wouters, Julia M.; Gusmao, Joao B.; Mattos, Gustavo; Lana, Paulo

2018-05-01

Innovative approaches are needed to help understanding how species diversity is related to the latitudinal gradient at large or small scales. We have applied a novel approach, by combining morphological and biological traits, to assess the relative importance of the large scale latitudinal gradient and regional morphodynamic drivers in shaping the functional diversity of polychaete assemblages in shallow water habitats, from exposed to estuarine sandy beaches. We used literature data on polychaetes from beaches along the southern and southeastern Brazilian coast together with data on beach types, slope, grain size, temperature, salinity, and chlorophyll a concentration. Generalized linear models on the FDis index for functional diversity calculated for each site and a combined RLQ and fourth-corner analysis were used to investigate relationships between functional traits and environmental variables. Functional diversity was not related to the latitudinal gradient but negatively correlated with grain size and beach slope. Functional diversity was highest in flat beaches with small grain size, little wave exposure and enhanced primary production, indicating that small scale morphodynamic conditions are the primary drivers of polychaete functional diversity.

Comparing methods for analysis of biomedical hyperspectral image data

NASA Astrophysics Data System (ADS)

Leavesley, Silas J.; Sweat, Brenner; Abbott, Caitlyn; Favreau, Peter F.; Annamdevula, Naga S.; Rich, Thomas C.

2017-02-01

Over the past 2 decades, hyperspectral imaging technologies have been adapted to address the need for molecule-specific identification in the biomedical imaging field. Applications have ranged from single-cell microscopy to whole-animal in vivo imaging and from basic research to clinical systems. Enabling this growth has been the availability of faster, more effective hyperspectral filtering technologies and more sensitive detectors. Hence, the potential for growth of biomedical hyperspectral imaging is high, and many hyperspectral imaging options are already commercially available. However, despite the growth in hyperspectral technologies for biomedical imaging, little work has been done to aid users of hyperspectral imaging instruments in selecting appropriate analysis algorithms. Here, we present an approach for comparing the effectiveness of spectral analysis algorithms by combining experimental image data with a theoretical "what if" scenario. This approach allows us to quantify several key outcomes that characterize a hyperspectral imaging study: linearity of sensitivity, positive detection cut-off slope, dynamic range, and false positive events. We present results of using this approach for comparing the effectiveness of several common spectral analysis algorithms for detecting weak fluorescent protein emission in the midst of strong tissue autofluorescence. Results indicate that this approach should be applicable to a very wide range of applications, allowing a quantitative assessment of the effectiveness of the combined biology, hardware, and computational analysis for detecting a specific molecular signature.

Groundwater vulnerability assessment for organic compounds: fuzzy multi-criteria approach for Mexico city.

PubMed

Mazari-Hiriart, Marisa; Cruz-Bello, Gustavo; Bojórquez-Tapia, Luis A; Juárez-Marusich, Lourdes; Alcantar-López, Georgina; Marín, Luis E; Soto-Galera, Ernesto

2006-03-01

This study was based on a groundwater vulnerability assessment approach implemented for the Mexico City Metropolitan Area (MCMA). The approach is based on a fuzzy multi-criteria procedure integrated in a geographic information system. The approach combined the potential contaminant sources with the permeability of geological materials. Initially, contaminant sources were ranked by experts through the Analytic Hierarchy Process. An aggregated contaminant sources map layer was obtained through the simple additive weighting method, using a scalar multiplication of criteria weights and binary maps showing the location of each source. A permeability map layer was obtained through the reclassification of a geology map using the respective hydraulic conductivity values, followed by a linear normalization of these values against a compatible scale. A fuzzy logic procedure was then applied to transform and combine the two map layers, resulting in a groundwater vulnerability map layer of five classes: very low, low, moderate, high, and very high. Results provided a more coherent assessment of the policy-making priorities considered when discussing the vulnerability of groundwater to organic compounds. The very high and high vulnerability areas covered a relatively small area (71 km(2) or 1.5% of the total study area), allowing the identification of the more critical locations. The advantage of a fuzzy logic procedure is that it enables the best possible use to be made of the information available regarding groundwater vulnerability in the MCMA.

