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Sample records for multiple model identification

  1. Identification of Multiple-Mode Linear Models Based on Particle Swarm Optimizer with Cyclic Network Mechanism

    Tae-Hyoung Kim

    2017-01-01

    Full Text Available This paper studies the metaheuristic optimizer-based direct identification of a multiple-mode system consisting of a finite set of linear regression representations of subsystems. To this end, the concept of a multiple-mode linear regression model is first introduced, and its identification issues are established. A method for reducing the identification problem for multiple-mode models to an optimization problem is also described in detail. Then, to overcome the difficulties that arise because the formulated optimization problem is inherently ill-conditioned and nonconvex, the cyclic-network-topology-based constrained particle swarm optimizer (CNT-CPSO is introduced, and a concrete procedure for the CNT-CPSO-based identification methodology is developed. This scheme requires no prior knowledge of the mode transitions between subsystems and, unlike some conventional methods, can handle a large amount of data without difficulty during the identification process. This is one of the distinguishing features of the proposed method. The paper also considers an extension of the CNT-CPSO-based identification scheme that makes it possible to simultaneously obtain both the optimal parameters of the multiple submodels and a certain decision parameter involved in the mode transition criteria. Finally, an experimental setup using a DC motor system is established to demonstrate the practical usability of the proposed metaheuristic optimizer-based identification scheme for developing a multiple-mode linear regression model.

  2. Multi-Frame Rate Based Multiple-Model Training for Robust Speaker Identification of Disguised Voice

    Prasad, Swati; Tan, Zheng-Hua; Prasad, Ramjee

    2013-01-01

    Speaker identification systems are prone to attack when voice disguise is adopted by the user. To address this issue,our paper studies the effect of using different frame rates on the accuracy of the speaker identification system for disguised voice.In addition, a multi-frame rate based multiple......-model training method is proposed. The experimental results show the superior performance of the proposed method compared to the commonly used single frame rate method for three types of disguised voice taken from the CHAINS corpus....

  3. On the identification of multiple space dependent ionic parameters in cardiac electrophysiology modelling

    Abidi, Yassine; Bellassoued, Mourad; Mahjoub, Moncef; Zemzemi, Nejib

    2018-03-01

    In this paper, we consider the inverse problem of space dependent multiple ionic parameters identification in cardiac electrophysiology modelling from a set of observations. We use the monodomain system known as a state-of-the-art model in cardiac electrophysiology and we consider a general Hodgkin-Huxley formalism to describe the ionic exchanges at the microscopic level. This formalism covers many physiological transmembrane potential models including those in cardiac electrophysiology. Our main result is the proof of the uniqueness and a Lipschitz stability estimate of ion channels conductance parameters based on some observations on an arbitrary subdomain. The key idea is a Carleman estimate for a parabolic operator with multiple coefficients and an ordinary differential equation system.

  4. The multiple deficit model of dyslexia: what does it mean for identification and intervention?

    Ring, Jeremiah; Black, Jeffrey L

    2018-04-24

    Research demonstrates that phonological skills provide the basis of reading acquisition and are a primary processing deficit in dyslexia. This consensus has led to the development of effective methods of reading intervention. However, a single phonological deficit is not sufficient to account for the heterogeneity of individuals with dyslexia, and recent research provides evidence that supports a multiple-deficit model of reading disorders. Two studies are presented that investigate (1) the prevalence of phonological and cognitive processing deficit profiles in children with significant reading disability and (2) the effects of those same phonological and cognitive processing skills on reading development in a sample of children that received treatment for dyslexia. The results are discussed in the context of implications for identification and an intervention approach that accommodates multiple deficits within a comprehensive skills-based reading program.

  5. Nonlinear Modeling and Identification of an Aluminum Honeycomb Panel with Multiple Bolts

    Yongpeng Chu

    2016-01-01

    Full Text Available This paper focuses on the nonlinear dynamics modeling and parameter identification of an Aluminum Honeycomb Panel (AHP with multiple bolted joints. Finite element method using eight-node solid elements is exploited to model the panel and the bolted connection interface as a homogeneous, isotropic plate and as a thin layer of nonlinear elastic-plastic material, respectively. The material properties of a thin layer are defined by a bilinear elastic plastic model, which can describe the energy dissipation and softening phenomena in the bolted joints under nonlinear states. Experimental tests at low and high excitation levels are performed to reveal the dynamic characteristics of the bolted structure. In particular, the linear material parameters of the panel are identified via experimental tests at low excitation levels, whereas the nonlinear material parameters of the thin layer are updated by using the genetic algorithm to minimize the residual error between the measured and the simulation data at a high excitation level. It is demonstrated by comparing the frequency responses of the updated FEM and the experimental system that the thin layer of bilinear elastic-plastic material is very effective for modeling the nonlinear joint interface of the assembled structure with multiple bolts.

  6. Hyperspectral material identification on radiance data using single-atmosphere or multiple-atmosphere modeling

    Mariano, Adrian V.; Grossmann, John M.

    2010-11-01

    Reflectance-domain methods convert hyperspectral data from radiance to reflectance using an atmospheric compensation model. Material detection and identification are performed by comparing the compensated data to target reflectance spectra. We introduce two radiance-domain approaches, Single atmosphere Adaptive Cosine Estimator (SACE) and Multiple atmosphere ACE (MACE) in which the target reflectance spectra are instead converted into sensor-reaching radiance using physics-based models. For SACE, known illumination and atmospheric conditions are incorporated in a single atmospheric model. For MACE the conditions are unknown so the algorithm uses many atmospheric models to cover the range of environmental variability, and it approximates the result using a subspace model. This approach is sometimes called the invariant method, and requires the choice of a subspace dimension for the model. We compare these two radiance-domain approaches to a Reflectance-domain ACE (RACE) approach on a HYDICE image featuring concealed materials. All three algorithms use the ACE detector, and all three techniques are able to detect most of the hidden materials in the imagery. For MACE we observe a strong dependence on the choice of the material subspace dimension. Increasing this value can lead to a decline in performance.

  7. The differential effects of transformational leadership on multiple identifications at work : A meta-analytic model

    Horstmeier, C.A.L.; Boer, D.; Homan, A.C.; Voelpel, S.C.

    Employees’ identifications are a valuable asset for modern organizations, and identification research has stressed the necessity to distinguish identifications according to their focus (i.e. organizational, team, or leader identification). Interestingly, transformational leadership (TFL) has been

  8. Identification of Determinants of Sports Skill Level in Badminton Players Using the Multiple Regression Model

    Jaworski Janusz

    2016-03-01

    Full Text Available Purpose. The aim of the study was to evaluate somatic and functional determinants of sports skill level in badminton players at three consecutive stages of training. Methods. The study examined 96 badminton players aged 11 to 19 years. The scope of the study included somatic characteristics, physical abilities and neurosensory abilities. Thirty nine variables were analysed in each athlete. Coefficients of multiple determination were used to evaluate the effect of structural and functional parameters on sports skill level in badminton players. Results. In the group of younger cadets, quality and effectiveness of playing were mostly determined by the level of physical abilities. In the group of cadets, the most important determinants were physical abilities, followed by somatic characteristics. In this group, coordination abilities were also important. In juniors, the most pronounced was a set of the variables that reflect physical abilities. Conclusions. Models of determination of sports skill level are most noticeable in the group of cadets. In all three groups of badminton players, the dominant effect on the quality of playing is due to a set of the variables that determine physical abilities.

  9. Overall Memory Impairment Identification with Mathematical Modeling of the CVLT-II Learning Curve in Multiple Sclerosis

    Stepanov, Igor I.; Abramson, Charles I.; Hoogs, Marietta; Benedict, Ralph H. B.

    2012-01-01

    The CVLT-II provides standardized scores for each of the List A five learning trials, so that the clinician can compare the patient's raw trials 1–5 scores with standardized ones. However, frequently, a patient's raw scores fluctuate making a proper interpretation difficult. The CVLT-II does not offer any other methods for classifying a patient's learning and memory status on the background of the learning curve. The main objective of this research is to illustrate that discriminant analysis provides an accurate assessment of the learning curve, if suitable predictor variables are selected. Normal controls were ninety-eight healthy volunteers (78 females and 20 males). A group of MS patients included 365 patients (266 females and 99 males) with clinically defined multiple sclerosis. We show that the best predictor variables are coefficients B3 and B4 of our mathematical model B3 ∗ exp(−B2  ∗  (X − 1)) + B4  ∗  (1 − exp(−B2  ∗  (X − 1))) because discriminant functions, calculated separately for B3 and B4, allow nearly 100% correct classification. These predictors allow identification of separate impairment of readiness to learn or ability to learn, or both. PMID:22745911

  10. Overall Memory Impairment Identification with Mathematical Modeling of the CVLT-II Learning Curve in Multiple Sclerosis

    Igor I. Stepanov

    2012-01-01

    Full Text Available The CVLT-II provides standardized scores for each of the List A five learning trials, so that the clinician can compare the patient's raw trials 1–5 scores with standardized ones. However, frequently, a patient's raw scores fluctuate making a proper interpretation difficult. The CVLT-II does not offer any other methods for classifying a patient's learning and memory status on the background of the learning curve. The main objective of this research is to illustrate that discriminant analysis provides an accurate assessment of the learning curve, if suitable predictor variables are selected. Normal controls were ninety-eight healthy volunteers (78 females and 20 males. A group of MS patients included 365 patients (266 females and 99 males with clinically defined multiple sclerosis. We show that the best predictor variables are coefficients 3 and 4 of our mathematical model 3∗exp(−2∗(−1+4∗(1−exp(−2∗(−1 because discriminant functions, calculated separately for 3 and 4, allow nearly 100% correct classification. These predictors allow identification of separate impairment of readiness to learn or ability to learn, or both.

  11. Identification of the timing-of-events model with multiple competing exit risks from single-spell data

    Drepper, Bettina; Effraimidis, G.

    2016-01-01

    The identification result of the timing-of-events model (Abbring and Van den Berg, 2003b) is extended to a model with several competing exit risk equations. This extension allows e.g. to simultaneously identify the different effects a benefit sanction has on the rate of finding work and leaving t...

  12. Multiple independent identification decisions: a method of calibrating eyewitness identifications.

    Pryke, Sean; Lindsay, R C L; Dysart, Jennifer E; Dupuis, Paul

    2004-02-01

    Two experiments (N = 147 and N = 90) explored the use of multiple independent lineups to identify a target seen live. In Experiment 1, simultaneous face, body, and sequential voice lineups were used. In Experiment 2, sequential face, body, voice, and clothing lineups were used. Both studies demonstrated that multiple identifications (by the same witness) from independent lineups of different features are highly diagnostic of suspect guilt (G. L. Wells & R. C. L. Lindsay, 1980). The number of suspect and foil selections from multiple independent lineups provides a powerful method of calibrating the accuracy of eyewitness identification. Implications for use of current methods are discussed. ((c) 2004 APA, all rights reserved)

  13. Multiplication circuit for particle identification

    Gerlier, Jean

    1962-01-01

    After having commented some characteristics of the particles present in a cyclotron, and their interactions, this report addresses the development and the implementation of a method and a device for selecting and counting particles. The author presents the principle and existing techniques of selection. In comparison with an existing device, the proportional counter and the scintillator are replaced by junctions: a surface barrier type junction (a silicon N layer with a very thin oxygen layer playing the role of the P layer), and lithium-based junction (a silicon P type layer made intrinsic by migration of lithium). The author then describes the developed circuit and assembly (background of the choice of a multiplication circuit), and their operation. In the next part, he presents the performed tests and discuses the obtained results. He finally outlines the benefits of the herein presented circuit [fr

  14. Identification of physical models

    Melgaard, Henrik

    1994-01-01

    of the model with the available prior knowledge. The methods for identification of physical models have been applied in two different case studies. One case is the identification of thermal dynamics of building components. The work is related to a CEC research project called PASSYS (Passive Solar Components......The problem of identification of physical models is considered within the frame of stochastic differential equations. Methods for estimation of parameters of these continuous time models based on descrete time measurements are discussed. The important algorithms of a computer program for ML or MAP...... design of experiments, which is for instance the design of an input signal that are optimal according to a criterion based on the information provided by the experiment. Also model validation is discussed. An important verification of a physical model is to compare the physical characteristics...

  15. Perceptions of Usefulness: Using the Holland Code Theory, Multiple Intelligences Theory, and Role Model Identification to Determine a Career Niche in the Fashion Industry for First-Quarter Fashion Students

    Green, Crystal D.

    2010-01-01

    This action research study investigated the perceptions that student participants had on the development of a career exploration model and a career exploration project. The Holland code theory was the primary assessment used for this research study, in addition to the Multiple Intelligences theory and the identification of a role model for the…

  16. Identification of multiple detrital sources for Otway Supergroup sedimentary rocks: implications for basin models and chronostratigraphic correlations

    Mitchell, M.M.

    1997-01-01

    Correlation of apatite chlorine content (wt%) with apatite fission track age (Ma) from Lower Cretaceous Otway Supergroup sediments at present-day low temperatures, allows identification of two characteristic detrital source regions. Apatites from eroded Palaeozoic basement terrains yield low Cl content (generally 0.5 wt%) and syndepositional fission track ages. Where post-depositional thermal annealing ( > 70 degree C) has significantly reduced the fission track age, provenance information is preserved in the apatite Cl composition alone. In the Otway Supergroup, evidence for contemporaneous volcanism was found in both the Eumeralla Formation (Albian-Aptian), and Crayfish Group (Aptian-Berriasian) in samples located towards the central rift, where less sandy facies dominate. Results suggest that Crayfish Group sediments deposited along the northern margin of the basin were predominantly derived from eroding basement material, while the section located towards the central rift contains a greater proportion of volcanogenic detritus. Evidence from this study suggests that volcanogenic detritus was a distal sediment source throughout the entire early rift phase, prior to the main influx of arc-related volcanogenic material during deposition of the Eumeralla Formation. As diagenesis of volcanogenic sediments significantly reduces porosity and permeability of the sandstones, reservoir quality and petroleum potential may be significantly reduced in the Crayfish Group in deeper parts of the basin where a greater proportion of volcanogenic detritus is suggested. The results presented here provide important information regarding Lower Cretaceous Otway Basin stratigraphy and clearly indicate that this methodology may have wider application. (authors)

  17. Identification of multiple modes of axisymmetric or circularly repetitive structures

    Kopff, P.

    1983-01-01

    The axisymmetric structures, or those composed with circularly repetitive elements, often display multiple modes, which are not easy to separate by modal identification of experimental responses. To be able to solve in situ some problems related to the vibrational behaviour of reactor vessels or other such huge structures, ELECTRICITY DE FRANCE developed a few years ago, experimental capabilities providing heavy harmonic driving forces, and elaborate data acquisition, signal processing and modal identification software, self-contained in an integrated mobile test facility. The modal analysis techniques we have developed with the LABORATOIRE DE MECANIQUE Appliquee of University of BESANCON (FRANCE) were especially suited for identification of multiple or separation of quasi-multiple modes, i.e. very close and strongly coupled resonances. Besides, the curve fitting methods involved, compute the same complex eigen-frequencies for all the vibration pick-ups, for better accuracy of the related eigen-vector components. Moreover, the latest extensions of these algorithms give us the means to deal with non-linear behaviour. The performances of these programs are drawn from some experimental results on axisymmetric or circularly repetitive structure, we tested in our laboratory to validate the computational hypothesis used in models for seismic responses of breeder reactor vessels. (orig.)

  18. Identification of nonlinear anelastic models

    Draganescu, G E; Bereteu, L; Ercuta, A

    2008-01-01

    A useful nonlinear identification technique applied to the anelastic and rheologic models is presented in this paper. First introduced by Feldman, the method is based on the Hilbert transform, and is currently used for identification of the nonlinear vibrations

  19. Exploiting Multiple Detections for Person Re-Identification

    Amran Bhuiyan

    2018-01-01

    Full Text Available Re-identification systems aim at recognizing the same individuals in multiple cameras, and one of the most relevant problems is that the appearance of same individual varies across cameras due to illumination and viewpoint changes. This paper proposes the use of cumulative weighted brightness transfer functions (CWBTFs to model these appearance variations. Different from recently proposed methods which only consider pairs of images to learn a brightness transfer function, we exploit such a multiple-frame-based learning approach that leverages consecutive detections of each individual to transfer the appearance. We first present a CWBTF framework for the task of transforming appearance from one camera to another. We then present a re-identification framework where we segment the pedestrian images into meaningful parts and extract features from such parts, as well as from the whole body. Jointly, both of these frameworks contribute to model the appearance variations more robustly. We tested our approach on standard multi-camera surveillance datasets, showing consistent and significant improvements over existing methods on three different datasets without any other additional cost. Our approach is general and can be applied to any appearance-based method.

  20. A Multiple Identity Approach to Gender: Identification with Women, Identification with Feminists, and Their Interaction

    van Breen, Jolien A.; Spears, Russell; Kuppens, Toon; de Lemus, Soledad

    2017-01-01

    Across four studies, we examine multiple identities in the context of gender and propose that women's attitudes toward gender group membership are governed by two largely orthogonal dimensions of gender identity: identification with women and identification with feminists. We argue that identification with women reflects attitudes toward the content society gives to group membership: what does it mean to be a woman in terms of group characteristics, interests and values? Identification with f...

  1. Multiple social identifications and adolescents' self-esteem.

    Benish-Weisman, Maya; Daniel, Ella; Schiefer, David; Möllering, Anna; Knafo-Noam, Ariel

    2015-10-01

    The research examined the relationship between multiple social identifications and self-esteem. Early adolescents (M = 11.4, SD = .95) and mid-adolescents (M = 15.9, SD = 1.18) from Germany and Israel (n = 2337) were studied. Respondents described their social identification as students, family members, and as members of the majority national group and reported self-esteem. A longitudinal, cross-sectional and cross-cultural design revealed, as predicted, multiple social identifications related positively to self-esteem concurrently; they also related positively to self-esteem longitudinally over the course of a year. Moreover, multiple social identifications were found to be antecedent to self-esteem, not vice versa. Finally, multiple social identifications were found to decrease over time. The article discusses the contribution of multiple social identifications to self-esteem at different ages and in various contexts. Copyright © 2015 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  2. On multiple crack identification by ultrasonic scanning

    Brigante, M.; Sumbatyan, M. A.

    2018-04-01

    The present work develops an approach which reduces operator equations arising in the engineering problems to the problem of minimizing the discrepancy functional. For this minimization, an algorithm of random global search is proposed, which is allied to some genetic algorithms. The efficiency of the method is demonstrated by the solving problem of simultaneous identification of several linear cracks forming an array in an elastic medium by using the circular Ultrasonic scanning.

  3. System identification via sparse multiple kernel-based regularization using sequential convex optimization techniques

    Chen, Tianshi; Andersen, Martin Skovgaard; Ljung, Lennart

    2014-01-01

    Model estimation and structure detection with short data records are two issues that receive increasing interests in System Identification. In this paper, a multiple kernel-based regularization method is proposed to handle those issues. Multiple kernels are conic combinations of fixed kernels...

  4. A multiple identity approach to gender : Identification with women, identification with feminists, and their interaction

    van Breen, Jolien A.; Spears, Russell; Kuppens, Toon; de Lemus, Soledad

    2017-01-01

    Across four studies, we examine multiple identities in the context of gender and propose that women's attitudes toward gender group membership are governed by two largely orthogonal dimensions of gender identity: identification with women and identification with feminists. We argue that

  5. A Multiple Identity Approach to Gender: Identification with Women, Identification with Feminists, and Their Interaction

    van Breen, Jolien A.; Spears, Russell; Kuppens, Toon; de Lemus, Soledad

    2017-01-01

    Across four studies, we examine multiple identities in the context of gender and propose that women's attitudes toward gender group membership are governed by two largely orthogonal dimensions of gender identity: identification with women and identification with feminists. We argue that identification with women reflects attitudes toward the content society gives to group membership: what does it mean to be a woman in terms of group characteristics, interests and values? Identification with feminists, on the other hand, is a politicized identity dimension reflecting attitudes toward the social position of the group: what does it mean to be a woman in terms of disadvantage, inequality, and relative status? We examine the utility of this multiple identity approach in four studies. Study 1 showed that identification with women reflects attitudes toward group characteristics, such as femininity and self-stereotyping, while identification with feminists reflects attitudes toward the group's social position, such as perceived sexism. The two dimensions are shown to be largely independent, and as such provide support for the multiple identity approach. In Studies 2–4, we examine the utility of this multiple identity approach in predicting qualitative differences in gender attitudes. Results show that specific combinations of identification with women and feminists predicted attitudes toward collective action and gender stereotypes. Higher identification with feminists led to endorsement of radical collective action (Study 2) and critical attitudes toward gender stereotypes (Studies 3–4), especially at lower levels of identification with women. The different combinations of high vs. low identification with women and feminists can be thought of as reflecting four theoretical identity “types.” A woman can be (1) strongly identified with neither women nor feminists (“low identifier”), (2) strongly identified with women but less so with feminists (

  6. A Multiple Identity Approach to Gender: Identification with Women, Identification with Feminists, and Their Interaction

    Jolien A. van Breen

    2017-06-01

    Full Text Available Across four studies, we examine multiple identities in the context of gender and propose that women's attitudes toward gender group membership are governed by two largely orthogonal dimensions of gender identity: identification with women and identification with feminists. We argue that identification with women reflects attitudes toward the content society gives to group membership: what does it mean to be a woman in terms of group characteristics, interests and values? Identification with feminists, on the other hand, is a politicized identity dimension reflecting attitudes toward the social position of the group: what does it mean to be a woman in terms of disadvantage, inequality, and relative status? We examine the utility of this multiple identity approach in four studies. Study 1 showed that identification with women reflects attitudes toward group characteristics, such as femininity and self-stereotyping, while identification with feminists reflects attitudes toward the group's social position, such as perceived sexism. The two dimensions are shown to be largely independent, and as such provide support for the multiple identity approach. In Studies 2–4, we examine the utility of this multiple identity approach in predicting qualitative differences in gender attitudes. Results show that specific combinations of identification with women and feminists predicted attitudes toward collective action and gender stereotypes. Higher identification with feminists led to endorsement of radical collective action (Study 2 and critical attitudes toward gender stereotypes (Studies 3–4, especially at lower levels of identification with women. The different combinations of high vs. low identification with women and feminists can be thought of as reflecting four theoretical identity “types.” A woman can be (1 strongly identified with neither women nor feminists (“low identifier”, (2 strongly identified with women but less so with feminists (

  7. A Multiple Identity Approach to Gender: Identification with Women, Identification with Feminists, and Their Interaction.

    van Breen, Jolien A; Spears, Russell; Kuppens, Toon; de Lemus, Soledad

    2017-01-01

    Across four studies, we examine multiple identities in the context of gender and propose that women's attitudes toward gender group membership are governed by two largely orthogonal dimensions of gender identity: identification with women and identification with feminists. We argue that identification with women reflects attitudes toward the content society gives to group membership: what does it mean to be a woman in terms of group characteristics, interests and values? Identification with feminists, on the other hand, is a politicized identity dimension reflecting attitudes toward the social position of the group: what does it mean to be a woman in terms of disadvantage, inequality, and relative status? We examine the utility of this multiple identity approach in four studies. Study 1 showed that identification with women reflects attitudes toward group characteristics, such as femininity and self-stereotyping, while identification with feminists reflects attitudes toward the group's social position, such as perceived sexism. The two dimensions are shown to be largely independent, and as such provide support for the multiple identity approach. In Studies 2-4, we examine the utility of this multiple identity approach in predicting qualitative differences in gender attitudes. Results show that specific combinations of identification with women and feminists predicted attitudes toward collective action and gender stereotypes. Higher identification with feminists led to endorsement of radical collective action (Study 2) and critical attitudes toward gender stereotypes (Studies 3-4), especially at lower levels of identification with women. The different combinations of high vs. low identification with women and feminists can be thought of as reflecting four theoretical identity "types." A woman can be (1) strongly identified with neither women nor feminists ("low identifier"), (2) strongly identified with women but less so with feminists ("traditional identifier"), (3

  8. Use of Multi-class Empirical Orthogonal Function for Identification of Hydrogeological Parameters and Spatiotemporal Pattern of Multiple Recharges in Groundwater Modeling

    Huang, C. L.; Hsu, N. S.; Yeh, W. W. G.; Hsieh, I. H.

    2017-12-01

    This study develops an innovative calibration method for regional groundwater modeling by using multi-class empirical orthogonal functions (EOFs). The developed method is an iterative approach. Prior to carrying out the iterative procedures, the groundwater storage hydrographs associated with the observation wells are calculated. The combined multi-class EOF amplitudes and EOF expansion coefficients of the storage hydrographs are then used to compute the initial gauss of the temporal and spatial pattern of multiple recharges. The initial guess of the hydrogeological parameters are also assigned according to in-situ pumping experiment. The recharges include net rainfall recharge and boundary recharge, and the hydrogeological parameters are riverbed leakage conductivity, horizontal hydraulic conductivity, vertical hydraulic conductivity, storage coefficient, and specific yield. The first step of the iterative algorithm is to conduct the numerical model (i.e. MODFLOW) by the initial guess / adjusted values of the recharges and parameters. Second, in order to determine the best EOF combination of the error storage hydrographs for determining the correction vectors, the objective function is devised as minimizing the root mean square error (RMSE) of the simulated storage hydrographs. The error storage hydrograph are the differences between the storage hydrographs computed from observed and simulated groundwater level fluctuations. Third, adjust the values of recharges and parameters and repeat the iterative procedures until the stopping criterion is reached. The established methodology was applied to the groundwater system of Ming-Chu Basin, Taiwan. The study period is from January 1st to December 2ed in 2012. Results showed that the optimal EOF combination for the multiple recharges and hydrogeological parameters can decrease the RMSE of the simulated storage hydrographs dramatically within three calibration iterations. It represents that the iterative approach that

  9. Geometrical model of multiple production

    Chikovani, Z.E.; Jenkovszky, L.L.; Kvaratshelia, T.M.; Struminskij, B.V.

    1988-01-01

    The relation between geometrical and KNO-scaling and their violation is studied in a geometrical model of multiple production of hadrons. Predictions concerning the behaviour of correlation coefficients at future accelerators are given

  10. Experimental identification of a comb-shaped chaotic region in multiple parameter spaces simulated by the Hindmarsh—Rose neuron model

    Jia, Bing

    2014-03-01

    A comb-shaped chaotic region has been simulated in multiple two-dimensional parameter spaces using the Hindmarsh—Rose (HR) neuron model in many recent studies, which can interpret almost all of the previously simulated bifurcation processes with chaos in neural firing patterns. In the present paper, a comb-shaped chaotic region in a two-dimensional parameter space was reproduced, which presented different processes of period-adding bifurcations with chaos with changing one parameter and fixed the other parameter at different levels. In the biological experiments, different period-adding bifurcation scenarios with chaos by decreasing the extra-cellular calcium concentration were observed from some neural pacemakers at different levels of extra-cellular 4-aminopyridine concentration and from other pacemakers at different levels of extra-cellular caesium concentration. By using the nonlinear time series analysis method, the deterministic dynamics of the experimental chaotic firings were investigated. The period-adding bifurcations with chaos observed in the experiments resembled those simulated in the comb-shaped chaotic region using the HR model. The experimental results show that period-adding bifurcations with chaos are preserved in different two-dimensional parameter spaces, which provides evidence of the existence of the comb-shaped chaotic region and a demonstration of the simulation results in different two-dimensional parameter spaces in the HR neuron model. The results also present relationships between different firing patterns in two-dimensional parameter spaces.

  11. Experimental identification of a comb-shaped chaotic region in multiple parameter spaces simulated by the Hindmarsh—Rose neuron model

    Jia Bing

    2014-01-01

    A comb-shaped chaotic region has been simulated in multiple two-dimensional parameter spaces using the Hindmarsh—Rose (HR) neuron model in many recent studies, which can interpret almost all of the previously simulated bifurcation processes with chaos in neural firing patterns. In the present paper, a comb-shaped chaotic region in a two-dimensional parameter space was reproduced, which presented different processes of period-adding bifurcations with chaos with changing one parameter and fixed the other parameter at different levels. In the biological experiments, different period-adding bifurcation scenarios with chaos by decreasing the extra-cellular calcium concentration were observed from some neural pacemakers at different levels of extra-cellular 4-aminopyridine concentration and from other pacemakers at different levels of extra-cellular caesium concentration. By using the nonlinear time series analysis method, the deterministic dynamics of the experimental chaotic firings were investigated. The period-adding bifurcations with chaos observed in the experiments resembled those simulated in the comb-shaped chaotic region using the HR model. The experimental results show that period-adding bifurcations with chaos are preserved in different two-dimensional parameter spaces, which provides evidence of the existence of the comb-shaped chaotic region and a demonstration of the simulation results in different two-dimensional parameter spaces in the HR neuron model. The results also present relationships between different firing patterns in two-dimensional parameter spaces

  12. Cross-Identification of Astronomical Catalogs on Multiple GPUs

    Lee, M. A.; Budavári, T.

    2013-10-01

    One of the most fundamental problems in observational astronomy is the cross-identification of sources. Observations are made in different wavelengths, at different times, and from different locations and instruments, resulting in a large set of independent observations. The scientific outcome is often limited by our ability to quickly perform meaningful associations between detections. The matching, however, is difficult scientifically, statistically, as well as computationally. The former two require detailed physical modeling and advanced probabilistic concepts; the latter is due to the large volumes of data and the problem's combinatorial nature. In order to tackle the computational challenge and to prepare for future surveys, whose measurements will be exponentially increasing in size past the scale of feasible CPU-based solutions, we developed a new implementation which addresses the issue by performing the associations on multiple Graphics Processing Units (GPUs). Our implementation utilizes up to 6 GPUs in combination with the Thrust library to achieve an over 40x speed up verses the previous best implementation running on a multi-CPU SQL Server.

  13. Identification of Known and Novel Recurrent Viral Sequences in Data from Multiple Patients and Multiple Cancers

    Friis-Nielsen, Jens; Kjartansdóttir, Kristín Rós; Mollerup, Sarah

    2016-01-01

    have developed a species independent pipeline that utilises sequence clustering for the identification of nucleotide sequences that co-occur across multiple sequencing data instances. We applied the workflow to 686 sequencing libraries from 252 cancer samples of different cancer and tissue types, 32...

  14. Metabolite identification through multiple kernel learning on fragmentation trees.

    Shen, Huibin; Dührkop, Kai; Böcker, Sebastian; Rousu, Juho

    2014-06-15

    Metabolite identification from tandem mass spectrometric data is a key task in metabolomics. Various computational methods have been proposed for the identification of metabolites from tandem mass spectra. Fragmentation tree methods explore the space of possible ways in which the metabolite can fragment, and base the metabolite identification on scoring of these fragmentation trees. Machine learning methods have been used to map mass spectra to molecular fingerprints; predicted fingerprints, in turn, can be used to score candidate molecular structures. Here, we combine fragmentation tree computations with kernel-based machine learning to predict molecular fingerprints and identify molecular structures. We introduce a family of kernels capturing the similarity of fragmentation trees, and combine these kernels using recently proposed multiple kernel learning approaches. Experiments on two large reference datasets show that the new methods significantly improve molecular fingerprint prediction accuracy. These improvements result in better metabolite identification, doubling the number of metabolites ranked at the top position of the candidates list. © The Author 2014. Published by Oxford University Press.

  15. Network-based identification of biomarkers coexpressed with multiple pathways.

    Guo, Nancy Lan; Wan, Ying-Wooi

    2014-01-01

    Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson's correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson's correlation networks when evaluated with MSigDB database.

  16. Research on the Multiple Factors Influencing Human Identification Based on Pyroelectric Infrared Sensors

    Lou, Ping; Hu, Jianmin

    2018-01-01

    Analysis of the multiple factors affecting human identification ability based on pyroelectric infrared technology is a complex problem. First, we examine various sensed pyroelectric waveforms of the human body thermal infrared signal and reveal a mechanism for affecting human identification. Then, we find that the mechanism is decided by the distance, human target, pyroelectric infrared (PIR) sensor, the body type, human moving velocity, signal modulation mask, and Fresnel lens. The mapping relationship between the sensed waveform and multiple influencing factors is established, and a group of mathematical models are deduced which fuse the macro factors and micro factors. Finally, the experimental results show the macro-factors indirectly affect the recognition ability of human based on the pyroelectric technology. At the same time, the correctness and effectiveness of the mathematical models is also verified, which make it easier to obtain more pyroelectric infrared information about the human body for discriminating human targets. PMID:29462908

  17. Adaptive Active Noise Suppression Using Multiple Model Switching Strategy

    Quanzhen Huang

    2017-01-01

    Full Text Available Active noise suppression for applications where the system response varies with time is a difficult problem. The computation burden for the existing control algorithms with online identification is heavy and easy to cause control system instability. A new active noise control algorithm is proposed in this paper by employing multiple model switching strategy for secondary path varying. The computation is significantly reduced. Firstly, a noise control system modeling method is proposed for duct-like applications. Then a multiple model adaptive control algorithm is proposed with a new multiple model switching strategy based on filter-u least mean square (FULMS algorithm. Finally, the proposed algorithm was implemented on Texas Instruments digital signal processor (DSP TMS320F28335 and real time experiments were done to test the proposed algorithm and FULMS algorithm with online identification. Experimental verification tests show that the proposed algorithm is effective with good noise suppression performance.

  18. Encryption and validation of multiple signals for optical identification systems

    Perez-Cabre, E [Universitat PoliteGcnica de Catalunya, Department Optica i Optometria, Violinista Vellsola 37, 08222 Terrassa (Spain); Millan, M S [Universitat PoliteGcnica de Catalunya, Department Optica i Optometria, Violinista Vellsola 37, 08222 Terrassa (Spain); Javidi, B [University of Connecticut, Electrical and Computer Engineering Department, 371 Fairfield Road, CT 06269 Storrs (United States)

    2007-07-15

    Multifactor encryption-authentication technique reinforces optical security by allowing the simultaneous A N D-verification of more than one primary image. Instead of basing the identification on a unique signature or piece of information, our goal is to authenticate a given person, object, vehicle by the simultaneous recognition of several factors. Some of them are intrinsic to the person and object or vehicle under control. Other factors, act as keys of the authentication step. Such a system is proposed for situations such as the access control to restricted areas, where the demand of security is high. The multifactor identification method involves double random-phase encoding, fully phase-based encryption and a combined nonlinear joint transform correlator and a classical 4f-correlator for simultaneous recognition and authentication of multiple images. The encoded signal fulfils the general requirements of invisible content, extreme difficulty in counterfeiting and real-time automatic verification. Four reference double-phase encoded images are compared with the retrieved input images obtained in situ from the person or the vehicle whose authentication is wanted and from a database. A recognition step based on the correlation between the signatures and the stored references determines the authentication or rejection of the person and object under surveillance.

  19. Encryption and validation of multiple signals for optical identification systems

    Perez-Cabre, E; Millan, M S; Javidi, B

    2007-01-01

    Multifactor encryption-authentication technique reinforces optical security by allowing the simultaneous A N D-verification of more than one primary image. Instead of basing the identification on a unique signature or piece of information, our goal is to authenticate a given person, object, vehicle by the simultaneous recognition of several factors. Some of them are intrinsic to the person and object or vehicle under control. Other factors, act as keys of the authentication step. Such a system is proposed for situations such as the access control to restricted areas, where the demand of security is high. The multifactor identification method involves double random-phase encoding, fully phase-based encryption and a combined nonlinear joint transform correlator and a classical 4f-correlator for simultaneous recognition and authentication of multiple images. The encoded signal fulfils the general requirements of invisible content, extreme difficulty in counterfeiting and real-time automatic verification. Four reference double-phase encoded images are compared with the retrieved input images obtained in situ from the person or the vehicle whose authentication is wanted and from a database. A recognition step based on the correlation between the signatures and the stored references determines the authentication or rejection of the person and object under surveillance

  20. Noisy: Identification of problematic columns in multiple sequence alignments

    Grünewald Stefan

    2008-06-01

    Full Text Available Abstract Motivation Sequence-based methods for phylogenetic reconstruction from (nucleic acid sequence data are notoriously plagued by two effects: homoplasies and alignment errors. Large evolutionary distances imply a large number of homoplastic sites. As most protein-coding genes show dramatic variations in substitution rates that are not uncorrelated across the sequence, this often leads to a patchwork pattern of (i phylogenetically informative and (ii effectively randomized regions. In highly variable regions, furthermore, alignment errors accumulate resulting in sometimes misleading signals in phylogenetic reconstruction. Results We present here a method that, based on assessing the distribution of character states along a cyclic ordering of the taxa, allows the identification of phylogenetically uninformative homoplastic sites in a multiple sequence alignment. Removal of these sites appears to improve the performance of phylogenetic reconstruction algorithms as measured by various indices of "tree quality". In particular, we obtain more stable trees due to the exclusion of phylogenetically incompatible sites that most likely represent strongly randomized characters. Software The computer program noisy implements this approach. It can be employed to improving phylogenetic reconstruction capability with quite a considerable success rate whenever (1 the average bootstrap support obtained from the original alignment is low, and (2 there are sufficiently many taxa in the data set – at least, say, 12 to 15 taxa. The software can be obtained under the GNU Public License from http://www.bioinf.uni-leipzig.de/Software/noisy/.

  1. LPV system identification using series expansion models

    Toth, R.; Heuberger, P.S.C.; Hof, Van den P.M.J.; Santos, dos P.L.; Perdicoúlis, T.P.A.; Novara, C.; Ramos, J.A.; Rivera, D.E.

    2011-01-01

    This review volume reports the state-of-the-art in Linear Parameter Varying (LPV) system identification. Written by world renowned researchers, the book contains twelve chapters, focusing on the most recent LPV identification methods for both discrete-time and continuous-time models, using different

  2. Modeling and identification for robot motion control

    Kostic, D.; Jager, de A.G.; Steinbuch, M.; Kurfess, T.R.

    2004-01-01

    This chapter deals with the problems of robot modelling and identification for high-performance model-based motion control. A derivation of robot kinematic and dynamic models was explained. Modelling of friction effects was also discussed. Use of a writing task to establish correctness of the models

  3. Identification of Known and Novel Recurrent Viral Sequences in Data from Multiple Patients and Multiple Cancers

    Friis-Nielsen, Jens; Kjartansdóttir, Kristín Rós; Mollerup, Sarah

    2016-01-01

    non-template controls, and 24 test samples. Recurrent sequences were statistically associated to biological, methodological or technical features with the aim to identify novel pathogens or plausible contaminants that may associate to a particular kit or method. We provide examples of identified......Virus discovery from high throughput sequencing data often follows a bottom-up approach where taxonomic annotation takes place prior to association to disease. Albeit effective in some cases, the approach fails to detect novel pathogens and remote variants not present in reference databases. We...... have developed a species independent pipeline that utilises sequence clustering for the identification of nucleotide sequences that co-occur across multiple sequencing data instances. We applied the workflow to 686 sequencing libraries from 252 cancer samples of different cancer and tissue types, 32...

  4. Multiple Indicator Stationary Time Series Models.

    Sivo, Stephen A.

    2001-01-01

    Discusses the propriety and practical advantages of specifying multivariate time series models in the context of structural equation modeling for time series and longitudinal panel data. For time series data, the multiple indicator model specification improves on classical time series analysis. For panel data, the multiple indicator model…

  5. Modeling Multiple Causes of Carcinogenesis

    Jones, T D

    1999-01-24

    An array of epidemiological results and databases on test animal indicate that risk of cancer and atherosclerosis can be up- or down-regulated by diet through a range of 200%. Other factors contribute incrementally and include the natural terrestrial environment and various human activities that jointly produce complex exposures to endotoxin-producing microorganisms, ionizing radiations, and chemicals. Ordinary personal habits and simple physical irritants have been demonstrated to affect the immune response and risk of disease. There tends to be poor statistical correlation of long-term risk with single agent exposures incurred throughout working careers. However, Agency recommendations for control of hazardous exposures to humans has been substance-specific instead of contextually realistic even though there is consistent evidence for common mechanisms of toxicological and carcinogenic action. That behavior seems to be best explained by molecular stresses from cellular oxygen metabolism and phagocytosis of antigenic invasion as well as breakdown of normal metabolic compounds associated with homeostatic- and injury-related renewal of cells. There is continually mounting evidence that marrow stroma, comprised largely of monocyte-macrophages and fibroblasts, is important to phagocytic and cytokinetic response, but the complex action of the immune process is difficult to infer from first-principle logic or biomarkers of toxic injury. The many diverse database studies all seem to implicate two important processes, i.e., the univalent reduction of molecular oxygen and breakdown of aginuine, an amino acid, by hydrolysis or digestion of protein which is attendant to normal antigen-antibody action. This behavior indicates that protection guidelines and risk coefficients should be context dependent to include reference considerations of the composite action of parameters that mediate oxygen metabolism. A logic of this type permits the realistic common-scale modeling of

  6. CEAI: CCM based Email Authorship Identification Model

    Nizamani, Sarwat; Memon, Nasrullah

    2013-01-01

    In this paper we present a model for email authorship identification (EAI) by employing a Cluster-based Classification (CCM) technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature-set to include some...... more interesting and effective features for email authorship identification (e.g. the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell). We also included Info Gain feature selection based...... reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM)-based models, as well as the models proposed by Iqbal et al. [1, 2]. The proposed model attains an accuracy rate of 94% for 10...