Compressor Performance Scaling in the Presence of Non-Uniform Flow

NASA Astrophysics Data System (ADS)

Hill, David Jarrod

Fuselage-embedded engines in future aircraft will see increased flow distortions due to the ingestion of airframe boundary layers. This reduces the required propulsive power compared to podded engines. Inlet flow distortions mean that localized regions of flow within the fan and first stage compressor are operating at off-design conditions. It is important to weigh the benefit of increased vehicle propulsive efficiency against the resultant reduction in engine efficiency. High computational cost has limited most past research to single distortion studies. The objective of this thesis is to extract scaling laws for transonic compressor performance in the presence of various distortion patterns and intensities. The machine studied is the NASA R67 transonic compressor. Volumetric source terms are used to model rotor and stator blade rows. The modelling approach is an innovative combination of existing flow turning and loss models, combined with a compressible flow correction. This approach allows for a steady calculation to capture distortion transfer; as a result, the computational cost is reduced by two orders of magnitude. At peak efficiency, the rotor work coefficient and isentropic efficiency are matched within 1.4% of previously published experimental results. A key finding of this thesis is that, in non-uniform flow, the state-of-the-art loss model employed is unable to capture the impact of variations in local flow coefficient, limiting the analysis of local entropy generation. New insight explains the mechanism governing the interaction between a total temperature distortion and a compressor rotor. A parametric study comprising 16 inlet distortions reveals that for total temperature distortions, upstream flow redistribution and rotor diffusion factor changes are shown to scale linearly with distortion severity. Linear diffusion factor scaling does not hold true for total pressure distortions. For combined total temperature and total pressure distortions, the changes in rotor diffusion factor are predicted by the summation of the individual distortions, within 3.65%.

Assessing the role of feed water constituents in irreversible membrane fouling of pilot-scale ultrafiltration drinking water treatment systems.

PubMed

Peiris, R H; Jaklewicz, M; Budman, H; Legge, R L; Moresoli, C

2013-06-15

Fluorescence excitation-emission matrix (EEM) approach together with principal component analysis (PCA) was used for assessing hydraulically irreversible fouling of three pilot-scale ultrafiltration (UF) systems containing full-scale and bench-scale hollow fiber membrane modules in drinking water treatment. These systems were operated for at least three months with extensive cycles of permeation, combination of back-pulsing and scouring and chemical cleaning. The principal component (PC) scores generated from the PCA of the fluorescence EEMs were found to be related to humic substances (HS), protein-like and colloidal/particulate matter content. PC scores of HS- and protein-like matter of the UF feed water, when considered separately, showed reasonably good correlations with the rate of hydraulically irreversible fouling for long-term UF operations. In contrast, comparatively weaker correlations for PC scores of colloidal/particulate matter and the rate of hydraulically irreversible fouling were obtained for all UF systems. Since, individual correlations could not fully explain the evolution of the rate of irreversible fouling, multi-linear regression models were developed to relate the combined effect of HS-like, protein-like and colloidal/particulate matter PC scores to the rate of hydraulically irreversible fouling for each specific UF system. These multi-linear regression models revealed significant individual and combined contribution of HS- and protein-like matter to the rate of hydraulically irreversible fouling, with protein-like matter generally showing the greatest contribution. The contribution of colloidal/particulate matter to the rate of hydraulically irreversible fouling was not as significant. The addition of polyaluminum chloride, as coagulant, to UF feed appeared to have a positive impact in reducing hydraulically irreversible fouling by these constituents. The proposed approach has applications in quantifying the individual and synergistic contribution of major natural water constituents to the rate of hydraulically irreversible membrane fouling and shows potential for controlling UF irreversible fouling in the production of drinking water. Copyright © 2013 Elsevier Ltd. All rights reserved.

A linear quadratic regulator approach to the stabilization of uncertain linear systems

NASA Technical Reports Server (NTRS)

Shieh, L. S.; Sunkel, J. W.; Wang, Y. J.

1990-01-01

This paper presents a linear quadratic regulator approach to the stabilization of uncertain linear systems. The uncertain systems under consideration are described by state equations with the presence of time-varying unknown-but-bounded uncertainty matrices. The method is based on linear quadratic regulator (LQR) theory and Liapunov stability theory. The robust stabilizing control law for a given uncertain system can be easily constructed from the symmetric positive-definite solution of the associated augmented Riccati equation. The proposed approach can be applied to matched and/or mismatched systems with uncertainty matrices in which only their matrix norms are bounded by some prescribed values and/or their entries are bounded by some prescribed constraint sets. Several numerical examples are presented to illustrate the results.

An inclusive SUSY approach to position dependent mass systems

NASA Astrophysics Data System (ADS)

Karthiga, S.; Chithiika Ruby, V.; Senthilvelan, M.