  7. Identification and Multiplicity of Double Vowels in Cochlear Implant Users

    Kwon, Bomjun J.; Perry, Trevor T.

    2014-01-01

    Purpose: The present study examined cochlear implant (CI) users' perception of vowels presented concurrently (i.e., "double vowels") to further our understanding of auditory grouping in electric hearing. Method: Identification of double vowels and single vowels was measured with 10 CI subjects. Fundamental frequencies (F0s) of…

  8. Structural system identification: Structural dynamics model validation

    Red-Horse, J.R.

    1997-04-01

    Structural system identification is concerned with the development of systematic procedures and tools for developing predictive analytical models based on a physical structure`s dynamic response characteristics. It is a multidisciplinary process that involves the ability (1) to define high fidelity physics-based analysis models, (2) to acquire accurate test-derived information for physical specimens using diagnostic experiments, (3) to validate the numerical simulation model by reconciling differences that inevitably exist between the analysis model and the experimental data, and (4) to quantify uncertainties in the final system models and subsequent numerical simulations. The goal of this project was to develop structural system identification techniques and software suitable for both research and production applications in code and model validation.

  9. Modeling and identification in structural dynamics

    Jayakumar, Paramsothy

    1987-01-01

    Analytical modeling of structures subjected to ground motions is an important aspect of fully dynamic earthquake-resistant design. In general, linear models are only sufficient to represent structural responses resulting from earthquake motions of small amplitudes. However, the response of structures during strong ground motions is highly nonlinear and hysteretic. System identification is an effective tool for developing analytical models from experimental data. Testing of full-scale prot...

  10. Multiplicity Control in Structural Equation Modeling

    Cribbie, Robert A.

    2007-01-01

    Researchers conducting structural equation modeling analyses rarely, if ever, control for the inflated probability of Type I errors when evaluating the statistical significance of multiple parameters in a model. In this study, the Type I error control, power and true model rates of famsilywise and false discovery rate controlling procedures were…

  11. Model Identification of Integrated ARMA Processes

    Stadnytska, Tetiana; Braun, Simone; Werner, Joachim

    2008-01-01

    This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can…

  12. Parameter identification in the logistic STAR model

    Ekner, Line Elvstrøm; Nejstgaard, Emil

    We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter is that th...

  13. THE RELIABILITY OF IDENTIFICATION EVIDENCE WITH MULTIPLE LINEUPS

    Nick J. Broers

    2013-01-01

    Full Text Available This study aimed to establish the diagnostic value of multiple lineup decisions made for portrait, body, and profile lineups, including multiple target/suspect choices, rejections, foil choices, and don’t know answers. A total of 192 participants identified a thief and a victim of theft from independent simultaneous target-present or target-absent 6-person portrait, body and profile lineups after watching one of two stimulus films. As hypothesized, multiple target/suspect choices had incriminating value whereas multiple rejections, foil choices, and don’t know answers had mostly exonerating value. For suspect choices, the combination of all three lineup modes consistently elicited high diagnosticities across targets. Combinations of non-suspect choices (rejections, foil choices, or don’t know answers were less successful and the different combinations showed less consistency in terms of diagnosticity. It was concluded that the use of multiple lineups, such as different facial poses and different aspects of a person should be particularly beneficial in three situations: if a witness only saw the perpetrator from a pose different than the frontal view normally used for lineups; if one or more witnesses saw the perpetrator from more than one perspective; and if different witnesses saw the perpetrator from different perspectives.

  14. Integrated identification, modeling and control with applications

    Shi, Guojun

    This thesis deals with the integration of system design, identification, modeling and control. In particular, six interdisciplinary engineering problems are addressed and investigated. Theoretical results are established and applied to structural vibration reduction and engine control problems. First, the data-based LQG control problem is formulated and solved. It is shown that a state space model is not necessary to solve this problem; rather a finite sequence from the impulse response is the only model data required to synthesize an optimal controller. The new theory avoids unnecessary reliance on a model, required in the conventional design procedure. The infinite horizon model predictive control problem is addressed for multivariable systems. The basic properties of the receding horizon implementation strategy is investigated and the complete framework for solving the problem is established. The new theory allows the accommodation of hard input constraints and time delays. The developed control algorithms guarantee the closed loop stability. A closed loop identification and infinite horizon model predictive control design procedure is established for engine speed regulation. The developed algorithms are tested on the Cummins Engine Simulator and desired results are obtained. A finite signal-to-noise ratio model is considered for noise signals. An information quality index is introduced which measures the essential information precision required for stabilization. The problems of minimum variance control and covariance control are formulated and investigated. Convergent algorithms are developed for solving the problems of interest. The problem of the integrated passive and active control design is addressed in order to improve the overall system performance. A design algorithm is developed, which simultaneously finds: (i) the optimal values of the stiffness and damping ratios for the structure, and (ii) an optimal output variance constrained stabilizing

  15. Quality of life and patient preferences: identification of subgroups of multiple sclerosis patients.

    Rosato, Rosalba; Testa, Silvia; Oggero, Alessandra; Molinengo, Giorgia; Bertolotto, Antonio

    2015-09-01

    The aim of this study was to estimate preferences related to quality of life attributes in people with multiple sclerosis, by keeping heterogeneity of patient preference in mind, using the latent class approach. A discrete choice experiment survey was developed using the following attributes: activities of daily living, instrumental activities of daily living, pain/fatigue, anxiety/depression and attention/concentration. Choice sets were presented as pairs of hypothetical health status, based upon a fractional factorial design. The latent class logit model estimated on 152 patients identified three subpopulations, which, respectively, attached more importance to: (1) the physical dimension; (2) pain/fatigue and anxiety/depression; and (3) instrumental activities of daily living impairments, anxiety/depression and attention/concentration. A posterior analysis suggests that the latent class membership may be related to an individual's age to some extent, or to diagnosis and treatment, while apart from energy dimension, no significant difference exists between latent groups, with regard to Multiple Sclerosis Quality of Life-54 scales. A quality of life preference-based utility measure for people with multiple sclerosis was developed. These utility values allow identification of a hierarchic priority among different aspects of quality of life and may allow physicians to develop a care programme tailored to patient needs.

  16. Non-parametric identification of multivariable systems : a local rational modeling approach with application to a vibration isolation benchmark

    Voorhoeve, R.J.; van der Maas, A.; Oomen, T.A.J.

    2018-01-01

    Frequency response function (FRF) identification is often used as a basis for control systems design and as a starting point for subsequent parametric system identification. The aim of this paper is to develop a multiple-input multiple-output (MIMO) local parametric modeling approach for FRF

  17. Hazard identification based on plant functional modelling

    Rasmussen, B.; Whetton, C.

    1993-10-01

    A major objective of the present work is to provide means for representing a process plant as a socio-technical system, so as to allow hazard identification at a high level. The method includes technical, human and organisational aspects and is intended to be used for plant level hazard identification so as to identify critical areas and the need for further analysis using existing methods. The first part of the method is the preparation of a plant functional model where a set of plant functions link together hardware, software, operations, work organisation and other safety related aspects of the plant. The basic principle of the functional modelling is that any aspect of the plant can be represented by an object (in the sense that this term is used in computer science) based upon an Intent (or goal); associated with each Intent are Methods, by which the Intent is realized, and Constraints, which limit the Intent. The Methods and Constraints can themselves be treated as objects and decomposed into lower-level Intents (hence the procedure is known as functional decomposition) so giving rise to a hierarchical, object-oriented structure. The plant level hazard identification is carried out on the plant functional model using the Concept Hazard Analysis method. In this, the user will be supported by checklists and keywords and the analysis is structured by pre-defined worksheets. The preparation of the plant functional model and the performance of the hazard identification can be carried out manually or with computer support. (au) (4 tabs., 10 ills., 7 refs.)

  18. Identification of potential biomarkers from microarray experiments using multiple criteria optimization

    Sánchez-Peña, Matilde L; Isaza, Clara E; Pérez-Morales, Jaileene; Rodríguez-Padilla, Cristina; Castro, José M; Cabrera-Ríos, Mauricio

    2013-01-01

    Microarray experiments are capable of determining the relative expression of tens of thousands of genes simultaneously, thus resulting in very large databases. The analysis of these databases and the extraction of biologically relevant knowledge from them are challenging tasks. The identification of potential cancer biomarker genes is one of the most important aims for microarray analysis and, as such, has been widely targeted in the literature. However, identifying a set of these genes consistently across different experiments, researches, microarray platforms, or cancer types is still an elusive endeavor. Besides the inherent difficulty of the large and nonconstant variability in these experiments and the incommensurability between different microarray technologies, there is the issue of the users having to adjust a series of parameters that significantly affect the outcome of the analyses and that do not have a biological or medical meaning. In this study, the identification of potential cancer biomarkers from microarray data is casted as a multiple criteria optimization (MCO) problem. The efficient solutions to this problem, found here through data envelopment analysis (DEA), are associated to genes that are proposed as potential cancer biomarkers. The method does not require any parameter adjustment by the user, and thus fosters repeatability. The approach also allows the analysis of different microarray experiments, microarray platforms, and cancer types simultaneously. The results include the analysis of three publicly available microarray databases related to cervix cancer. This study points to the feasibility of modeling the selection of potential cancer biomarkers from microarray data as an MCO problem and solve it using DEA. Using MCO entails a new optic to the identification of potential cancer biomarkers as it does not require the definition of a threshold value to establish significance for a particular gene and the selection of a normalization

  19. Design of Xen Hybrid Multiple Police Model

    Sun, Lei; Lin, Renhao; Zhu, Xianwei

    2017-10-01

    Virtualization Technology has attracted more and more attention. As a popular open-source virtualization tools, XEN is used more and more frequently. Xsm, XEN security model, has also been widespread concern. The safety status classification has not been established in the XSM, and it uses the virtual machine as a managed object to make Dom0 a unique administrative domain that does not meet the minimum privilege. According to these questions, we design a Hybrid multiple police model named SV_HMPMD that organically integrates multiple single security policy models include DTE,RBAC,BLP. It can fullfill the requirement of confidentiality and integrity for security model and use different particle size to different domain. In order to improve BLP’s practicability, the model introduce multi-level security labels. In order to divide the privilege in detail, we combine DTE with RBAC. In order to oversize privilege, we limit the privilege of domain0.

  20. Multiple Model Approaches to Modelling and Control,

    on the ease with which prior knowledge can be incorporated. It is interesting to note that researchers in Control Theory, Neural Networks,Statistics, Artificial Intelligence and Fuzzy Logic have more or less independently developed very similar modelling methods, calling them Local ModelNetworks, Operating......, and allows direct incorporation of high-level and qualitative plant knowledge into themodel. These advantages have proven to be very appealing for industrial applications, and the practical, intuitively appealing nature of the framework isdemonstrated in chapters describing applications of local methods...... to problems in the process industries, biomedical applications and autonomoussystems. The successful application of the ideas to demanding problems is already encouraging, but creative development of the basic framework isneeded to better allow the integration of human knowledge with automated learning...

  1. Identification of Multiple Loci Associated with Social Parasitism in Honeybees.

    Wallberg, Andreas; Pirk, Christian W; Allsopp, Mike H; Webster, Matthew T

    2016-06-01

    In colonies of the honeybee Apis mellifera, the queen is usually the only reproductive female, which produces new females (queens and workers) by laying fertilized eggs. However, in one subspecies of A. mellifera, known as the Cape bee (A. m. capensis), worker bees reproduce asexually by thelytoky, an abnormal form of meiosis where two daughter nucleii fuse to form single diploid eggs, which develop into females without being fertilized. The Cape bee also exhibits a suite of phenotypes that facilitate social parasitism whereby workers lay such eggs in foreign colonies so their offspring can exploit their resources. The genetic basis of this switch to social parasitism in the Cape bee is unknown. To address this, we compared genome variation in a sample of Cape bees with other African populations. We find genetic divergence between these populations to be very low on average but identify several regions of the genome with extreme differentiation. The regions are strongly enriched for signals of selection in Cape bees, indicating that increased levels of positive selection have produced the unique set of derived phenotypic traits in this subspecies. Genetic variation within these regions allows unambiguous genetic identification of Cape bees and likely underlies the genetic basis of social parasitism. The candidate loci include genes involved in ecdysteroid signaling and juvenile hormone and dopamine biosynthesis, which may regulate worker ovary activation and others whose products localize at the centrosome and are implicated in chromosomal segregation during meiosis. Functional analysis of these loci will yield insights into the processes of reproduction and chemical signaling in both parasitic and non-parasitic populations and advance understanding of the process of normal and atypical meiosis.

  2. Identification of Multiple Loci Associated with Social Parasitism in Honeybees.

    Andreas Wallberg

    2016-06-01

    Full Text Available In colonies of the honeybee Apis mellifera, the queen is usually the only reproductive female, which produces new females (queens and workers by laying fertilized eggs. However, in one subspecies of A. mellifera, known as the Cape bee (A. m. capensis, worker bees reproduce asexually by thelytoky, an abnormal form of meiosis where two daughter nucleii fuse to form single diploid eggs, which develop into females without being fertilized. The Cape bee also exhibits a suite of phenotypes that facilitate social parasitism whereby workers lay such eggs in foreign colonies so their offspring can exploit their resources. The genetic basis of this switch to social parasitism in the Cape bee is unknown. To address this, we compared genome variation in a sample of Cape bees with other African populations. We find genetic divergence between these populations to be very low on average but identify several regions of the genome with extreme differentiation. The regions are strongly enriched for signals of selection in Cape bees, indicating that increased levels of positive selection have produced the unique set of derived phenotypic traits in this subspecies. Genetic variation within these regions allows unambiguous genetic identification of Cape bees and likely underlies the genetic basis of social parasitism. The candidate loci include genes involved in ecdysteroid signaling and juvenile hormone and dopamine biosynthesis, which may regulate worker ovary activation and others whose products localize at the centrosome and are implicated in chromosomal segregation during meiosis. Functional analysis of these loci will yield insights into the processes of reproduction and chemical signaling in both parasitic and non-parasitic populations and advance understanding of the process of normal and atypical meiosis.

  3. Structural model analysis of multiple quantitative traits.

    Renhua Li

    2006-07-01

    Full Text Available We introduce a method for the analysis of multilocus, multitrait genetic data that provides an intuitive and precise characterization of genetic architecture. We show that it is possible to infer the magnitude and direction of causal relationships among multiple correlated phenotypes and illustrate the technique using body composition and bone density data from mouse intercross populations. Using these techniques we are able to distinguish genetic loci that affect adiposity from those that affect overall body size and thus reveal a shortcoming of standardized measures such as body mass index that are widely used in obesity research. The identification of causal networks sheds light on the nature of genetic heterogeneity and pleiotropy in complex genetic systems.

  4. A feature point identification method for positron emission particle tracking with multiple tracers

    Wiggins, Cody, E-mail: cwiggin2@vols.utk.edu [University of Tennessee-Knoxville, Department of Physics and Astronomy, 1408 Circle Drive, Knoxville, TN 37996 (United States); Santos, Roque [University of Tennessee-Knoxville, Department of Nuclear Engineering (United States); Escuela Politécnica Nacional, Departamento de Ciencias Nucleares (Ecuador); Ruggles, Arthur [University of Tennessee-Knoxville, Department of Nuclear Engineering (United States)

    2017-01-21

    A novel detection algorithm for Positron Emission Particle Tracking (PEPT) with multiple tracers based on optical feature point identification (FPI) methods is presented. This new method, the FPI method, is compared to a previous multiple PEPT method via analyses of experimental and simulated data. The FPI method outperforms the older method in cases of large particle numbers and fine time resolution. Simulated data show the FPI method to be capable of identifying 100 particles at 0.5 mm average spatial error. Detection error is seen to vary with the inverse square root of the number of lines of response (LORs) used for detection and increases as particle separation decreases. - Highlights: • A new approach to positron emission particle tracking is presented. • Using optical feature point identification analogs, multiple particle tracking is achieved. • Method is compared to previous multiple particle method. • Accuracy and applicability of method is explored.

  5. Multiple model cardinalized probability hypothesis density filter

    Georgescu, Ramona; Willett, Peter

    2011-09-01

    The Probability Hypothesis Density (PHD) filter propagates the first-moment approximation to the multi-target Bayesian posterior distribution while the Cardinalized PHD (CPHD) filter propagates both the posterior likelihood of (an unlabeled) target state and the posterior probability mass function of the number of targets. Extensions of the PHD filter to the multiple model (MM) framework have been published and were implemented either with a Sequential Monte Carlo or a Gaussian Mixture approach. In this work, we introduce the multiple model version of the more elaborate CPHD filter. We present the derivation of the prediction and update steps of the MMCPHD particularized for the case of two target motion models and proceed to show that in the case of a single model, the new MMCPHD equations reduce to the original CPHD equations.

  6. Predictive performance models and multiple task performance

    Wickens, Christopher D.; Larish, Inge; Contorer, Aaron

    1989-01-01

    Five models that predict how performance of multiple tasks will interact in complex task scenarios are discussed. The models are shown in terms of the assumptions they make about human operator divided attention. The different assumptions about attention are then empirically validated in a multitask helicopter flight simulation. It is concluded from this simulation that the most important assumption relates to the coding of demand level of different component tasks.

  7. Identification of critical locations across multiple infrastructures for terrorist actions

    Patterson, S.A.; Apostolakis, G.E.

    2007-01-01

    This paper presents a possible approach to ranking geographic regions that can influence multiple infrastructures. Once ranked, decision makers can determine whether these regions are critical locations based on their susceptibility to terrorist acts. We identify these locations by calculating a value for a geographic region that represents the combined values to the decision makers of all the infrastructures crossing through that region. These values, as well as the size of the geographic region, are conditional on an assumed destructive threat of a given size. In our case study, the threat is assumed to be minor, e.g., a bomb that can affect objects within 7 m of it. This approach first requires an assessment of the users of the system. During this assessment, each user is assigned a performance index (PI) based on the disutility of the loss of each infrastructure's resource via multi-attribute utility theory (MAUT). A Monte Carlo network analysis is then performed to develop importance measures (IM) for the elements of each infrastructure for their ability to service each user. We combine the IMs with the user PIs to a value that we call valued worth (VW) for each infrastructure's elements independently. Then we use spatial analysis techniques within a geographic information system (GIS) to combine the VWs of each infrastructure's elements in a geographic area, conditional on the threat, into a total value we call geographic valued worth (GVW). The GVW is displayed graphically in the GIS system in a color scheme that shows the numerical ranking of these geographic areas. The map and rankings are then submitted to the decision makers to better allocate anti-terrorism resources. A case study of this methodology is performed on the Massachusetts Institute of Technology (MIT) campus. The results of the study show how the methodology can bring attention to areas that are important when several infrastructures are considered, but may be ignored when infrastructures

  8. Maximizing the sensitivity and reliability of peptide identification in large-scale proteomic experiments by harnessing multiple search engines.

    Yu, Wen; Taylor, J Alex; Davis, Michael T; Bonilla, Leo E; Lee, Kimberly A; Auger, Paul L; Farnsworth, Chris C; Welcher, Andrew A; Patterson, Scott D

    2010-03-01

    Despite recent advances in qualitative proteomics, the automatic identification of peptides with optimal sensitivity and accuracy remains a difficult goal. To address this deficiency, a novel algorithm, Multiple Search Engines, Normalization and Consensus is described. The method employs six search engines and a re-scoring engine to search MS/MS spectra against protein and decoy sequences. After the peptide hits from each engine are normalized to error rates estimated from the decoy hits, peptide assignments are then deduced using a minimum consensus model. These assignments are produced in a series of progressively relaxed false-discovery rates, thus enabling a comprehensive interpretation of the data set. Additionally, the estimated false-discovery rate was found to have good concordance with the observed false-positive rate calculated from known identities. Benchmarking against standard proteins data sets (ISBv1, sPRG2006) and their published analysis, demonstrated that the Multiple Search Engines, Normalization and Consensus algorithm consistently achieved significantly higher sensitivity in peptide identifications, which led to increased or more robust protein identifications in all data sets compared with prior methods. The sensitivity and the false-positive rate of peptide identification exhibit an inverse-proportional and linear relationship with the number of participating search engines.