2018-06-01

The supersymmetry (SUSY) formalism for a position dependent mass problem with a more general ordering is yet to be formulated. In this paper, we present an unified SUSY approach for PDM problems of any ordering. Highlighting all non-Hermitian Hamiltonians of PDM problems are of quasi-Hermitian nature, the SUSY operators of these problems are constructed using similarity transformation. The methodology that we propose here is applicable for even more general cases where the kinetic energy term is represented by linear combination of infinite number of possible orderings. We illustrate the method with an example, namely Mathews-Lakshmanan (ML) oscillator. Our results show that the latter system is shape invariant for all possible orderings. We derive eigenvalues and eigenvectors of this nonlinear oscillator for all possible orderings including Hermitian and non-Hermitian ones.

Distribution-Agnostic Stochastic Optimal Power Flow for Distribution Grids: Preprint

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

Baker, Kyri; Dall'Anese, Emiliano; Summers, Tyler

2016-09-01

This paper outlines a data-driven, distributionally robust approach to solve chance-constrained AC optimal power flow problems in distribution networks. Uncertain forecasts for loads and power generated by photovoltaic (PV) systems are considered, with the goal of minimizing PV curtailment while meeting power flow and voltage regulation constraints. A data- driven approach is utilized to develop a distributionally robust conservative convex approximation of the chance-constraints; particularly, the mean and covariance matrix of the forecast errors are updated online, and leveraged to enforce voltage regulation with predetermined probability via Chebyshev-based bounds. By combining an accurate linear approximation of the AC power flowmore » equations with the distributionally robust chance constraint reformulation, the resulting optimization problem becomes convex and computationally tractable.« less

Improved synchronization criteria for time-delayed chaotic Lur'e systems using sampled-data control

NASA Astrophysics Data System (ADS)

Duan, Wenyong; Li, Yan; Fu, Xiaorong; Du, Baozhu

2017-02-01

This paper is concerned with the synchronization for a class of time-delayed chaotic Lur’e systems using sampled-data control. Both of time-varying and time-invariant delays are considered. New criteria are proposed in terms of linear matrix inequalities (LMIs) by employing a modified LKF combined with the delay-fraction theory and some novel terms. The criteria are less conservative than some previous ones and a longer sampling period is achieved under the new results. Furthermore, the derived conditions are employed to design a sampled-data controller. The desired controller gain matrix can be obtained by means of the LMI approach. Finally, a numerical examples and simulations on Chua’s circuit is presented to show the effectiveness of the proposed approach.

A Flexible and Efficient Method for Solving Ill-Posed Linear Integral Equations of the First Kind for Noisy Data

NASA Astrophysics Data System (ADS)

Antokhin, I. I.

2017-06-01

We propose an efficient and flexible method for solving Fredholm and Abel integral equations of the first kind, frequently appearing in astrophysics. These equations present an ill-posed problem. Our method is based on solving them on a so-called compact set of functions and/or using Tikhonov's regularization. Both approaches are non-parametric and do not require any theoretic model, apart from some very loose a priori constraints on the unknown function. The two approaches can be used independently or in a combination. The advantage of the method, apart from its flexibility, is that it gives uniform convergence of the approximate solution to the exact one, as the errors of input data tend to zero. Simulated and astrophysical examples are presented.

Protein model quality assessment prediction by combining fragment comparisons and a consensus Cα contact potential

PubMed Central

Zhou, Hongyi; Skolnick, Jeffrey

2009-01-01

In this work, we develop a fully automated method for the quality assessment prediction of protein structural models generated by structure prediction approaches such as fold recognition servers, or ab initio methods. The approach is based on fragment comparisons and a consensus Cα contact potential derived from the set of models to be assessed and was tested on CASP7 server models. The average Pearson linear correlation coefficient between predicted quality and model GDT-score per target is 0.83 for the 98 targets which is better than those of other quality assessment methods that participated in CASP7. Our method also outperforms the other methods by about 3% as assessed by the total GDT-score of the selected top models. PMID:18004783

Introducing linear functions: an alternative statistical approach

NASA Astrophysics Data System (ADS)

Nolan, Caroline; Herbert, Sandra

2015-12-01

The introduction of linear functions is the turning point where many students decide if mathematics is useful or not. This means the role of parameters and variables in linear functions could be considered to be `threshold concepts'. There is recognition that linear functions can be taught in context through the exploration of linear modelling examples, but this has its limitations. Currently, statistical data is easily attainable, and graphics or computer algebra system (CAS) calculators are common in many classrooms. The use of this technology provides ease of access to different representations of linear functions as well as the ability to fit a least-squares line for real-life data. This means these calculators could support a possible alternative approach to the introduction of linear functions. This study compares the results of an end-of-topic test for two classes of Australian middle secondary students at a regional school to determine if such an alternative approach is feasible. In this study, test questions were grouped by concept and subjected to concept by concept analysis of the means of test results of the two classes. This analysis revealed that the students following the alternative approach demonstrated greater competence with non-standard questions.