  9. Advances in Modelling, System Identification and Parameter ...

    Authors show, using numerical simulation for two system functions, the improvement in percentage normalized ... of nonlinear systems. The approach is to use multiple linearizing models fitted along the operating trajectories. ... over emphasized in the light of present day high level of research activity in the field of aerospace ...

  10. Automatic identification approach for high-performance liquid chromatography-multiple reaction monitoring fatty acid global profiling.

    Tie, Cai; Hu, Ting; Jia, Zhi-Xin; Zhang, Jin-Lan

    2015-08-18

    Fatty acids (FAs) are a group of lipid molecules that are essential to organisms. As potential biomarkers for different diseases, FAs have attracted increasing attention from both biological researchers and the pharmaceutical industry. A sensitive and accurate method for globally profiling and identifying FAs is required for biomarker discovery. The high selectivity and sensitivity of high-performance liquid chromatography-multiple reaction monitoring (HPLC-MRM) gives it great potential to fulfill the need to identify FAs from complicated matrices. This paper developed a new approach for global FA profiling and identification for HPLC-MRM FA data mining. Mathematical models for identifying FAs were simulated using the isotope-induced retention time (RT) shift (IRS) and peak area ratios between parallel isotope peaks for a series of FA standards. The FA structures were predicated using another model based on the RT and molecular weight. Fully automated FA identification software was coded using the Qt platform based on these mathematical models. Different samples were used to verify the software. A high identification efficiency (greater than 75%) was observed when 96 FA species were identified in plasma. This FAs identification strategy promises to accelerate FA research and applications.

  11. A neural network model of lateralization during letter identification.

    Shevtsova, N; Reggia, J A

    1999-03-01

    The causes of cerebral lateralization of cognitive and other functions are currently not well understood. To investigate one aspect of function lateralization, a bihemispheric neural network model for a simple visual identification task was developed that has two parallel interacting paths of information processing. The model is based on commonly accepted concepts concerning neural connectivity, activity dynamics, and synaptic plasticity. A combination of both unsupervised (Hebbian) and supervised (Widrow-Hoff) learning rules is used to train the model to identify a small set of letters presented as input stimuli in the left visual hemifield, in the central position, and in the right visual hemifield. Each visual hemifield projects onto the contralateral hemisphere, and the two hemispheres interact via a simulated corpus callosum. The contribution of each individual hemisphere to the process of input stimuli identification was studied for a variety of underlying asymmetries. The results indicate that multiple asymmetries may cause lateralization. Lateralization occurred toward the side having larger size, higher excitability, or higher learning rate parameters. It appeared more intensively with strong inhibitory callosal connections, supporting the hypothesis that the corpus callosum plays a functionally inhibitory role. The model demonstrates clearly the dependence of lateralization on different hemisphere parameters and suggests that computational models can be useful in better understanding the mechanisms underlying emergence of lateralization.

  12. MSblender: A probabilistic approach for integrating peptide identifications from multiple database search engines.

    Kwon, Taejoon; Choi, Hyungwon; Vogel, Christine; Nesvizhskii, Alexey I; Marcotte, Edward M

    2011-07-01

    Shotgun proteomics using mass spectrometry is a powerful method for protein identification but suffers limited sensitivity in complex samples. Integrating peptide identifications from multiple database search engines is a promising strategy to increase the number of peptide identifications and reduce the volume of unassigned tandem mass spectra. Existing methods pool statistical significance scores such as p-values or posterior probabilities of peptide-spectrum matches (PSMs) from multiple search engines after high scoring peptides have been assigned to spectra, but these methods lack reliable control of identification error rates as data are integrated from different search engines. We developed a statistically coherent method for integrative analysis, termed MSblender. MSblender converts raw search scores from search engines into a probability score for every possible PSM and properly accounts for the correlation between search scores. The method reliably estimates false discovery rates and identifies more PSMs than any single search engine at the same false discovery rate. Increased identifications increment spectral counts for most proteins and allow quantification of proteins that would not have been quantified by individual search engines. We also demonstrate that enhanced quantification contributes to improve sensitivity in differential expression analyses.

  13. Are multiple-trial experiments appropriate for eyewitness identification studies? Accuracy, choosing, and confidence across trials.

    Mansour, J K; Beaudry, J L; Lindsay, R C L

    2017-12-01

    Eyewitness identification experiments typically involve a single trial: A participant views an event and subsequently makes a lineup decision. As compared to this single-trial paradigm, multiple-trial designs are more efficient, but significantly reduce ecological validity and may affect the strategies that participants use to make lineup decisions. We examined the effects of a number of forensically relevant variables (i.e., memory strength, type of disguise, degree of disguise, and lineup type) on eyewitness accuracy, choosing, and confidence across 12 target-present and 12 target-absent lineup trials (N = 349; 8,376 lineup decisions). The rates of correct rejections and choosing (across both target-present and target-absent lineups) did not vary across the 24 trials, as reflected by main effects or interactions with trial number. Trial number had a significant but trivial quadratic effect on correct identifications (OR = 0.99) and interacted significantly, but again trivially, with disguise type (OR = 1.00). Trial number did not significantly influence participants' confidence in correct identifications, confidence in correct rejections, or confidence in target-absent selections. Thus, multiple-trial designs appear to have minimal effects on eyewitness accuracy, choosing, and confidence. Researchers should thus consider using multiple-trial designs for conducting eyewitness identification experiments.

  14. Application of Multiple Evaluation Models in Brazil

    Rafael Victal Saliba

    2008-07-01

    Full Text Available Based on two different samples, this article tests the performance of a number of Value Drivers commonly used for evaluating companies by finance practitioners, through simple regression models of cross-section type which estimate the parameters associated to each Value Driver, denominated Market Multiples. We are able to diagnose the behavior of several multiples in the period 1994-2004, with an outlook also on the particularities of the economic activities performed by the sample companies (and their impacts on the performance through a subsequent analysis with segregation of companies in the sample by sectors. Extrapolating simple multiples evaluation standards from analysts of the main financial institutions in Brazil, we find that adjusting the ratio formulation to allow for an intercept does not provide satisfactory results in terms of pricing errors reduction. Results found, in spite of evidencing certain relative and absolute superiority among the multiples, may not be generically representative, given samples limitation.

  15. Identification of Civil Engineering Structures using Vector ARMA Models

    Andersen, P.

    The dissertation treats the matter of systems identification and modelling of load-bearing constructions using Auto-Regressive Moving Average Vector (ARMAV) models.......The dissertation treats the matter of systems identification and modelling of load-bearing constructions using Auto-Regressive Moving Average Vector (ARMAV) models....

  16. Practical Modeling and Comprehensive System Identification of a BLDC Motor

    Changle Xiang

    2015-01-01

    Full Text Available The aim of this paper is to outline all the steps in a rigorous and simple procedure for system identification of BLDC motor. A practical mathematical model for identification is derived. Frequency domain identification techniques and time domain estimation method are combined to obtain the unknown parameters. The methods in time domain are founded on the least squares approximation method and a disturbance observer. Only the availability of experimental data for rotor speed and armature current are required for identification. The proposed identification method is systematically investigated, and the final identified model is validated by experimental results performed on a typical BLDC motor in UAV.

  17. Multiple model adaptive control with mixing

    Kuipers, Matthew

    Despite the remarkable theoretical accomplishments and successful applications of adaptive control, the field is not sufficiently mature to solve challenging control problems requiring strict performance and safety guarantees. Towards addressing these issues, a novel deterministic multiple-model adaptive control approach called adaptive mixing control is proposed. In this approach, adaptation comes from a high-level system called the supervisor that mixes into feedback a number of candidate controllers, each finely-tuned to a subset of the parameter space. The mixing signal, the supervisor's output, is generated by estimating the unknown parameters and, at every instant of time, calculating the contribution level of each candidate controller based on certainty equivalence. The proposed architecture provides two characteristics relevant to solving stringent, performance-driven applications. First, the full-suite of linear time invariant control tools is available. A disadvantage of conventional adaptive control is its restriction to utilizing only those control laws whose solutions can be feasibly computed in real-time, such as model reference and pole-placement type controllers. Because its candidate controllers are computed off line, the proposed approach suffers no such restriction. Second, the supervisor's output is smooth and does not necessarily depend on explicit a priori knowledge of the disturbance model. These characteristics can lead to improved performance by avoiding the unnecessary switching and chattering behaviors associated with some other multiple adaptive control approaches. The stability and robustness properties of the adaptive scheme are analyzed. It is shown that the mean-square regulation error is of the order of the modeling error. And when the parameter estimate converges to its true value, which is guaranteed if a persistence of excitation condition is satisfied, the adaptive closed-loop system converges exponentially fast to a closed

  18. Iterative integral parameter identification of a respiratory mechanics model.

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

    2012-07-18

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

  19. Iterative integral parameter identification of a respiratory mechanics model

    Schranz Christoph

    2012-07-01

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

  20. Inverse mathematical modelling and identification in metal powder compaction process

    Gakwaya, A.; Hrairi, M.; Guillot, M.

    2000-01-01

    An online assessment of the quality of advanced integrated computer aided manufacturing systems require the knowledge of accurate and reliable non-linear constitutive material behavior. This paper is concerned with material parameter identification based on experimental data for which non uniform distribution of stresses and deformation within the volume of the specimen is considered. Both geometric and material non linearities as well interfacial frictional contact are taken into account during the simulation. Within the framework of finite deformation theory, a multisurface multiplicative plasticity model for metal powder compaction process is presented. The model is seen to involve several parameters which are not always activated by a single state variable even though it may be technologically important in assessing the final product quality and manufacturing performance. The resulting expressions are presented in spatial setting and gradient based descent method utilizing the modified Levenberg-Marquardt scheme is used for the minimization of least square functional so as to obtain the best agreement between relevant experimental data and simulated data in a specified energy norm. The identification of a subset of material parameters of the cap model for stainless steel powder compaction is performed. The obtained parameters are validated through a simulation of an industrial part manufacturing case. A very good agreement between simulated final density and measured density is obtained thus demonstrating the practical usefulness of the proposed approach. (author)

  1. Parallelized Genetic Identification of the Thermal-Electrochemical Model for Lithium-Ion Battery

    Liqiang Zhang

    2013-01-01

    Full Text Available The parameters of a well predicted model can be used as health characteristics for Lithium-ion battery. This article reports a parallelized parameter identification of the thermal-electrochemical model, which significantly reduces the time consumption of parameter identification. Since the P2D model has the most predictability, it is chosen for further research and expanded to the thermal-electrochemical model by coupling thermal effect and temperature-dependent parameters. Then Genetic Algorithm is used for parameter identification, but it takes too much time because of the long time simulation of model. For this reason, a computer cluster is built by surplus computing resource in our laboratory based on Parallel Computing Toolbox and Distributed Computing Server in MATLAB. The performance of two parallelized methods, namely Single Program Multiple Data (SPMD and parallel FOR loop (PARFOR, is investigated and then the parallelized GA identification is proposed. With this method, model simulations running parallelly and the parameter identification could be speeded up more than a dozen times, and the identification result is batter than that from serial GA. This conclusion is validated by model parameter identification of a real LiFePO4 battery.

  2. Model Updating Nonlinear System Identification Toolbox, Phase II

    National Aeronautics and Space Administration — ZONA Technology (ZONA) proposes to develop an enhanced model updating nonlinear system identification (MUNSID) methodology that utilizes flight data with...

  3. Model Pembelajaran Berbasis Penstimulasian Multiple Intelligences Siswa

    Edy Legowo

    2017-01-01

    Tulisan ini membahas mengenai penerapan teori multiple intelligences dalam pembelajaran di sekolah. Pembahasan diawali dengan menguraikan perkembangan konsep inteligensi dan multiple intelligences. Diikuti dengan menjelaskan dampak teori multiple intelligences dalam bidang pendidikan dan pembelajaran di sekolah. Bagian selanjutnya menguraikan tentang implementasi teori multiple intelligences dalam praktik pembelajaran di kelas yaitu bagaimana pemberian pengalaman belajar siswa yang difasilita...

  4. Search-based model identification of smart-structure damage

    Glass, B. J.; Macalou, A.

    1991-01-01

    This paper describes the use of a combined model and parameter identification approach, based on modal analysis and artificial intelligence (AI) techniques, for identifying damage or flaws in a rotating truss structure incorporating embedded piezoceramic sensors. This smart structure example is representative of a class of structures commonly found in aerospace systems and next generation space structures. Artificial intelligence techniques of classification, heuristic search, and an object-oriented knowledge base are used in an AI-based model identification approach. A finite model space is classified into a search tree, over which a variant of best-first search is used to identify the model whose stored response most closely matches that of the input. Newly-encountered models can be incorporated into the model space. This adaptativeness demonstrates the potential for learning control. Following this output-error model identification, numerical parameter identification is used to further refine the identified model. Given the rotating truss example in this paper, noisy data corresponding to various damage configurations are input to both this approach and a conventional parameter identification method. The combination of the AI-based model identification with parameter identification is shown to lead to smaller parameter corrections than required by the use of parameter identification alone.

  5. On the identification of fractionally cointegrated VAR models with the F(d) condition

    Carlini, Federico; Santucci de Magistris, Paolo

    with different fractional integration and cointegration parameters. The properties of these multiple non-identified sub-models are studied and a necessary and sufficient condition for the identification of the fractional parameters of the system is provided. The condition is named F(d). The assessment of the F(d...

  6. MetaboSearch: tool for mass-based metabolite identification using multiple databases.

    Bin Zhou

    Full Text Available Searching metabolites against databases according to their masses is often the first step in metabolite identification for a mass spectrometry-based untargeted metabolomics study. Major metabolite databases include Human Metabolome DataBase (HMDB, Madison Metabolomics Consortium Database (MMCD, Metlin, and LIPID MAPS. Since each one of these databases covers only a fraction of the metabolome, integration of the search results from these databases is expected to yield a more comprehensive coverage. However, the manual combination of multiple search results is generally difficult when identification of hundreds of metabolites is desired. We have implemented a web-based software tool that enables simultaneous mass-based search against the four major databases, and the integration of the results. In addition, more complete chemical identifier information for the metabolites is retrieved by cross-referencing multiple databases. The search results are merged based on IUPAC International Chemical Identifier (InChI keys. Besides a simple list of m/z values, the software can accept the ion annotation information as input for enhanced metabolite identification. The performance of the software is demonstrated on mass spectrometry data acquired in both positive and negative ionization modes. Compared with search results from individual databases, MetaboSearch provides better coverage of the metabolome and more complete chemical identifier information.The software tool is available at http://omics.georgetown.edu/MetaboSearch.html.

  7. Model Pembelajaran Berbasis Penstimulasian Multiple Intelligences Siswa

    Edy Legowo

    2017-03-01

    Full Text Available Tulisan ini membahas mengenai penerapan teori multiple intelligences dalam pembelajaran di sekolah. Pembahasan diawali dengan menguraikan perkembangan konsep inteligensi dan multiple intelligences. Diikuti dengan menjelaskan dampak teori multiple intelligences dalam bidang pendidikan dan pembelajaran di sekolah. Bagian selanjutnya menguraikan tentang implementasi teori multiple intelligences dalam praktik pembelajaran di kelas yaitu bagaimana pemberian pengalaman belajar siswa yang difasilitasi guru dapat menstimulasi multiple intelligences siswa. Evaluasi hasil belajar siswa dari pandangan penerapan teori multiple intelligences seharusnya dilakukan menggunakan authentic assessment dan portofolio yang lebih memfasilitasi para siswa mengungkapkan atau mengaktualisasikan hasil belajarnya melalui berbagai cara sesuai dengan kekuatan jenis inteligensinya.

  8. Multiple Temperature Model for Near Continuum Flows

    XU, Kun; Liu, Hongwei; Jiang, Jianzheng

    2007-01-01

    In the near continuum flow regime, the flow may have different translational temperatures in different directions. It is well known that for increasingly rarefied flow fields, the predictions from continuum formulation, such as the Navier-Stokes equations, lose accuracy. These inaccuracies may be partially due to the single temperature assumption in the Navier-Stokes equations. Here, based on the gas-kinetic Bhatnagar-Gross-Krook (BGK) equation, a multitranslational temperature model is proposed and used in the flow calculations. In order to fix all three translational temperatures, two constraints are additionally proposed to model the energy exchange in different directions. Based on the multiple temperature assumption, the Navier-Stokes relation between the stress and strain is replaced by the temperature relaxation term, and the Navier-Stokes assumption is recovered only in the limiting case when the flow is close to the equilibrium with the same temperature in different directions. In order to validate the current model, both the Couette and Poiseuille flows are studied in the transition flow regime

  9. Vibratory gyroscopes : identification of mathematical model from test data

    Shatalov, MY

    2007-05-01

    Full Text Available Simple mathematical model of vibratory gyroscopes imperfections is formulated, which includes anisotropic damping and variation of mass-stiffness parameters and their harmonics. The method of identification of parameters of the mathematical model...

  10. A hybrid approach to parameter identification of linear delay differential equations involving multiple delays

    Marzban, Hamid Reza

    2018-05-01

    In this paper, we are concerned with the parameter identification of linear time-invariant systems containing multiple delays. The approach is based upon a hybrid of block-pulse functions and Legendre's polynomials. The convergence of the proposed procedure is established and an upper error bound with respect to the L2-norm associated with the hybrid functions is derived. The problem under consideration is first transformed into a system of algebraic equations. The least squares technique is then employed for identification of the desired parameters. Several multi-delay systems of varying complexity are investigated to evaluate the performance and capability of the proposed approximation method. It is shown that the proposed approach is also applicable to a class of nonlinear multi-delay systems. It is demonstrated that the suggested procedure provides accurate results for the desired parameters.

  11. A physiologically based nonhomogeneous Poisson counter model of visual identification

    Christensen, Jeppe H; Markussen, Bo; Bundesen, Claus

    2018-01-01

    A physiologically based nonhomogeneous Poisson counter model of visual identification is presented. The model was developed in the framework of a Theory of Visual Attention (Bundesen, 1990; Kyllingsbæk, Markussen, & Bundesen, 2012) and meant for modeling visual identification of objects that are ......A physiologically based nonhomogeneous Poisson counter model of visual identification is presented. The model was developed in the framework of a Theory of Visual Attention (Bundesen, 1990; Kyllingsbæk, Markussen, & Bundesen, 2012) and meant for modeling visual identification of objects...... that mimicked the dynamics of receptive field selectivity as found in neurophysiological studies. Furthermore, the initial sensory response yielded theoretical hazard rate functions that closely resembled empirically estimated ones. Finally, supplied with a Naka-Rushton type contrast gain control, the model...

  12. On the identification of fractionally cointegrated VAR models with the F(d) condition

    Santucci de Magistris, Paolo; Carlini, Federico

    for any choice of the lag-length when the true cointegration rank is known. The properties of these multiple non-identified models are studied and a necessary and sufficient condition for the identification of the fractional parameters of the system is provided. The condition is named F(d......). This is a generalization of the well-known I(1) condition to the fractional case. Imposing a proper restriction on the fractional integration parameter, d, is sufficient to guarantee identification of all model parameters and the validity of the F(d) condition. The paper also illustrates the indeterminacy between...

  13. Identification of GMS friction model without friction force measurement

    Grami, Said; Aissaoui, Hicham

    2011-01-01

    This paper deals with an online identification of the Generalized Maxwell Slip (GMS) friction model for both presliding and sliding regime at the same time. This identification is based on robust adaptive observer without friction force measurement. To apply the observer, a new approach of calculating the filtered friction force from the measurable signals is introduced. Moreover, two approximations are proposed to get the friction model linear over the unknown parameters and an approach of suitable filtering is introduced to guarantee the continuity of the model. Simulation results are presented to prove the efficiency of the approach of identification.