Simulation of herbicide degradation in different soils by use of Pedo-transfer functions (PTF) and non-linear kinetics.

PubMed

von Götz, N; Richter, O

1999-03-01

The degradation behaviour of bentazone in 14 different soils was examined at constant temperature and moisture conditions. Two soils were examined at different temperatures. On the basis of these data the influence of soil properties and temperature on degradation was assessed and modelled. Pedo-transfer functions (PTF) in combination with a linear and a non-linear model were found suitable to describe the bentazone degradation in the laboratory as related to soil properties. The linear PTF can be combined with a rate related to the temperature to account for both soil property and temperature influence at the same time.

Quantum processing by remote quantum control

NASA Astrophysics Data System (ADS)

Qiang, Xiaogang; Zhou, Xiaoqi; Aungskunsiri, Kanin; Cable, Hugo; O'Brien, Jeremy L.

2017-12-01

Client-server models enable computations to be hosted remotely on quantum servers. We present a novel protocol for realizing this task, with practical advantages when using technology feasible in the near term. Client tasks are realized as linear combinations of operations implemented by the server, where the linear coefficients are hidden from the server. We report on an experimental demonstration of our protocol using linear optics, which realizes linear combination of two single-qubit operations by a remote single-qubit control. In addition, we explain when our protocol can remain efficient for larger computations, as well as some ways in which privacy can be maintained using our protocol.

A displacement-based approach for determining non-linear effects on pre-tensioned-cable cross-braced structures

NASA Astrophysics Data System (ADS)

Giaccu, Gian Felice; Caracoglia, Luca

2017-04-01

Pre-tensioned-cable bracing systems are widely employed in structural engineering to limit lateral deflections and stabilize structures. A suitable configuration of the pre-tensioned-cable bracing systems in a structure is an important issue since the internal force distribution, emerging from the interaction with the existing structure, significantly affects the structural dynamic behavior. The design, however, is often based on the intuition and the previous experience of the engineer. In recent years, the authors have been investigating the non-linear dynamic response of cable systems, installed on cable-stayed bridges, and in particular the so-called "cable-cross-tie systems" forming a cable network. The bracing cables (cross-ties) can exhibit slackening or snapping. Therefore, a non-linear unilateral model, combined with the taut-cable theory, is required to simulate the incipient slackening conditions in the stays. Capitalizing from this work on non-linear cable dynamics, this paper proposes a new approach to analyze, in laterally- braced truss structures, the unilateral effects and dynamic response accounting for the loss in the pre-tensioning force imparted to the bracing cables. This effect leads to non-linear vibration of the structure. In this preliminary study, the free vibrations of the structure are investigated by using the "Equivalent Linearization Method". A performance coefficient, a real positive number between 0.5 and 1.0, is defined and employed to monitor the relative reduction in the apparent stiffness of the braces during structural vibration, "mode by mode". It is shown that the system can exhibit alternate unilateral behavior of the cross-braces. A reduction of the performance coefficient close to fifty percent is observed in the braces when the initial pre-tensioning force is small. On the other hand the performance coefficient tends to one in the case of a high level of pre-stress. It is concluded that the performance coefficient may possibly be used as an indicator for the design of the braces since a suitable selection of the initial pre-tensioning force can avoid slackening in the braces.

Multilayer neural networks for reduced-rank approximation.