  14. Multiple Scattering Model for Optical Coherence Tomography with Rytov Approximation

    Li, Muxingzi

    2017-01-01

    of speckles due to multiple scatterers within the coherence length, and other random noise. Motivated by the above two challenges, a multiple scattering model based on Rytov approximation and Gaussian beam optics is proposed for the OCT setup. Some previous

  15. Pollutant source identification model for water pollution incidents in small straight rivers based on genetic algorithm

    Zhang, Shou-ping; Xin, Xiao-kang

    2017-07-01

    Identification of pollutant sources for river pollution incidents is an important and difficult task in the emergency rescue, and an intelligent optimization method can effectively compensate for the weakness of traditional methods. An intelligent model for pollutant source identification has been established using the basic genetic algorithm (BGA) as an optimization search tool and applying an analytic solution formula of one-dimensional unsteady water quality equation to construct the objective function. Experimental tests show that the identification model is effective and efficient: the model can accurately figure out the pollutant amounts or positions no matter single pollution source or multiple sources. Especially when the population size of BGA is set as 10, the computing results are sound agree with analytic results for a single source amount and position identification, the relative errors are no more than 5 %. For cases of multi-point sources and multi-variable, there are some errors in computing results for the reasons that there exist many possible combinations of the pollution sources. But, with the help of previous experience to narrow the search scope, the relative errors of the identification results are less than 5 %, which proves the established source identification model can be used to direct emergency responses.

  16. Modeling of Biometric Identification System Using the Colored Petri Nets

    Petrosyan, G. R.; Ter-Vardanyan, L. A.; Gaboutchian, A. V.

    2015-05-01

    In this paper we present a model of biometric identification system transformed into Petri Nets. Petri Nets, as a graphical and mathematical tool, provide a uniform environment for modelling, formal analysis, and design of discrete event systems. The main objective of this paper is to introduce the fundamental concepts of Petri Nets to the researchers and practitioners, both from identification systems, who are involved in the work in the areas of modelling and analysis of biometric identification types of systems, as well as those who may potentially be involved in these areas. In addition, the paper introduces high-level Petri Nets, as Colored Petri Nets (CPN). In this paper the model of Colored Petri Net describes the identification process much simpler.

  17. Model Updating Nonlinear System Identification Toolbox, Phase I

    National Aeronautics and Space Administration — ZONA Technology proposes to develop an enhanced model updating nonlinear system identification (MUNSID) methodology by adopting the flight data with state-of-the-art...

  18. Systematic identification of crystallization kinetics within a generic modelling framework

    Abdul Samad, Noor Asma Fazli Bin; Meisler, Kresten Troelstrup; Gernaey, Krist

    2012-01-01

    A systematic development of constitutive models within a generic modelling framework has been developed for use in design, analysis and simulation of crystallization operations. The framework contains a tool for model identification connected with a generic crystallizer modelling tool-box, a tool...

  19. Testing for Nonuniform Differential Item Functioning with Multiple Indicator Multiple Cause Models

    Woods, Carol M.; Grimm, Kevin J.

    2011-01-01

    In extant literature, multiple indicator multiple cause (MIMIC) models have been presented for identifying items that display uniform differential item functioning (DIF) only, not nonuniform DIF. This article addresses, for apparently the first time, the use of MIMIC models for testing both uniform and nonuniform DIF with categorical indicators. A…

  20. Multiplicity distributions in the dual parton model

    Batunin, A.V.; Tolstenkov, A.N.

    1985-01-01

    Multiplicity distributions are calculated by means of a new mechanism of production of hadrons in a string, which was proposed previously by the authors and takes into account explicitly the valence character of the ends of the string. It is shown that allowance for this greatly improves the description of the low-energy multiplicity distributions. At superhigh energies, the contribution of the ends of the strings becomes negligibly small, but in this case multi-Pomeron contributions must be taken into account

  1. Modeling and Analysis of Surgery Patient Identification Using RFID

    Byungho Jeong; Chen-Yang Cheng; Vittal Prabhu

    2009-01-01

    This article proposes a workflow and reliability model for surgery patient identification using RFID (Radio Frequency Identification). Certain types of mistakes may be prevented by automatically identifying the patient before surgery. The proposed workflow is designed to ensure that both the correct site and patient are engaged in the surgical process. The reliability model can be used to assess improvements in patients’ safety during this process. A proof-of-concept system is developed to ...

  2. Quantitative Seq-LGS: Genome-Wide Identification of Genetic Drivers of Multiple Phenotypes in Malaria Parasites

    Abkallo, Hussein M.

    2016-10-01

    Identifying the genetic determinants of phenotypes that impact on disease severity is of fundamental importance for the design of new interventions against malaria. Traditionally, such discovery has relied on labor-intensive approaches that require significant investments of time and resources. By combining Linkage Group Selection (LGS), quantitative whole genome population sequencing and a novel mathematical modeling approach (qSeq-LGS), we simultaneously identified multiple genes underlying two distinct phenotypes, identifying novel alleles for growth rate and strain specific immunity (SSI), while removing the need for traditionally required steps such as cloning, individual progeny phenotyping and marker generation. The detection of novel variants, verified by experimental phenotyping methods, demonstrates the remarkable potential of this approach for the identification of genes controlling selectable phenotypes in malaria and other apicomplexan parasites for which experimental genetic crosses are amenable.

  3. Evolving Four Part Harmony Using a Multiple Worlds Model

    Scirea, Marco; Brown, Joseph Alexander

    2015-01-01

    This application of the Multiple Worlds Model examines a collaborative fitness model for generating four part harmonies. In this model we have multiple populations and the fitness of the individuals is based on the ability of a member from each population to work with the members of other...

  4. Explaining clinical behaviors using multiple theoretical models

    Eccles Martin P

    2012-10-01

    the five surveys. For the predictor variables, the mean construct scores were above the mid-point on the scale with median values across the five behaviors generally being above four out of seven and the range being from 1.53 to 6.01. Across all of the theories, the highest proportion of the variance explained was always for intention and the lowest was for behavior. The Knowledge-Attitudes-Behavior Model performed poorly across all behaviors and dependent variables; CSSRM also performed poorly. For TPB, SCT, II, and LT across the five behaviors, we predicted median R2 of 25% to 42.6% for intention, 6.2% to 16% for behavioral simulation, and 2.4% to 6.3% for behavior. Conclusions We operationalized multiple theories measuring across five behaviors. Continuing challenges that emerge from our work are: better specification of behaviors, better operationalization of theories; how best to appropriately extend the range of theories; further assessment of the value of theories in different settings and groups; exploring the implications of these methods for the management of chronic diseases; and moving to experimental designs to allow an understanding of behavior change.

  5. Explaining clinical behaviors using multiple theoretical models.

    Eccles, Martin P; Grimshaw, Jeremy M; MacLennan, Graeme; Bonetti, Debbie; Glidewell, Liz; Pitts, Nigel B; Steen, Nick; Thomas, Ruth; Walker, Anne; Johnston, Marie

    2012-10-17

    , the mean construct scores were above the mid-point on the scale with median values across the five behaviors generally being above four out of seven and the range being from 1.53 to 6.01. Across all of the theories, the highest proportion of the variance explained was always for intention and the lowest was for behavior. The Knowledge-Attitudes-Behavior Model performed poorly across all behaviors and dependent variables; CSSRM also performed poorly. For TPB, SCT, II, and LT across the five behaviors, we predicted median R2 of 25% to 42.6% for intention, 6.2% to 16% for behavioral simulation, and 2.4% to 6.3% for behavior. We operationalized multiple theories measuring across five behaviors. Continuing challenges that emerge from our work are: better specification of behaviors, better operationalization of theories; how best to appropriately extend the range of theories; further assessment of the value of theories in different settings and groups; exploring the implications of these methods for the management of chronic diseases; and moving to experimental designs to allow an understanding of behavior change.

  6. Modeling emotional content of music using system identification.

    Korhonen, Mark D; Clausi, David A; Jernigan, M Ed

    2006-06-01

    Research was conducted to develop a methodology to model the emotional content of music as a function of time and musical features. Emotion is quantified using the dimensions valence and arousal, and system-identification techniques are used to create the models. Results demonstrate that system identification provides a means to generalize the emotional content for a genre of music. The average R2 statistic of a valid linear model structure is 21.9% for valence and 78.4% for arousal. The proposed method of constructing models of emotional content generalizes previous time-series models and removes ambiguity from classifiers of emotion.

  7. System Identification of a Heaving Point Absorber: Design of Experiment and Device Modeling

    Giorgio Bacelli

    2017-04-01

    Full Text Available Empirically based modeling is an essential aspect of design for a wave energy converter. Empirically based models are used in structural, mechanical and control design processes, as well as for performance prediction. Both the design of experiments and methods used in system identification have a strong impact on the quality of the resulting model. This study considers the system identification and model validation process based on data collected from a wave tank test of a model-scale wave energy converter. Experimental design and data processing techniques based on general system identification procedures are discussed and compared with the practices often followed for wave tank testing. The general system identification processes are shown to have a number of advantages, including an increased signal-to-noise ratio, reduced experimental time and higher frequency resolution. The experimental wave tank data is used to produce multiple models using different formulations to represent the dynamics of the wave energy converter. These models are validated and their performance is compared against one another. While most models of wave energy converters use a formulation with surface elevation as an input, this study shows that a model using a hull pressure measurement to incorporate the wave excitation phenomenon has better accuracy.

  8. Stochastic Models in the Identification Process

    Slovák, Dalibor; Zvárová, Jana

    2011-01-01

    Roč. 7, č. 1 (2011), s. 44-50 ISSN 1801-5603 R&D Projects: GA MŠk(CZ) 1M06014 Institutional research plan: CEZ:AV0Z10300504 Keywords : identification process * weight-of evidence formula * coancestry coefficient * beta-binomial sampling formula * DNA mixtures Subject RIV: IN - Informatics, Computer Science http://www.ejbi.eu/images/2011-1/Slovak_en.pdf

  9. A basket two-part model to analyze medical expenditure on interdependent multiple sectors.

    Sugawara, Shinya; Wu, Tianyi; Yamanishi, Kenji

    2018-05-01

    This study proposes a novel statistical methodology to analyze expenditure on multiple medical sectors using consumer data. Conventionally, medical expenditure has been analyzed by two-part models, which separately consider purchase decision and amount of expenditure. We extend the traditional two-part models by adding the step of basket analysis for dimension reduction. This new step enables us to analyze complicated interdependence between multiple sectors without an identification problem. As an empirical application for the proposed method, we analyze data of 13 medical sectors from the Medical Expenditure Panel Survey. In comparison with the results of previous studies that analyzed the multiple sector independently, our method provides more detailed implications of the impacts of individual socioeconomic status on the composition of joint purchases from multiple medical sectors; our method has a better prediction performance.

  10. A Fuzzy Logic Framework for Integrating Multiple Learned Models

    Hartog, Bobi Kai Den [Univ. of Nebraska, Lincoln, NE (United States)

    1999-03-01

    The Artificial Intelligence field of Integrating Multiple Learned Models (IMLM) explores ways to combine results from sets of trained programs. Aroclor Interpretation is an ill-conditioned problem in which trained programs must operate in scenarios outside their training ranges because it is intractable to train them completely. Consequently, they fail in ways related to the scenarios. We developed a general-purpose IMLM solution, the Combiner, and applied it to Aroclor Interpretation. The Combiner's first step, Scenario Identification (M), learns rules from very sparse, synthetic training data consisting of results from a suite of trained programs called Methods. S1 produces fuzzy belief weights for each scenario by approximately matching the rules. The Combiner's second step, Aroclor Presence Detection (AP), classifies each of three Aroclors as present or absent in a sample. The third step, Aroclor Quantification (AQ), produces quantitative values for the concentration of each Aroclor in a sample. AP and AQ use automatically learned empirical biases for each of the Methods in each scenario. Through fuzzy logic, AP and AQ combine scenario weights, automatically learned biases for each of the Methods in each scenario, and Methods' results to determine results for a sample.

  11. Integration of multiple, excess, backup, and expected covering models

    M S Daskin; K Hogan; C ReVelle

    1988-01-01

    The concepts of multiple, excess, backup, and expected coverage are defined. Model formulations using these constructs are reviewed and contrasted to illustrate the relationships between them. Several new formulations are presented as is a new derivation of the expected covering model which indicates more clearly the relationship of the model to other multi-state covering models. An expected covering model with multiple time standards is also presented.

  12. Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach

    Xiao-meng Song

    2013-01-01

    Full Text Available Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on (1 a screening method (Morris for qualitative ranking of parameters, and (2 a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model (RSMSobol. First, the Morris screening method was used to qualitatively identify the parameters' sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model (RSM were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.

  13. Nonlinear adaptive synchronization rule for identification of a large amount of parameters in dynamical models

    Ma Huanfei; Lin Wei

    2009-01-01

    The existing adaptive synchronization technique based on the stability theory and invariance principle of dynamical systems, though theoretically proved to be valid for parameters identification in specific models, is always showing slow convergence rate and even failed in practice when the number of parameters becomes large. Here, for parameters update, a novel nonlinear adaptive rule is proposed to accelerate the rate. Its feasibility is validated by analytical arguments as well as by specific parameters identification in the Lotka-Volterra model with multiple species. Two adjustable factors in this rule influence the identification accuracy, which means that a proper choice of these factors leads to an optimal performance of this rule. In addition, a feasible method for avoiding the occurrence of the approximate linear dependence among terms with parameters on the synchronized manifold is also proposed.

  14. CEAI: CCM-based email authorship identification model

    Sarwat Nizamani

    2013-11-01

    Full Text Available In this paper we present a model for email authorship identification (EAI by employing a Cluster-based Classification (CCM technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature set to include some more interesting and effective features for email authorship identification (e.g., the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell. We also included Info Gain feature selection based content features. It is observed that the use of such features in the authorship identification process has a positive impact on the accuracy of the authorship identification task. We performed experiments to justify our arguments and compared the results with other base line models. Experimental results reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM-based models, as well as the models proposed by Iqbal et al. (2010, 2013 [1,2]. The proposed model attains an accuracy rate of 94% for 10 authors, 89% for 25 authors, and 81% for 50 authors, respectively on Enron dataset, while 89.5% accuracy has been achieved on authors’ constructed real email dataset. The results on Enron dataset have been achieved on quite a large number of authors as compared to the models proposed by Iqbal et al. [1,2].

  15. Identification of a nuclear plant dynamics via ARMAX model

    Yamamoto, Shigeki; Otsuji, Tomoo; Muramatsu, Eiichi

    2000-01-01

    Dynamics of the reactor of nuclear ship 'Mutsu' is described by a linear time-invariant discrete-time model which is referred to as ARMAX (Auto-Regressive Moving Average eXogenious inputs) model. Applying system identification methods, parameters of the ARMAX model are determined from input-output data of the reactor. Accuracy of the model is examined in time and frequency domain. We show that the model can be a good approximation of the plant dynamics. (author)

  16. Application of Metamodels to Identification of Metallic Materials Models

    Pietrzyk, Maciej; Kusiak, Jan; Szeliga, Danuta; Rauch, Łukasz; Sztangret, Łukasz; Górecki, Grzegorz

    2016-01-01

    Improvement of the efficiency of the inverse analysis (IA) for various material tests was the objective of the paper. Flow stress models and microstructure evolution models of various complexity of mathematical formulation were considered. Different types of experiments were performed and the results were used for the identification of models. Sensitivity analysis was performed for all the models and the importance of parameters in these models was evaluated. Metamodels based on artificial ne...

  17. ‘Healthy’ identities? : Revisiting rejection-identification and rejection-disidentification models among voluntary and forced immigrants

    Bobowik, Magdalena; Martinovic, Borja; Basabe, Nekane; Barsties, Lisa S.; Wachter, Gusta

    2017-01-01

    Rejection-identification and rejection-disidentification models propose that low-status groups identify with their in-group and disidentify with a high-status out-group in response to rejection by the latter. Our research tests these two models simultaneously among multiple groups of foreign-born

  18. A test for the parameters of multiple linear regression models ...

    A test for the parameters of multiple linear regression models is developed for conducting tests simultaneously on all the parameters of multiple linear regression models. The test is robust relative to the assumptions of homogeneity of variances and absence of serial correlation of the classical F-test. Under certain null and ...

  19. Identification of multiple distinct Snf2 subfamilies with conserved structural motifs.

    Flaus, Andrew; Martin, David M A; Barton, Geoffrey J; Owen-Hughes, Tom

    2006-01-01

    The Snf2 family of helicase-related proteins includes the catalytic subunits of ATP-dependent chromatin remodelling complexes found in all eukaryotes. These act to regulate the structure and dynamic properties of chromatin and so influence a broad range of nuclear processes. We have exploited progress in genome sequencing to assemble a comprehensive catalogue of over 1300 Snf2 family members. Multiple sequence alignment of the helicase-related regions enables 24 distinct subfamilies to be identified, a considerable expansion over earlier surveys. Where information is known, there is a good correlation between biological or biochemical function and these assignments, suggesting Snf2 family motor domains are tuned for specific tasks. Scanning of complete genomes reveals all eukaryotes contain members of multiple subfamilies, whereas they are less common and not ubiquitous in eubacteria or archaea. The large sample of Snf2 proteins enables additional distinguishing conserved sequence blocks within the helicase-like motor to be identified. The establishment of a phylogeny for Snf2 proteins provides an opportunity to make informed assignments of function, and the identification of conserved motifs provides a framework for understanding the mechanisms by which these proteins function.

  20. Overhead longwave infrared hyperspectral material identification using radiometric models

    Zelinski, M. E. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2018-01-09

    Material detection algorithms used in hyperspectral data processing are computationally efficient but can produce relatively high numbers of false positives. Material identification performed as a secondary processing step on detected pixels can help separate true and false positives. This paper presents a material identification processing chain for longwave infrared hyperspectral data of solid materials collected from airborne platforms. The algorithms utilize unwhitened radiance data and an iterative algorithm that determines the temperature, humidity, and ozone of the atmospheric profile. Pixel unmixing is done using constrained linear regression and Bayesian Information Criteria for model selection. The resulting product includes an optimal atmospheric profile and full radiance material model that includes material temperature, abundance values, and several fit statistics. A logistic regression method utilizing all model parameters to improve identification is also presented. This paper details the processing chain and provides justification for the algorithms used. Several examples are provided using modeled data at different noise levels.

  1. Medicare capitation model, functional status, and multiple comorbidities: model accuracy

    Noyes, Katia; Liu, Hangsheng; Temkin-Greener, Helena

    2012-01-01

    Objective This study examined financial implications of CMS-Hierarchical Condition Categories (HCC) risk-adjustment model on Medicare payments for individuals with comorbid chronic conditions. Study Design The study used 1992-2000 data from the Medicare Current Beneficiary Survey and corresponding Medicare claims. The pairs of comorbidities were formed based on the prior evidence about possible synergy between these conditions and activities of daily living (ADL) deficiencies and included heart disease and cancer, lung disease and cancer, stroke and hypertension, stroke and arthritis, congestive heart failure (CHF) and osteoporosis, diabetes and coronary artery disease, CHF and dementia. Methods For each beneficiary, we calculated the actual Medicare cost ratio as the ratio of the individual’s annualized costs to the mean annual Medicare cost of all people in the study. The actual Medicare cost ratios, by ADLs, were compared to the HCC ratios under the CMS-HCC payment model. Using multivariate regression models, we tested whether having the identified pairs of comorbidities affects the accuracy of CMS-HCC model predictions. Results The CMS-HCC model underpredicted Medicare capitation payments for patients with hypertension, lung disease, congestive heart failure and dementia. The difference between the actual costs and predicted payments was partially explained by beneficiary functional status and less than optimal adjustment for these chronic conditions. Conclusions Information about beneficiary functional status should be incorporated in reimbursement models since underpaying providers for caring for population with multiple comorbidities may provide severe disincentives for managed care plans to enroll such individuals and to appropriately manage their complex and costly conditions. PMID:18837646

  2. Parameter identification in multinomial processing tree models

    Schmittmann, V.D.; Dolan, C.V.; Raijmakers, M.E.J.; Batchelder, W.H.

    2010-01-01

    Multinomial processing tree models form a popular class of statistical models for categorical data that have applications in various areas of psychological research. As in all statistical models, establishing which parameters are identified is necessary for model inference and selection on the basis

  3. Multiple Scenario Generation of Subsurface Models

    Cordua, Knud Skou

    of information is obeyed such that no unknown assumptions and biases influence the solution to the inverse problem. This involves a definition of the probabilistically formulated inverse problem, a discussion about how prior models can be established based on statistical information from sample models...... of the probabilistic formulation of the inverse problem. This function is based on an uncertainty model that describes the uncertainties related to the observed data. In a similar way, a formulation of the prior probability distribution that takes into account uncertainties related to the sample model statistics...... similar to observation uncertainties. We refer to the effect of these approximations as modeling errors. Examples that show how the modeling error is estimated are provided. Moreover, it is shown how these effects can be taken into account in the formulation of the posterior probability distribution...

  4. Vortex Tube Modeling Using the System Identification Method

    Han, Jaeyoung; Jeong, Jiwoong; Yu, Sangseok [Chungnam Nat’l Univ., Daejeon (Korea, Republic of); Im, Seokyeon [Tongmyong Univ., Busan (Korea, Republic of)

    2017-05-15

    In this study, vortex tube system model is developed to predict the temperature of the hot and the cold sides. The vortex tube model is developed based on the system identification method, and the model utilized in this work to design the vortex tube is ARX type (Auto-Regressive with eXtra inputs). The derived polynomial model is validated against experimental data to verify the overall model accuracy. It is also shown that the derived model passes the stability test. It is confirmed that the derived model closely mimics the physical behavior of the vortex tube from both the static and dynamic numerical experiments by changing the angles of the low-temperature side throttle valve, clearly showing temperature separation. These results imply that the system identification based modeling can be a promising approach for the prediction of complex physical systems, including the vortex tube.

  5. Multiple system modelling of waste management

    Eriksson, Ola; Bisaillon, Mattias

    2011-01-01

    Highlights: → Linking of models will provide a more complete, correct and credible picture of the systems. → The linking procedure is easy to perform and also leads to activation of project partners. → The simulation procedure is a bit more complicated and calls for the ability to run both models. - Abstract: Due to increased environmental awareness, planning and performance of waste management has become more and more complex. Therefore waste management has early been subject to different types of modelling. Another field with long experience of modelling and systems perspective is energy systems. The two modelling traditions have developed side by side, but so far there are very few attempts to combine them. Waste management systems can be linked together with energy systems through incineration plants. The models for waste management can be modelled on a quite detailed level whereas surrounding systems are modelled in a more simplistic way. This is a problem, as previous studies have shown that assumptions on the surrounding system often tend to be important for the conclusions. In this paper it is shown how two models, one for the district heating system (MARTES) and another one for the waste management system (ORWARE), can be linked together. The strengths and weaknesses with model linking are discussed when compared to simplistic assumptions on effects in the energy and waste management systems. It is concluded that the linking of models will provide a more complete, correct and credible picture of the consequences of different simultaneous changes in the systems. The linking procedure is easy to perform and also leads to activation of project partners. However, the simulation procedure is a bit more complicated and calls for the ability to run both models.

  6. Modeling and identification of centrifugal compressor dynamics with approximate realizations

    Helvoirt, van J.; Jager, de A.G.; Steinbuch, M.; Smeulers, J.P.M.

    2005-01-01

    This paper deals with the parameter identification of a model for the dynamic behavior of a large industrial centrifugal compression system. Experimental results are presented to evaluate a new approach for determining the parameters of the modified version of the well-known Greitzer model. This

  7. Mean multiplicity in the Regge models with rising cross sections

    Chikovani, Z.E.; Kobylisky, N.A.; Martynov, E.S.

    1979-01-01

    Behaviour of the mean multiplicity and the total cross section σsub(t) of hadron-hadron interactions is considered in the framework of the Regge models at high energies. Generating function was plotted for models of dipole and froissaron, and the mean multiplicity and multiplicity moments were calculated. It is shown that approximately ln 2 S (energy square) in the dipole model, which is in good agreement with the experiment. It is also found that in various Regge models approximately σsub(t)lnS

  8. Discrete choice models with multiplicative error terms

    Fosgerau, Mogens; Bierlaire, Michel

    2009-01-01

    The conditional indirect utility of many random utility maximization (RUM) discrete choice models is specified as a sum of an index V depending on observables and an independent random term ε. In general, the universe of RUM consistent models is much larger, even fixing some specification of V due...

  9. Multiple-lesion track-structure model

    Wilson, J.W.; Cucinotta, F.A.; Shinn, J.L.

    1992-03-01

    A multilesion cell kinetic model is derived, and radiation kinetic coefficients are related to the Katz track structure model. The repair-related coefficients are determined from the delayed plating experiments of Yang et al. for the C3H10T1/2 cell system. The model agrees well with the x ray and heavy ion experiments of Yang et al. for the immediate plating, delaying plating, and fractionated exposure protocols employed by Yang. A study is made of the effects of target fragments in energetic proton exposures and of the repair-deficient target-fragment-induced lesions

  10. Identification of alternative splice variants in Aspergillus flavus through comparison of multiple tandem MS search algorithms

    Chang Kung-Yen

    2011-07-01

    Full Text Available Abstract Background Database searching is the most frequently used approach for automated peptide assignment and protein inference of tandem mass spectra. The results, however, depend on the sequences in target databases and on search algorithms. Recently by using an alternative splicing database, we identified more proteins than with the annotated proteins in Aspergillus flavus. In this study, we aimed at finding a greater number of eligible splice variants based on newly available transcript sequences and the latest genome annotation. The improved database was then used to compare four search algorithms: Mascot, OMSSA, X! Tandem, and InsPecT. Results The updated alternative splicing database predicted 15833 putative protein variants, 61% more than the previous results. There was transcript evidence for 50% of the updated genes compared to the previous 35% coverage. Database searches were conducted using the same set of spectral data, search parameters, and protein database but with different algorithms. The false discovery rates of the peptide-spectrum matches were estimated Conclusions We were able to detect dozens of new peptides using the improved alternative splicing database with the recently updated annotation of the A. flavus genome. Unlike the identifications of the peptides and the RefSeq proteins, large variations existed between the putative splice variants identified by different algorithms. 12 candidates of putative isoforms were reported based on the consensus peptide-spectrum matches. This suggests that applications of multiple search engines effectively reduced the possible false positive results and validated the protein identifications from tandem mass spectra using an alternative splicing database.

  11. Affine LIBOR Models with Multiple Curves

    Grbac, Zorana; Papapantoleon, Antonis; Schoenmakers, John

    2015-01-01

    are specified following the methodology of the affine LIBOR models and are driven by the wide and flexible class of affine processes. The affine property is preserved under forward measures, which allows us to derive Fourier pricing formulas for caps, swaptions, and basis swaptions. A model specification...... with dependent LIBOR rates is developed that allows for an efficient and accurate calibration to a system of caplet prices....

  12. Identification and modelling of Lithium ion battery

    Tsang, K.M.; Sun, L.; Chan, W.L.

    2010-01-01

    A universal battery model for the charging process has been identified for Lithium ion battery working at constant temperature. Mathematical models are fitted to different collected charging profiles using the least squares algorithm. With the removal of the component which is related to the DC resistance of the battery, a universal model can be fitted to predict profiles of different charging rates after time scaling. Experimental results are included to demonstrate the goodness of fit of the model at different charging rates and for batteries of different capacities. Comparison with standard electrical-circuit model is also presented. With the proposed model, it is possible to derive more effective way to monitor the status of Lithium ion batteries, and to develop a universal quick charger for different capacities of batteries to result with a more effective usage of Lithium ion batteries.

  13. Identification and communication of uncertainties of phenomenological models in PSA

    Pulkkinen, U.; Simola, K.

    2001-11-01

    This report aims at presenting a view upon uncertainty analysis of phenomenological models with an emphasis on the identification and documentation of various types of uncertainties and assumptions in the modelling of the phenomena. In an uncertainty analysis, it is essential to include and document all unclear issues, in order to obtain a maximal coverage of unresolved issues. This holds independently on their nature or type of the issues. The classification of uncertainties is needed in the decomposition of the problem and it helps in the identification of means for uncertainty reduction. Further, an enhanced documentation serves to evaluate the applicability of the results to various risk-informed applications. (au)

  14. Identification of cascade water tanks using a PWARX model

    Mattsson, Per; Zachariah, Dave; Stoica, Petre

    2018-06-01

    In this paper we consider the identification of a discrete-time nonlinear dynamical model for a cascade water tank process. The proposed method starts with a nominal linear dynamical model of the system, and proceeds to model its prediction errors using a model that is piecewise affine in the data. As data is observed, the nominal model is refined into a piecewise ARX model which can capture a wide range of nonlinearities, such as the saturation in the cascade tanks. The proposed method uses a likelihood-based methodology which adaptively penalizes model complexity and directly leads to a computationally efficient implementation.

  15. Transfer Function Identification Using Orthogonal Fourier Transform Modeling Functions

    Morelli, Eugene A.

    2013-01-01

    A method for transfer function identification, including both model structure determination and parameter estimation, was developed and demonstrated. The approach uses orthogonal modeling functions generated from frequency domain data obtained by Fourier transformation of time series data. The method was applied to simulation data to identify continuous-time transfer function models and unsteady aerodynamic models. Model fit error, estimated model parameters, and the associated uncertainties were used to show the effectiveness of the method for identifying accurate transfer function models from noisy data.

  16. SDG and qualitative trend based model multiple scale validation

    Gao, Dong; Xu, Xin; Yin, Jianjin; Zhang, Hongyu; Zhang, Beike

    2017-09-01

    Verification, Validation and Accreditation (VV&A) is key technology of simulation and modelling. For the traditional model validation methods, the completeness is weak; it is carried out in one scale; it depends on human experience. The SDG (Signed Directed Graph) and qualitative trend based multiple scale validation is proposed. First the SDG model is built and qualitative trends are added to the model. And then complete testing scenarios are produced by positive inference. The multiple scale validation is carried out by comparing the testing scenarios with outputs of simulation model in different scales. Finally, the effectiveness is proved by carrying out validation for a reactor model.

  17. A physiologically based nonhomogeneous Poisson counter model of visual identification.

    Christensen, Jeppe H; Markussen, Bo; Bundesen, Claus; Kyllingsbæk, Søren

    2018-04-30

    A physiologically based nonhomogeneous Poisson counter model of visual identification is presented. The model was developed in the framework of a Theory of Visual Attention (Bundesen, 1990; Kyllingsbæk, Markussen, & Bundesen, 2012) and meant for modeling visual identification of objects that are mutually confusable and hard to see. The model assumes that the visual system's initial sensory response consists in tentative visual categorizations, which are accumulated by leaky integration of both transient and sustained components comparable with those found in spike density patterns of early sensory neurons. The sensory response (tentative categorizations) feeds independent Poisson counters, each of which accumulates tentative object categorizations of a particular type to guide overt identification performance. We tested the model's ability to predict the effect of stimulus duration on observed distributions of responses in a nonspeeded (pure accuracy) identification task with eight response alternatives. The time courses of correct and erroneous categorizations were well accounted for when the event-rates of competing Poisson counters were allowed to vary independently over time in a way that mimicked the dynamics of receptive field selectivity as found in neurophysiological studies. Furthermore, the initial sensory response yielded theoretical hazard rate functions that closely resembled empirically estimated ones. Finally, supplied with a Naka-Rushton type contrast gain control, the model provided an explanation for Bloch's law. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  18. Experimental Damage Identification of a Model Reticulated Shell

    Jing Xu

    2017-04-01

    Full Text Available The damage identification of a reticulated shell is a challenging task, facing various difficulties, such as the large number of degrees of freedom (DOFs, the phenomenon of modal localization and transition, and low modeling accuracy. Based on structural vibration responses, the damage identification of a reticulated shell was studied. At first, the auto-regressive (AR time series model was established based on the acceleration responses of the reticulated shell. According to the changes in the coefficients of the AR model between the damaged conditions and the undamaged condition, the damage of the reticulated shell can be detected. In addition, the damage sensitive factors were determined based on the coefficients of the AR model. With the damage sensitive factors as the inputs and the damage positions as the outputs, back-propagation neural networks (BPNNs were then established and were trained using the Levenberg–Marquardt algorithm (L–M algorithm. The locations of the damages can be predicted by the back-propagation neural networks. At last, according to the experimental scheme of single-point excitation and multi-point responses, the impact experiments on a K6 shell model with a scale of 1/10 were conducted. The experimental results verified the efficiency of the proposed damage identification method based on the AR time series model and back-propagation neural networks. The proposed damage identification method can ensure the safety of the practical engineering to some extent.

  19. Modelling of rate effects at multiple scales

    Pedersen, R.R.; Simone, A.; Sluys, L. J.

    2008-01-01

    , the length scale in the meso-model and the macro-model can be coupled. In this fashion, a bridging of length scales can be established. A computational analysis of  a Split Hopkinson bar test at medium and high impact load is carried out at macro-scale and meso-scale including information from  the micro-scale.......At the macro- and meso-scales a rate dependent constitutive model is used in which visco-elasticity is coupled to visco-plasticity and damage. A viscous length scale effect is introduced to control the size of the fracture process zone. By comparison of the widths of the fracture process zone...

  20. Global Nonlinear Model Identification with Multivariate Splines

    De Visser, C.C.

    2011-01-01

    At present, model based control systems play an essential role in many aspects of modern society. Application areas of model based control systems range from food processing to medical imaging, and from process control in oil refineries to the flight control systems of modern aircraft. Central to a

  1. The Talent Search Model of Gifted Identification

    Assouline, Susan G.; Lupkowski-Shoplik, Ann

    2012-01-01

    The Talent Search model, founded at Johns Hopkins University by Dr. Julian C. Stanley, is fundamentally an above-level testing program. This simplistic description belies the enduring impact that the Talent Search model has had on the lives of hundreds of thousands of gifted students as well as their parents and teachers. In this article, we…

  2. Application of Metamodels to Identification of Metallic Materials Models

    Maciej Pietrzyk

    2016-01-01

    Full Text Available Improvement of the efficiency of the inverse analysis (IA for various material tests was the objective of the paper. Flow stress models and microstructure evolution models of various complexity of mathematical formulation were considered. Different types of experiments were performed and the results were used for the identification of models. Sensitivity analysis was performed for all the models and the importance of parameters in these models was evaluated. Metamodels based on artificial neural network were proposed to simulate experiments in the inverse solution. Performed analysis has shown that significant decrease of the computing times could be achieved when metamodels substitute finite element model in the inverse analysis, which is the case in the identification of flow stress models. Application of metamodels gave good results for flow stress models based on closed form equations accounting for an influence of temperature, strain, and strain rate (4 coefficients and additionally for softening due to recrystallization (5 coefficients and for softening and saturation (7 coefficients. Good accuracy and high efficiency of the IA were confirmed. On the contrary, identification of microstructure evolution models, including phase transformation models, did not give noticeable reduction of the computing time.

  3. Multiple organizational identification levels and the impact of perceived external prestige and communication climate

    Bartels, J.; Pruyn, A.T.H.; Jong, de M.D.T.; Joustra, I.

    2007-01-01

    Earlier studies have shown that perceived external prestige and communication climate influence organizational identification. In this paper we present the results of a study of the influence of communication climate and perceived external prestige on organizational identification at various

  4. New experimental model of multiple myeloma.

    Telegin, G B; Kalinina, A R; Ponomarenko, N A; Ovsepyan, A A; Smirnov, S V; Tsybenko, V V; Homeriki, S G

    2001-06-01

    NSO/1 (P3x63Ay 8Ut) and SP20 myeloma cells were inoculated to BALB/c OlaHsd mice. NSO/1 cells allowed adequate stage-by-stage monitoring of tumor development. The adequacy of this model was confirmed in experiments with conventional cytostatics: prospidium and cytarabine caused necrosis of tumor cells and reduced animal mortality.

  5. Animal model of human disease. Multiple myeloma

    Radl, J.; Croese, J.W.; Zurcher, C.; Enden-Vieveen, M.H.M. van den; Leeuw, A.M. de

    1988-01-01

    Animal models of spontaneous and induced plasmacytomas in some inbred strains of mice have proven to be useful tools for different studies on tumorigenesis and immunoregulation. Their wide applicability and the fact that after their intravenous transplantation, the recipient mice developed bone

  6. Multiple Social Networks, Data Models and Measures for

    Magnani, Matteo; Rossi, Luca

    2017-01-01

    Multiple Social Network Analysis is a discipline defining models, measures, methodologies, and algorithms to study multiple social networks together as a single social system. It is particularly valuable when the networks are interconnected, e.g., the same actors are present in more than one...

  7. Identification of parameters of discrete-continuous models

    Cekus, Dawid; Warys, Pawel

    2015-01-01

    In the paper, the parameters of a discrete-continuous model have been identified on the basis of experimental investigations and formulation of optimization problem. The discrete-continuous model represents a cantilever stepped Timoshenko beam. The mathematical model has been formulated and solved according to the Lagrange multiplier formalism. Optimization has been based on the genetic algorithm. The presented proceeding’s stages make the identification of any parameters of discrete-continuous systems possible

  8. Identification of Influential Points in a Linear Regression Model

    Jan Grosz

    2011-03-01

    Full Text Available The article deals with the detection and identification of influential points in the linear regression model. Three methods of detection of outliers and leverage points are described. These procedures can also be used for one-sample (independentdatasets. This paper briefly describes theoretical aspects of several robust methods as well. Robust statistics is a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. A simulation model of the simple linear regression is presented.

  9. Identification of parameters of discrete-continuous models

    Cekus, Dawid, E-mail: cekus@imipkm.pcz.pl; Warys, Pawel, E-mail: warys@imipkm.pcz.pl [Institute of Mechanics and Machine Design Foundations, Czestochowa University of Technology, Dabrowskiego 73, 42-201 Czestochowa (Poland)

    2015-03-10

    In the paper, the parameters of a discrete-continuous model have been identified on the basis of experimental investigations and formulation of optimization problem. The discrete-continuous model represents a cantilever stepped Timoshenko beam. The mathematical model has been formulated and solved according to the Lagrange multiplier formalism. Optimization has been based on the genetic algorithm. The presented proceeding’s stages make the identification of any parameters of discrete-continuous systems possible.

  10. Modeling Rabbit Responses to Single and Multiple Aerosol ...

    Journal Article Survival models are developed here to predict response and time-to-response for mortality in rabbits following exposures to single or multiple aerosol doses of Bacillus anthracis spores. Hazard function models were developed for a multiple dose dataset to predict the probability of death through specifying dose-response functions and the time between exposure and the time-to-death (TTD). Among the models developed, the best-fitting survival model (baseline model) has an exponential dose-response model with a Weibull TTD distribution. Alternative models assessed employ different underlying dose-response functions and use the assumption that, in a multiple dose scenario, earlier doses affect the hazard functions of each subsequent dose. In addition, published mechanistic models are analyzed and compared with models developed in this paper. None of the alternative models that were assessed provided a statistically significant improvement in fit over the baseline model. The general approach utilizes simple empirical data analysis to develop parsimonious models with limited reliance on mechanistic assumptions. The baseline model predicts TTDs consistent with reported results from three independent high-dose rabbit datasets. More accurate survival models depend upon future development of dose-response datasets specifically designed to assess potential multiple dose effects on response and time-to-response. The process used in this paper to dev

  11. Identification of milling and baking quality QTL in multiple soft wheat mapping populations.

    Cabrera, Antonio; Guttieri, Mary; Smith, Nathan; Souza, Edward; Sturbaum, Anne; Hua, Duc; Griffey, Carl; Barnett, Marla; Murphy, Paul; Ohm, Herb; Uphaus, Jim; Sorrells, Mark; Heffner, Elliot; Brown-Guedira, Gina; Van Sanford, David; Sneller, Clay

    2015-11-01

    Two mapping approaches were use to identify and validate milling and baking quality QTL in soft wheat. Two LG were consistently found important for multiple traits and we recommend the use marker-assisted selection on specific markers reported here. Wheat-derived food products require a range of characteristics. Identification and understanding of the genetic components controlling end-use quality of wheat is important for crop improvement. We assessed the underlying genetics controlling specific milling and baking quality parameters of soft wheat including flour yield, softness equivalent, flour protein, sucrose, sodium carbonate, water absorption and lactic acid, solvent retention capacities in a diversity panel and five bi-parental mapping populations. The populations were genotyped with SSR and DArT markers, with markers specific for the 1BL.1RS translocation and sucrose synthase gene. Association analysis and composite interval mapping were performed to identify quantitative trait loci (QTL). High heritability was observed for each of the traits evaluated, trait correlations were consistent over populations, and transgressive segregants were common in all bi-parental populations. A total of 26 regions were identified as potential QTL in the diversity panel and 74 QTL were identified across all five bi-parental mapping populations. Collinearity of QTL from chromosomes 1B and 2B was observed across mapping populations and was consistent with results from the association analysis in the diversity panel. Multiple regression analysis showed the importance of the two 1B and 2B regions and marker-assisted selection for the favorable alleles at these regions should improve quality.