PubMed

Diamantaras, K I; Kung, S Y

1994-01-01

This paper is developed in two parts. First, the authors formulate the solution to the general reduced-rank linear approximation problem relaxing the invertibility assumption of the input autocorrelation matrix used by previous authors. The authors' treatment unifies linear regression, Wiener filtering, full rank approximation, auto-association networks, SVD and principal component analysis (PCA) as special cases. The authors' analysis also shows that two-layer linear neural networks with reduced number of hidden units, trained with the least-squares error criterion, produce weights that correspond to the generalized singular value decomposition of the input-teacher cross-correlation matrix and the input data matrix. As a corollary the linear two-layer backpropagation model with reduced hidden layer extracts an arbitrary linear combination of the generalized singular vector components. Second, the authors investigate artificial neural network models for the solution of the related generalized eigenvalue problem. By introducing and utilizing the extended concept of deflation (originally proposed for the standard eigenvalue problem) the authors are able to find that a sequential version of linear BP can extract the exact generalized eigenvector components. The advantage of this approach is that it's easier to update the model structure by adding one more unit or pruning one or more units when the application requires it. An alternative approach for extracting the exact components is to use a set of lateral connections among the hidden units trained in such a way as to enforce orthogonality among the upper- and lower-layer weights. The authors call this the lateral orthogonalization network (LON) and show via theoretical analysis-and verify via simulation-that the network extracts the desired components. The advantage of the LON-based model is that it can be applied in a parallel fashion so that the components are extracted concurrently. Finally, the authors show the application of their results to the solution of the identification problem of systems whose excitation has a non-invertible autocorrelation matrix. Previous identification methods usually rely on the invertibility assumption of the input autocorrelation, therefore they can not be applied to this case.

The 20 GHz GaAs monolithic power amplifier module development

NASA Technical Reports Server (NTRS)

1984-01-01

The development of a 20 GHz GaAs FET monlithic power amplifier module for advanced communication applications is described. Four-way power combing of four 0.6 W amplifier modules is used as the baseline approach. For this purpose, a monolithic four-way traveling-wave power divider/combiner was developed. Over a 20 GHz bandwidth (10 to 30 GHz), an insertion loss of no more than 1.2 dB was measured for a pair of back-to-back connected divider/combiners. Isolation between output ports is better than 20 dB, and VSWRs are better than 21:1. A distributed amplifier with six 300 micron gate width FETs and gate and drain transmission line tapers has been designed, fabricated, and evaluated for use as an 0.6 W module. This amplifier has achieved state-of-the-art results of 0.5 W output power with at least 4 dB gain across the entire 2 to 21 GHz frequency range. An output power of 2 W was achieved at a measurement frequency of 18 GHz when four distributed amplifiers were power-combined using a pair of traveling-wave divider/combiners. Another approach is the direct common-source cascading of three power FET stages. An output power of up to 2W with 12 dB gain and 20% power-added efficiency has been achieved with this approach (at 17 GHz). The linear gain was 14 dB at 1 W output. The first two stages of the three-stage amplifier have achieved an output power of 1.6 W with 9 dB gain and 26% power-added efficiency at 16 GHz.

Assessment of combined toxicity of heavy metals from industrial wastewaters on Photobacterium phosphoreum T3S

NASA Astrophysics Data System (ADS)

Zeb, BibiSaima; Ping, Zheng; Mahmood, Qaisar; Lin, Qiu; Pervez, Arshid; Irshad, Muhammad; Bilal, Muhammad; Bhatti, Zulfiqar Ahmad; Shaheen, Shahida

2017-07-01

This research work is focusing on the toxicities of heavy metals of industrial origin to anaerobic digestion of the industrial wastewater. Photobacterium phosphoreum T3S was used as an indicator organism. The acute toxicities of heavy metals on P. phosphoreum T3S were assessed during 15-min half inhibitory concentration (IC50) as indicator at pH 5.5-6. Toxicity assays involved the assessment of multicomponent mixtures using TU and MTI approaches. The results of individual toxicity indicated that the toxicity of Cd, Cu and Pb on P. phosphoreum increased with increasing concentrations and there was a linear correlation. The 15-min IC50 values of Cd, Cu and Pb were 0.537, 1.905 and 1.231 mg/L, respectively, and their toxic order was Cd > Pb > Cu. The combined effects of Cd, Cu and Pb were assayed by equivalent concentration mixing method. The results showed that the combined effects of Cd + Cu, Cd + Pb, Cu + Pb, Cd + Cu + Pb were antagonistic, antagonistic and partly additive. The combined effect of three heavy metals was partly additive.

Operator Factorization and the Solution of Second-Order Linear Ordinary Differential Equations

ERIC Educational Resources Information Center

Robin, W.

2007-01-01

The theory and application of second-order linear ordinary differential equations is reviewed from the standpoint of the operator factorization approach to the solution of ordinary differential equations (ODE). Using the operator factorization approach, the general second-order linear ODE is solved, exactly, in quadratures and the resulting…

Construction of large signaling pathways using an adaptive perturbation approach with phosphoproteomic data.