  12. MITIE: Simultaneous RNA-Seq-based transcript identification and quantification in multiple samples.

    Behr, Jonas; Kahles, André; Zhong, Yi; Sreedharan, Vipin T; Drewe, Philipp; Rätsch, Gunnar

    2013-10-15

    High-throughput sequencing of mRNA (RNA-Seq) has led to tremendous improvements in the detection of expressed genes and reconstruction of RNA transcripts. However, the extensive dynamic range of gene expression, technical limitations and biases, as well as the observed complexity of the transcriptional landscape, pose profound computational challenges for transcriptome reconstruction. We present the novel framework MITIE (Mixed Integer Transcript IdEntification) for simultaneous transcript reconstruction and quantification. We define a likelihood function based on the negative binomial distribution, use a regularization approach to select a few transcripts collectively explaining the observed read data and show how to find the optimal solution using Mixed Integer Programming. MITIE can (i) take advantage of known transcripts, (ii) reconstruct and quantify transcripts simultaneously in multiple samples, and (iii) resolve the location of multi-mapping reads. It is designed for genome- and assembly-based transcriptome reconstruction. We present an extensive study based on realistic simulated RNA-Seq data. When compared with state-of-the-art approaches, MITIE proves to be significantly more sensitive and overall more accurate. Moreover, MITIE yields substantial performance gains when used with multiple samples. We applied our system to 38 Drosophila melanogaster modENCODE RNA-Seq libraries and estimated the sensitivity of reconstructing omitted transcript annotations and the specificity with respect to annotated transcripts. Our results corroborate that a well-motivated objective paired with appropriate optimization techniques lead to significant improvements over the state-of-the-art in transcriptome reconstruction. MITIE is implemented in C++ and is available from http://bioweb.me/mitie under the GPL license.

  13. System identification application using Hammerstein model

    Saban Ozer

    results of the Hammerstein model focused on this study. *For correspondence. 597 ..... Example 1: In this sample study, considering the block structure given in ..... Graduate School of Natural and Applied Science, Turkey. [20] Cui M, Liu H, Li Z ...

  14. Explaining clinical behaviors using multiple theoretical models

    Eccles, Martin P; Grimshaw, Jeremy M; MacLennan, Graeme; Bonetti, Debbie; Glidewell, Liz; Pitts, Nigel B; Steen, Nick; Thomas, Ruth; Walker, Anne; Johnston, Marie

    2012-01-01

    Abstract Background In the field of implementation research, there is an increased interest in use of theory when designing implementation research studies involving behavior change. In 2003, we initiated a series of five studies to establish a scientific rationale for interventions to translate research findings into clinical practice by exploring the performance of a number of different, commonly used, overlapping behavioral theories and models. We reflect on the strengths and weaknesses of...

  15. Airport choice model in multiple airport regions

    Claudia Muñoz

    2017-02-01

    Full Text Available Purpose: This study aims to analyze travel choices made by air transportation users in multi airport regions because it is a crucial component when planning passenger redistribution policies. The purpose of this study is to find a utility function which makes it possible to know the variables that influence users’ choice of the airports on routes to the main cities in the Colombian territory. Design/methodology/approach: This research generates a Multinomial Logit Model (MNL, which is based on the theory of maximizing utility, and it is based on the data obtained on revealed and stated preference surveys applied to users who reside in the metropolitan area of Aburrá Valley (Colombia. This zone is the only one in the Colombian territory which has two neighboring airports for domestic flights. The airports included in the modeling process were Enrique Olaya Herrera (EOH Airport and José María Córdova (JMC Airport. Several structure models were tested, and the MNL proved to be the most significant revealing the common variables that affect passenger airport choice include the airfare, the price to travel the airport, and the time to get to the airport. Findings and Originality/value: The airport choice model which was calibrated corresponds to a valid powerful tool used to calculate the probability of each analyzed airport of being chosen for domestic flights in the Colombian territory. This is done bearing in mind specific characteristic of each of the attributes contained in the utility function. In addition, these probabilities will be used to calculate future market shares of the two airports considered in this study, and this will be done generating a support tool for airport and airline marketing policies.

  16. Using Pareto points for model identification in predictive toxicology

    2013-01-01

    Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology. PMID:23517649

  17. Multiple simultaneous event model for radiation carcinogenesis

    Baum, J.W.

    1976-01-01

    A mathematical model is proposed which postulates that cancer induction is a multi-event process, that these events occur naturally, usually one at a time in any cell, and that radiation frequently causes two of these events to occur simultaneously. Microdosimetric considerations dictate that for high LET radiations the simultaneous events are associated with a single particle or track. The model predicts: (a) linear dose-effect relations for early times after irradiation with small doses, (b) approximate power functions of dose (i.e. Dsup(x)) having exponent less than one for populations of mixed age examined at short times after irradiation with small doses, (c) saturation of effect at either long times after irradiation with small doses or for all times after irradiation with large doses, and (d) a net increase in incidence which is dependent on age of observation but independent of age at irradiation. Data of Vogel, for neutron induced mammary tumors in rats, are used to illustrate the validity of the formulation. This model provides a quantitative framework to explain several unexpected results obtained by Vogel. It also provides a logical framework to explain the dose-effect relations observed in the Japanese survivors of the atomic bombs. (author)

  18. Simplified fuel cell system model identification

    Caux, S.; Fadel, M. [Laboratoire d' Electrotechnique et d' Electronique Industrielle, Toulouse (France); Hankache, W. [Laboratoire d' Electrotechnique et d' Electronique Industrielle, Toulouse (France)]|[Laboratoire de recherche en Electronique, Electrotechnique et Systemes, Belfort (France); Hissel, D. [Laboratoire de recherche en Electronique, Electrotechnique et Systemes, Belfort (France)

    2006-07-01

    This paper discussed a simplified physical fuel cell model used to study fuel cell and supercap energy applications for vehicles. Anode, cathode, membrane, and electrode elements of the cell were modelled. A quasi-static Amphlett model was used to predict voltage responses of the fuel cell as a function of the current, temperature, and partial pressures of the reactive gases. The potential of each cell was multiplied by the number of cells in order to model a fuel cell stack. The model was used to describe the main phenomena associated with current voltage behaviour. Data were then compared with data from laboratory tests conducted on a 20 cell stack subjected to a current and time profile developed using speed data from a vehicle operating in an urban environment. The validated model was used to develop iterative optimization algorithms for an energy management strategy that linked 3 voltage sources with fuel cell parameters. It was concluded that classic state and dynamic measurements using a simple least square algorithm can be used to identify the most important parameters for optimal fuel cell operation. 9 refs., 1 tab., 6 figs.

  19. Multiple Sclerosis Identification Based on Fractional Fourier Entropy and a Modified Jaya Algorithm

    Shui-Hua Wang

    2018-04-01

    Full Text Available Aim: Currently, identifying multiple sclerosis (MS by human experts may come across the problem of “normal-appearing white matter”, which causes a low sensitivity. Methods: In this study, we presented a computer vision based approached to identify MS in an automatic way. This proposed method first extracted the fractional Fourier entropy map from a specified brain image. Afterwards, it sent the features to a multilayer perceptron trained by a proposed improved parameter-free Jaya algorithm. We used cost-sensitivity learning to handle the imbalanced data problem. Results: The 10 × 10-fold cross validation showed our method yielded a sensitivity of 97.40 ± 0.60%, a specificity of 97.39 ± 0.65%, and an accuracy of 97.39 ± 0.59%. Conclusions: We validated by experiments that the proposed improved Jaya performs better than plain Jaya algorithm and other latest bioinspired algorithms in terms of classification performance and training speed. In addition, our method is superior to four state-of-the-art MS identification approaches.

  20. The theory of multiple intelligences in the identification of high-ability students

    Daniel Hernández-Torrano

    2014-01-01

    Full Text Available This study provides a framework to implement the theory of multiple intelligences (MI in the identification of high-ability students in secondary education. The internal structure of three scales to assess students' MI (students, parents and teachers' ratings was analyzed in a sample of 566 students nominated as gifted by their teachers. Participants aged 11 to 16 years (M = 14.85, SD = 1.08. The results indicated differentiated intellectual profiles depending on the informant estimating students' MI. This study provided evidence for two components that allow us to analyze the cognitive competence of high-ability students beyond the areas commonly assessed at school: an academic component composed by the linguistic, logical-mathematical, naturalistic, and visual-spatial intelligences; and a non-academic component statistically loaded by the bodily-kinesthetic, musical and social intelligences. Convergence of the two components in the three scales was evidenced; and correlations between these components and students' objective performance on a psychometric intelligence test were found to be low. Finally, the utility of the MI scales to identify high-ability students in secondary education is discussed.

  1. Identification of Misconceptions through Multiple Choice Tasks at Municipal Chemistry Competition Test

    Dušica D Milenković

    2016-01-01

    Full Text Available In this paper, the level of conceptual understanding of chemical contents among seventh grade students who participated in the municipal Chemistry competition in Novi Sad, Serbia, in 2013 have been examined. Tests for the municipal chemistry competition were used as a measuring instrument, wherein only multiple choice tasks were considered and analyzed. Determination of the level of conceptual understanding of the tested chemical contents was based on the calculation of the frequency of choosing the correct answers. Thereby, identification of areas of satisfactory conceptual understanding, areas of roughly adequate performance, areas of inadequate performance, and areas of quite inadequate performance have been conducted. On the other hand, the analysis of misconceptions was based on the analysis of distractors. The results showed that satisfactory level of conceptual understanding and roughly adequate performance characterize majority of contents, which was expected since only the best students who took part in the contest were surveyed. However, this analysis identified a large number of misunderstandings, as well. In most of the cases, these misconceptions were related to the inability to distinguish elements, compounds, homogeneous and heterogeneous mixtures. Besides, it is shown that students are not familiar with crystal structure of the diamond, and with metric prefixes. The obtained results indicate insufficient visualization of the submicroscopic level in school textbooks, the imprecise use of chemical language by teachers and imprecise use of language in chemistry textbooks.

  2. Multiple Imputation of Predictor Variables Using Generalized Additive Models

    de Jong, Roel; van Buuren, Stef; Spiess, Martin

    2016-01-01

    The sensitivity of multiple imputation methods to deviations from their distributional assumptions is investigated using simulations, where the parameters of scientific interest are the coefficients of a linear regression model, and values in predictor variables are missing at random. The

  3. Identification of linear error-models with projected dynamical systems

    Krejčí, Pavel; Kuhnen, K.

    2004-01-01

    Roč. 10, č. 1 (2004), s. 59-91 ISSN 1387-3954 Keywords : identification * error models * projected dynamical systems Subject RIV: BA - General Mathematics Impact factor: 0.292, year: 2004 http://www.informaworld.com/smpp/content~db=all~content=a713682517

  4. Robust model identification applied to type 1diabetes

    Finan, Daniel Aaron; Jørgensen, John Bagterp; Poulsen, Niels Kjølstad

    2010-01-01

    In many realistic applications, process noise is known to be neither white nor normally distributed. When identifying models in these cases, it may be more effective to minimize a different penalty function than the standard sum of squared errors (as in a least-squares identification method). Thi...

  5. Systematic approach for the identification of process reference models

    Van Der Merwe, A

    2009-02-01

    Full Text Available and make it economically viable. In the identification of core elements within the process reference model, the focus is often on the end-product and not on the procedure used to identify the elements. As often proved in development of projects, there is a...

  6. A review on modeling, identification and servo control of robotic ...

    user

    This article reviews modeling, identification, and low level control of the robotic excavator. ... The oil viscosity, oil flow through the spool valves, and variable loading, ..... squares, to identify all the unknown individual parameters for a unmanned ..... Robust low level control of robotic excavation, PhD Thesis, The University of ...

  7. Entrepreneurial intention modeling using hierarchical multiple regression

    Marina Jeger

    2014-12-01

    Full Text Available The goal of this study is to identify the contribution of effectuation dimensions to the predictive power of the entrepreneurial intention model over and above that which can be accounted for by other predictors selected and confirmed in previous studies. As is often the case in social and behavioral studies, some variables are likely to be highly correlated with each other. Therefore, the relative amount of variance in the criterion variable explained by each of the predictors depends on several factors such as the order of variable entry and sample specifics. The results show the modest predictive power of two dimensions of effectuation prior to the introduction of the theory of planned behavior elements. The article highlights the main advantages of applying hierarchical regression in social sciences as well as in the specific context of entrepreneurial intention formation, and addresses some of the potential pitfalls that this type of analysis entails.

  8. Multiple Time Series Ising Model for Financial Market Simulations

    Takaishi, Tetsuya

    2015-01-01

    In this paper we propose an Ising model which simulates multiple financial time series. Our model introduces the interaction which couples to spins of other systems. Simulations from our model show that time series exhibit the volatility clustering that is often observed in the real financial markets. Furthermore we also find non-zero cross correlations between the volatilities from our model. Thus our model can simulate stock markets where volatilities of stocks are mutually correlated

  9. Identification of optimal strategies for energy management systems planning under multiple uncertainties

    Cai, Y.P.; Huang, G.H.; Yang, Z.F.; Tan, Q.

    2009-01-01

    Management of energy resources is crucial for many regions throughout the world. Many economic, environmental and political factors are having significant effects on energy management practices, leading to a variety of uncertainties in relevant decision making. The objective of this research is to identify optimal strategies in the planning of energy management systems under multiple uncertainties through the development of a fuzzy-random interval programming (FRIP) model. The method is based on an integration of the existing interval linear programming (ILP), superiority-inferiority-based fuzzy-stochastic programming (SI-FSP) and mixed integer linear programming (MILP). Such a FRIP model allows multiple uncertainties presented as interval values, possibilistic and probabilistic distributions, as well as their combinations within a general optimization framework. It can also be used for facilitating capacity-expansion planning of energy-production facilities within a multi-period and multi-option context. Complexities in energy management systems can be systematically reflected, thus applicability of the modeling process can be highly enhanced. The developed method has then been applied to a case of long-term energy management planning for a region with three cities. Useful solutions for the planning of energy management systems were generated. Interval solutions associated with different risk levels of constraint violation were obtained. They could be used for generating decision alternatives and thus help decision makers identify desired policies under various economic and system-reliability constraints. The solutions can also provide desired energy resource/service allocation and capacity-expansion plans with a minimized system cost, a maximized system reliability and a maximized energy security. Tradeoffs between system costs and constraint-violation risks could be successfully tackled, i.e., higher costs will increase system stability, while a desire for lower

  10. On the identification of fractionally cointegrated VAR models with the F(d) condition

    Carlini, Federico; Santucci de Magistris, Paolo

    for any choice of the lag length, also when the true cointegration rank is known. The properties of these multiple non-identified models are studied and a necessary and sufficient condition for the identification of the fractional parameters of the system is provided. The condition is named F(d......) and it is a generalization to the fractional case of the I(1) condition in the VECM model. The assessment of the F(d) condition in the empirical analysis is relevant for the determination of the fractional parameters as well as the number of lags. The paper also illustrates the indeterminacy between the cointegration rank...

  11. Modeling and identification of induction micromachines in microelectromechanical systems applications

    Lyshevski, S.E. [Purdue University at Indianapolis (United States). Dept. of Electrical and Computer Engineering

    2002-11-01

    Microelectromechanical systems (MEMS), which integrate motion microstructures, radiating energy microdevices, controlling and signal processing integrated circuits (ICs), are widely used. Rotational and translational electromagnetic based micromachines are used in MEMS as actuators and sensors. Brushless high performance micromachines are the preferable choice in different MEMS applications, and therefore, synchronous and induction micromachines are the best candidates. Affordability, good performance characteristics (efficiency, controllability, robustness, reliability, power and torque densities etc.) and expanded operating envelopes result in a strong interest in the application of induction micromachines. In addition, induction micromachines can be easily fabricated using surface micromachining and high aspect ratio fabrication technologies. Thus, it is anticipated that induction micromachines, controlled using different control algorithms implemented using ICs, will be widely used in MEMS. Controllers can be implemented using specifically designed ICs to attain superior performance, maximize efficiency and controllability, minimize losses and electromagnetic interference, reduce noise and vibration, etc. In order to design controllers, the induction micromachine must be modeled, and its mathematical model parameters must be identified. Using microelectromechanics, nonlinear mathematical models are derived. This paper illustrates the application of nonlinear identification methods as applied to identify the unknown parameters of three phase induction micromachines. Two identification methods are studied. In particular, nonlinear error mapping technique and least squares identification are researched. Analytical and numerical results, as well as practical capabilities and effectiveness, are illustrated, identifying the unknown parameters of a three phase brushless induction micromotor. Experimental results fully support the identification methods. (author)

  12. Correlations in multiple production on nuclei and Glauber model of multiple scattering

    Zoller, V.R.; Nikolaev, N.N.

    1982-01-01

    Critical analysis of possibility for describing correlation phenomena during multiple production on nuclei within the framework of the Glauber multiple seattering model generalized for particle production processes with Capella, Krziwinski and Shabelsky has been performed. It was mainly concluded that the suggested generalization of the Glauber model gives dependences on Ng(Np) (where Ng-the number of ''grey'' tracess, and Np-the number of protons flying out of nucleus) and, eventually, on #betta# (where #betta#-the number of intranuclear interactions) contradicting experience. Independent of choice of relation between #betta# and Ng(Np) in the model the rapidity corrletor Rsub(eta) is overstated in the central region and understated in the region of nucleus fragmentation. In mean multiplicities these two contradictions of experience are disguised with random compensation and agreement with experience in Nsub(S) (function of Ng) cannot be an argument in favour of the model. It is concluded that eiconal model doesn't permit to quantitatively describe correlation phenomena during the multiple production on nuclei

  13. Optimization of inverse model identification for multi-axial test rig control

    Müller Tino

    2016-01-01

    Full Text Available Laboratory testing of multi-axial fatigue situations improves repeatability and allows a time condensing of tests which can be carried out until component failure, compared to field testing. To achieve realistic and convincing durability results, precise load data reconstruction is necessary. Cross-talk and a high number of degrees of freedom negatively affect the control accuracy. Therefore a multiple input/multiple output (MIMO model of the system, capturing all inherent cross-couplings is identified. In a first step the model order is estimated based on the physical fundamentals of a one channel hydraulic-servo system. Subsequently, the structure of the MIMO model is optimized using correlation of the outputs, to increase control stability and reduce complexity of the parameter optimization. The identification process is successfully applied to the iterative control of a multi-axial suspension rig. The results show accurate control, with increased stability compared to control without structure optimization.

  14. A Systematic Identification Method for Thermodynamic Property Modelling

    Ana Perederic, Olivia; Cunico, Larissa; Sarup, Bent

    2017-01-01

    In this work, a systematic identification method for thermodynamic property modelling is proposed. The aim of the method is to improve the quality of phase equilibria prediction by group contribution based property prediction models. The method is applied to lipid systems where the Original UNIFAC...... model is used. Using the proposed method for estimating the interaction parameters using only VLE data, a better phase equilibria prediction for both VLE and SLE was obtained. The results were validated and compared with the original model performance...

  15. Multiple Response Regression for Gaussian Mixture Models with Known Labels.

    Lee, Wonyul; Du, Ying; Sun, Wei; Hayes, D Neil; Liu, Yufeng

    2012-12-01

    Multiple response regression is a useful regression technique to model multiple response variables using the same set of predictor variables. Most existing methods for multiple response regression are designed for modeling homogeneous data. In many applications, however, one may have heterogeneous data where the samples are divided into multiple groups. Our motivating example is a cancer dataset where the samples belong to multiple cancer subtypes. In this paper, we consider modeling the data coming from a mixture of several Gaussian distributions with known group labels. A naive approach is to split the data into several groups according to the labels and model each group separately. Although it is simple, this approach ignores potential common structures across different groups. We propose new penalized methods to model all groups jointly in which the common and unique structures can be identified. The proposed methods estimate the regression coefficient matrix, as well as the conditional inverse covariance matrix of response variables. Asymptotic properties of the proposed methods are explored. Through numerical examples, we demonstrate that both estimation and prediction can be improved by modeling all groups jointly using the proposed methods. An application to a glioblastoma cancer dataset reveals some interesting common and unique gene relationships across different cancer subtypes.