PubMed

Melas, Ioannis N; Mitsos, Alexander; Messinis, Dimitris E; Weiss, Thomas S; Rodriguez, Julio-Saez; Alexopoulos, Leonidas G

2012-04-01

Construction of large and cell-specific signaling pathways is essential to understand information processing under normal and pathological conditions. On this front, gene-based approaches offer the advantage of large pathway exploration whereas phosphoproteomic approaches offer a more reliable view of pathway activities but are applicable to small pathway sizes. In this paper, we demonstrate an experimentally adaptive approach to construct large signaling pathways from phosphoproteomic data within a 3-day time frame. Our approach--taking advantage of the fast turnaround time of the xMAP technology--is carried out in four steps: (i) screen optimal pathway inducers, (ii) select the responsive ones, (iii) combine them in a combinatorial fashion to construct a phosphoproteomic dataset, and (iv) optimize a reduced generic pathway via an Integer Linear Programming formulation. As a case study, we uncover novel players and their corresponding pathways in primary human hepatocytes by interrogating the signal transduction downstream of 81 receptors of interest and constructing a detailed model for the responsive part of the network comprising 177 species (of which 14 are measured) and 365 interactions.

A Bayesian Approach to Model Selection in Hierarchical Mixtures-of-Experts Architectures.

PubMed

Tanner, Martin A.; Peng, Fengchun; Jacobs, Robert A.

1997-03-01

There does not exist a statistical model that shows good performance on all tasks. Consequently, the model selection problem is unavoidable; investigators must decide which model is best at summarizing the data for each task of interest. This article presents an approach to the model selection problem in hierarchical mixtures-of-experts architectures. These architectures combine aspects of generalized linear models with those of finite mixture models in order to perform tasks via a recursive "divide-and-conquer" strategy. Markov chain Monte Carlo methodology is used to estimate the distribution of the architectures' parameters. One part of our approach to model selection attempts to estimate the worth of each component of an architecture so that relatively unused components can be pruned from the architecture's structure. A second part of this approach uses a Bayesian hypothesis testing procedure in order to differentiate inputs that carry useful information from nuisance inputs. Simulation results suggest that the approach presented here adheres to the dictum of Occam's razor; simple architectures that are adequate for summarizing the data are favored over more complex structures. Copyright 1997 Elsevier Science Ltd. All Rights Reserved.

Wigner tomography of multispin quantum states

NASA Astrophysics Data System (ADS)

Leiner, David; Zeier, Robert; Glaser, Steffen J.

2017-12-01

We study the tomography of multispin quantum states in the context of finite-dimensional Wigner representations. An arbitrary operator can be completely characterized and visualized using multiple shapes assembled from linear combinations of spherical harmonics [A. Garon, R. Zeier, and S. J. Glaser, Phys. Rev. A 91, 042122 (2015), 10.1103/PhysRevA.91.042122]. We develop a general methodology to experimentally recover these shapes by measuring expectation values of rotated axial spherical tensor operators and provide an interpretation in terms of fictitious multipole potentials. Our approach is experimentally demonstrated for quantum systems consisting of up to three spins using nuclear magnetic resonance spectroscopy.

Cell growth, division, and death in cohesive tissues: A thermodynamic approach

NASA Astrophysics Data System (ADS)

Yabunaka, Shunsuke; Marcq, Philippe

2017-08-01

Cell growth, division, and death are defining features of biological tissues that contribute to morphogenesis. In hydrodynamic descriptions of cohesive tissues, their occurrence implies a nonzero rate of variation of cell density. We show how linear nonequilibrium thermodynamics allows us to express this rate as a combination of relevant thermodynamic forces: chemical potential, velocity divergence, and activity. We illustrate the resulting effects of the nonconservation of cell density on simple examples inspired by recent experiments on cell monolayers, considering first the velocity of a spreading front, and second an instability leading to mechanical waves.

Weighted cubic and biharmonic splines

NASA Astrophysics Data System (ADS)

Kvasov, Boris; Kim, Tae-Wan

2017-01-01

In this paper we discuss the design of algorithms for interpolating discrete data by using weighted cubic and biharmonic splines in such a way that the monotonicity and convexity of the data are preserved. We formulate the problem as a differential multipoint boundary value problem and consider its finite-difference approximation. Two algorithms for automatic selection of shape control parameters (weights) are presented. For weighted biharmonic splines the resulting system of linear equations can be efficiently solved by combining Gaussian elimination with successive over-relaxation method or finite-difference schemes in fractional steps. We consider basic computational aspects and illustrate main features of this original approach.