  16. Reduced Complexity Volterra Models for Nonlinear System Identification

    Hacıoğlu Rıfat

    2001-01-01

    Full Text Available A broad class of nonlinear systems and filters can be modeled by the Volterra series representation. However, its practical use in nonlinear system identification is sometimes limited due to the large number of parameters associated with the Volterra filter′s structure. The parametric complexity also complicates design procedures based upon such a model. This limitation for system identification is addressed in this paper using a Fixed Pole Expansion Technique (FPET within the Volterra model structure. The FPET approach employs orthonormal basis functions derived from fixed (real or complex pole locations to expand the Volterra kernels and reduce the number of estimated parameters. That the performance of FPET can considerably reduce the number of estimated parameters is demonstrated by a digital satellite channel example in which we use the proposed method to identify the channel dynamics. Furthermore, a gradient-descent procedure that adaptively selects the pole locations in the FPET structure is developed in the paper.

  17. Identification of dust storm source areas in West Asia using multiple environmental datasets.

    Cao, Hui; Amiraslani, Farshad; Liu, Jian; Zhou, Na

    2015-01-01

    Sand and Dust storms are common phenomena in arid and semi-arid areas. West Asia Region, especially Tigris-Euphrates alluvial plain, has been recognized as one of the most important dust source areas in the world. In this paper, a method is applied to extract SDS (Sand and Dust Storms) sources in West Asia region using thematic maps, climate and geography, HYSPLIT model and satellite images. Out of 50 dust storms happened during 2000-2013 and collected in form of MODIS images, 27 events were incorporated as demonstrations of the simulated trajectories by HYSPLIT model. Besides, a dataset of the newly released Landsat images was used as base-map for the interpretation of SDS source regions. As a result, six main clusters were recognized as dust source areas. Of which, 3 clusters situated in Tigris-Euphrates plain were identified as severe SDS sources (including 70% dust storms in this research). Another cluster in Sistan plain is also a potential source area. This approach also confirmed six main paths causing dust storms. These paths are driven by the climate system including Siberian and Polar anticyclones, monsoon from Indian Subcontinent and depression from north of Africa. The identification of SDS source areas and paths will improve our understandings on the mechanisms and impacts of dust storms on socio-economy and environment of the region. Copyright © 2014 Elsevier B.V. All rights reserved.

  18. Particle Identification in Jets and High-Multiplicity pp Events with the ALICE TPC

    AUTHOR|(SzGeCERN)683272; Vogelsang, Werner

    The spectra of identified particles in a collision experiment comprise crucial information about the underlying physical processes. The ALICE experiment has powerful Particle IDentification (PID) capabilities, which are unique at the Large Hadron Collider (LHC). In this thesis, a statistical PID method based on the specific energy loss d$E$/d$x$ in the ALICE Time Projection Chamber (TPC) is developed: the TPC Multi-Template Fit (MTF). The MTF allows for the extraction of identified charged particle spectra in a wide momentum range, which extends from about 150 MeV/$c$ to above 20 GeV/$c$. The TPC PID requires a detailed modelling of the TPC d$E$/d$x$ response for momenta above 2-3 GeV/$c$. A framework is developed that allows for the determination of the model parameters and for evaluating the PID information of charged particles. With the MTF, the transverse momentum $p_{\\mathrm{T}}$ spectra of charged pions, kaons and protons at mid-rapidity ($|\\eta| < 0.9$) are measured for pp collisions at $\\sqrt{s}$ ...

  19. Contribution to the modeling and the identification of haptic interfaces

    Janot, A.

    2007-12-01

    This thesis focuses on the modeling and the identification of haptic interfaces using cable drive. An haptic interface is a force feedback device, which enables its user to interact with a virtual world or a remote environment explored by a slave system. It aims at the matching between the forces and displacements given by the user and those applied to virtual world. Usually, haptic interfaces make use of a mechanical actuated structure whose distal link is equipped with a handle. When manipulating this handle to interact with explored world, the user feels the apparent mass, compliance and friction of the interface. This distortion introduced between the operator and the virtual world must be modeled and identified to enhance the design of the interface and develop appropriate control laws. The first approach has been to adapt the modeling and identification methods of rigid and localized flexibilities robots to haptic interfaces. The identification technique makes use of the inverse dynamic model and the linear least squares with the measurements of joint torques and positions. This approach is validated on a single degree of freedom and a three degree of freedom haptic devices. A new identification method needing only torque data is proposed. It is based on a closed loop simulation using the direct dynamic model. The optimal parameters minimize the 2 norms of the error between the actual torque and the simulated torque assuming the same control law and the same tracking trajectory. This non linear least squares problem dramatically is simplified using the inverse model to calculate the simulated torque. This method is validated on the single degree of freedom haptic device and the SCARA robot. (author)

  20. Efficient Adoption and Assessment of Multiple Process Improvement Reference Models

    Simona Jeners

    2013-06-01

    Full Text Available A variety of reference models such as CMMI, COBIT or ITIL support IT organizations to improve their processes. These process improvement reference models (IRMs cover different domains such as IT development, IT Services or IT Governance but also share some similarities. As there are organizations that address multiple domains and need to coordinate their processes in their improvement we present MoSaIC, an approach to support organizations to efficiently adopt and conform to multiple IRMs. Our solution realizes a semantic integration of IRMs based on common meta-models. The resulting IRM integration model enables organizations to efficiently implement and asses multiple IRMs and to benefit from synergy effects.

  1. Detection and Identification of Multiple Stationary Human Targets Via Bio-Radar Based on the Cross-Correlation Method

    Yang Zhang

    2016-10-01

    Full Text Available Ultra-wideband (UWB radar has been widely used for detecting human physiological signals (respiration, movement, etc. in the fields of rescue, security, and medicine owing to its high penetrability and range resolution. In these applications, especially in rescue after disaster (earthquake, collapse, mine accident, etc., the presence, number, and location of the trapped victims to be detected and rescued are the key issues of concern. Ample research has been done on the first issue, whereas the identification and localization of multi-targets remains a challenge. False positive and negative identification results are two common problems associated with the detection of multiple stationary human targets. This is mainly because the energy of the signal reflected from the target close to the receiving antenna is considerably stronger than those of the targets at further range, often leading to missing or false recognition if the identification method is based on the energy of the respiratory signal. Therefore, a novel method based on cross-correlation is proposed in this paper that is based on the relativity and periodicity of the signals, rather than on the energy. The validity of this method is confirmed through experiments using different scenarios; the results indicate a discernible improvement in the detection precision and identification of the multiple stationary targets.

  2. Detection and Identification of Multiple Stationary Human Targets Via Bio-Radar Based on the Cross-Correlation Method.

    Zhang, Yang; Chen, Fuming; Xue, Huijun; Li, Zhao; An, Qiang; Wang, Jianqi; Zhang, Yang

    2016-10-27

    Ultra-wideband (UWB) radar has been widely used for detecting human physiological signals (respiration, movement, etc.) in the fields of rescue, security, and medicine owing to its high penetrability and range resolution. In these applications, especially in rescue after disaster (earthquake, collapse, mine accident, etc.), the presence, number, and location of the trapped victims to be detected and rescued are the key issues of concern. Ample research has been done on the first issue, whereas the identification and localization of multi-targets remains a challenge. False positive and negative identification results are two common problems associated with the detection of multiple stationary human targets. This is mainly because the energy of the signal reflected from the target close to the receiving antenna is considerably stronger than those of the targets at further range, often leading to missing or false recognition if the identification method is based on the energy of the respiratory signal. Therefore, a novel method based on cross-correlation is proposed in this paper that is based on the relativity and periodicity of the signals, rather than on the energy. The validity of this method is confirmed through experiments using different scenarios; the results indicate a discernible improvement in the detection precision and identification of the multiple stationary targets.

  3. Model Seleksi Premi Asuransi Jiwa Dwiguna untuk Kasus Multiple Decrement

    Cita, Devi Ramana; Pane, Rolan; ', Harison

    2015-01-01

    This article discusses a select survival model for the case of multiple decrements in evaluating endowment life insurance premium for person currently aged ( + ) years, who is selected at age with ℎ years selection period. The case of multiple decrements in this case is limited to two cases. The calculation of the annual premium is done by prior evaluating of the single premium, and the present value of annuity depends on theconstant force assumption.

  4. Multiple organizational identification levels and the impact of perceived external prestige and communication climate

    Bartels, J.; Pruyn, Adriaan T.H.; de Jong, Menno D.T.; Joustra, Inge

    2007-01-01

    sEarlier studies have shown that perceived external prestige and communication climate influence organizational identification. In this paper we present the results of a study of the influence of communication climate and perceived external prestige on organizational identification at various

  5. Computerized nipple identification for multiple image analysis in computer-aided diagnosis

    Zhou Chuan; Chan Heangping; Paramagul, Chintana; Roubidoux, Marilyn A.; Sahiner, Berkman; Hadjiiski, Labomir M.; Petrick, Nicholas

    2004-01-01

    Correlation of information from multiple-view mammograms (e.g., MLO and CC views, bilateral views, or current and prior mammograms) can improve the performance of breast cancer diagnosis by radiologists or by computer. The nipple is a reliable and stable landmark on mammograms for the registration of multiple mammograms. However, accurate identification of nipple location on mammograms is challenging because of the variations in image quality and in the nipple projections, resulting in some nipples being nearly invisible on the mammograms. In this study, we developed a computerized method to automatically identify the nipple location on digitized mammograms. First, the breast boundary was obtained using a gradient-based boundary tracking algorithm, and then the gray level profiles along the inside and outside of the boundary were identified. A geometric convergence analysis was used to limit the nipple search to a region of the breast boundary. A two-stage nipple detection method was developed to identify the nipple location using the gray level information around the nipple, the geometric characteristics of nipple shapes, and the texture features of glandular tissue or ducts which converge toward the nipple. At the first stage, a rule-based method was designed to identify the nipple location by detecting significant changes of intensity along the gray level profiles inside and outside the breast boundary and the changes in the boundary direction. At the second stage, a texture orientation-field analysis was developed to estimate the nipple location based on the convergence of the texture pattern of glandular tissue or ducts towards the nipple. The nipple location was finally determined from the detected nipple candidates by a rule-based confidence analysis. In this study, 377 and 367 randomly selected digitized mammograms were used for training and testing the nipple detection algorithm, respectively. Two experienced radiologists identified the nipple locations

  6. Design and selection of load control strategies using a multiple objective model and evolutionary algorithms

    Gomes, Alvaro; Antunes, Carlos Henggeler; Martins, Antonio Gomes

    2005-01-01

    This paper aims at presenting a multiple objective model to evaluate the attractiveness of the use of demand resources (through load management control actions) by different stakeholders and in diverse structure scenarios in electricity systems. For the sake of model flexibility, the multiple (and conflicting) objective functions of technical, economical and quality of service nature are able to capture distinct market scenarios and operating entities that may be interested in promoting load management activities. The computation of compromise solutions is made by resorting to evolutionary algorithms, which are well suited to tackle multiobjective problems of combinatorial nature herein involving the identification and selection of control actions to be applied to groups of loads. (Author)

  7. AgMIP Training in Multiple Crop Models and Tools

    Boote, Kenneth J.; Porter, Cheryl H.; Hargreaves, John; Hoogenboom, Gerrit; Thornburn, Peter; Mutter, Carolyn

    2015-01-01

    The Agricultural Model Intercomparison and Improvement Project (AgMIP) has the goal of using multiple crop models to evaluate climate impacts on agricultural production and food security in developed and developing countries. There are several major limitations that must be overcome to achieve this goal, including the need to train AgMIP regional research team (RRT) crop modelers to use models other than the ones they are currently familiar with, plus the need to harmonize and interconvert the disparate input file formats used for the various models. Two activities were followed to address these shortcomings among AgMIP RRTs to enable them to use multiple models to evaluate climate impacts on crop production and food security. We designed and conducted courses in which participants trained on two different sets of crop models, with emphasis on the model of least experience. In a second activity, the AgMIP IT group created templates for inputting data on soils, management, weather, and crops into AgMIP harmonized databases, and developed translation tools for converting the harmonized data into files that are ready for multiple crop model simulations. The strategies for creating and conducting the multi-model course and developing entry and translation tools are reviewed in this chapter.

  8. Parameter identification in a nonlinear nuclear reactor model using quasilinearization

    Barreto, J.M.; Martins Neto, A.F.; Tanomaru, N.

    1980-09-01

    Parameter identification in a nonlinear, lumped parameter, nuclear reactor model is carried out using discrete output power measurements during the transient caused by an external reactivity change. In order to minimize the difference between the model and the reactor power responses, the parameter promt neutron generation time and a parameter in fuel temperature reactivity coefficient equation are adjusted using quasilinearization. The influences of the external reactivity disturbance, the number and frequency of measurements and the measurement noise level on the method accuracy and rate of convergence are analysed through simulation. Procedures for the design of the identification experiments are suggested. The method proved to be very effective for low level noise measurements. (Author) [pt

  9. System Identification, Environmental Modelling, and Control System Design

    Garnier, Hugues

    2012-01-01

    System Identification, Environmetric Modelling, and Control Systems Design is dedicated to Professor Peter Young on the occasion of his seventieth birthday. Professor Young has been a pioneer in systems and control, and over the past 45 years he has influenced many developments in this field. This volume is comprised of a collection of contributions by leading experts in system identification, time-series analysis, environmetric modelling and control system design – modern research in topics that reflect important areas of interest in Professor Young’s research career. Recent theoretical developments in and relevant applications of these areas are explored treating the various subjects broadly and in depth. The authoritative and up-to-date research presented here will be of interest to academic researcher in control and disciplines related to environmental research, particularly those to with water systems. The tutorial style in which many of the contributions are composed also makes the book suitable as ...

  10. A Bayesian Hierarchical Model for Relating Multiple SNPs within Multiple Genes to Disease Risk

    Lewei Duan

    2013-01-01

    Full Text Available A variety of methods have been proposed for studying the association of multiple genes thought to be involved in a common pathway for a particular disease. Here, we present an extension of a Bayesian hierarchical modeling strategy that allows for multiple SNPs within each gene, with external prior information at either the SNP or gene level. The model involves variable selection at the SNP level through latent indicator variables and Bayesian shrinkage at the gene level towards a prior mean vector and covariance matrix that depend on external information. The entire model is fitted using Markov chain Monte Carlo methods. Simulation studies show that the approach is capable of recovering many of the truly causal SNPs and genes, depending upon their frequency and size of their effects. The method is applied to data on 504 SNPs in 38 candidate genes involved in DNA damage response in the WECARE study of second breast cancers in relation to radiotherapy exposure.

  11. Validation of the measurement model concept for error structure identification

    Shukla, Pavan K.; Orazem, Mark E.; Crisalle, Oscar D.

    2004-01-01

    The development of different forms of measurement models for impedance has allowed examination of key assumptions on which the use of such models to assess error structure are based. The stochastic error structures obtained using the transfer-function and Voigt measurement models were identical, even when non-stationary phenomena caused some of the data to be inconsistent with the Kramers-Kronig relations. The suitability of the measurement model for assessment of consistency with the Kramers-Kronig relations, however, was found to be more sensitive to the confidence interval for the parameter estimates than to the number of parameters in the model. A tighter confidence interval was obtained for Voigt measurement model, which made the Voigt measurement model a more sensitive tool for identification of inconsistencies with the Kramers-Kronig relations

  12. Reflector modelization for neutronic diffusion and parameters identification

    Argaud, J.P.

    1993-04-01

    Physical parameters of neutronic diffusion equations can be adjusted to decrease calculations-measurements errors. The reflector being always difficult to modelize, we choose to elaborate a new reflector model and to use the parameters of this model as adjustment coefficients in the identification procedure. Using theoretical results, and also the physical behaviour of neutronic flux solutions, the reflector model consists then in its replacement by boundary conditions for the diffusion equations on the core only. This theoretical result of non-local operator relations leads then to some discrete approximations by taking into account the multiscaled behaviour, on the core-reflector interface, of neutronic diffusion solutions. The resulting model of this approach is then compared with previous reflector modelizations, and first results indicate that this new model gives the same representation of reflector for the core than previous. (author). 12 refs

  13. Probability of identification: a statistical model for the validation of qualitative botanical identification methods.

    LaBudde, Robert A; Harnly, James M

    2012-01-01

    A qualitative botanical identification method (BIM) is an analytical procedure that returns a binary result (1 = Identified, 0 = Not Identified). A BIM may be used by a buyer, manufacturer, or regulator to determine whether a botanical material being tested is the same as the target (desired) material, or whether it contains excessive nontarget (undesirable) material. The report describes the development and validation of studies for a BIM based on the proportion of replicates identified, or probability of identification (POI), as the basic observed statistic. The statistical procedures proposed for data analysis follow closely those of the probability of detection, and harmonize the statistical concepts and parameters between quantitative and qualitative method validation. Use of POI statistics also harmonizes statistical concepts for botanical, microbiological, toxin, and other analyte identification methods that produce binary results. The POI statistical model provides a tool for graphical representation of response curves for qualitative methods, reporting of descriptive statistics, and application of performance requirements. Single collaborator and multicollaborative study examples are given.

  14. Identification of Chemical Reactor Plant’s Mathematical Model

    Pyakullya, Boris Ivanovich; Kladiev, Sergey Nikolaevich

    2015-01-01

    This work presents a solution of the identification problem of chemical reactor plant’s mathematical model. The main goal is to obtain a mathematical description of a chemical reactor plant from experimental data, which based on plant’s time response measurements. This data consists sequence of measurements for water jacket temperature and information about control input signal, which is used to govern plant’s behavior.

  15. Identification of Chemical Reactor Plant’s Mathematical Model

    Pyakillya Boris

    2015-01-01

    Full Text Available This work presents a solution of the identification problem of chemical reactor plant’s mathematical model. The main goal is to obtain a mathematical description of a chemical reactor plant from experimental data, which based on plant’s time response measurements. This data consists sequence of measurements for water jacket temperature and information about control input signal, which is used to govern plant’s behavior.

  16. TLM modeling and system identification of optimized antenna structures

    N. Fichtner

    2008-05-01

    Full Text Available The transmission line matrix (TLM method in conjunction with the genetic algorithm (GA is presented for the bandwidth optimization of a low profile patch antenna. The optimization routine is supplemented by a system identification (SI procedure. By the SI the model parameters of the structure are estimated which is used for a reduction of the total TLM simulation time. The SI utilizes a new stability criterion of the physical poles for the parameter extraction.

  17. Parametric modeling for damped sinusoids from multiple channels

    Zhou, Zhenhua; So, Hing Cheung; Christensen, Mads Græsbøll

    2013-01-01

    frequencies and damping factors are then computed with the multi-channel weighted linear prediction method. The estimated sinusoidal poles are then matched to each channel according to the extreme value theory of distribution of random fields. Simulations are performed to show the performance advantages......The problem of parametric modeling for noisy damped sinusoidal signals from multiple channels is addressed. Utilizing the shift invariance property of the signal subspace, the number of distinct sinusoidal poles in the multiple channels is first determined. With the estimated number, the distinct...... of the proposed multi-channel sinusoidal modeling methodology compared with existing methods....

  18. A Multiple Model Prediction Algorithm for CNC Machine Wear PHM

    Huimin Chen

    2011-01-01

    Full Text Available The 2010 PHM data challenge focuses on the remaining useful life (RUL estimation for cutters of a high speed CNC milling machine using measurements from dynamometer, accelerometer, and acoustic emission sensors. We present a multiple model approach for wear depth estimation of milling machine cutters using the provided data. The feature selection, initial wear estimation and multiple model fusion components of the proposed algorithm are explained in details and compared with several alternative methods using the training data. The final submission ranked #2 among professional and student participants and the method is applicable to other data driven PHM problems.

  19. Multiple-tracer tests for contaminant transport process identification in saturated municipal solid waste

    Woodman, N.D.; Rees-White, T.C.; Stringfellow, A.M.; Beaven, R.P.; Hudson, A.P.

    2015-01-01

    Highlights: • Multiple tracers were applied to saturated MSW to test dual-porosity properties. • Lithium demonstrated to be non-conservative as a tracer. • 260 mm diameter column too small to test transport properties of MSW. • The classical advection-dispersion mode was rejected due to high dispersivity. • Characteristic diffusion times did not vary with the tracer. - Abstract: Two column tests were performed in conditions emulating vertical flow beneath the leachate table in a biologically active landfill to determine dominant transport mechanisms occurring in landfills. An improved understanding of contaminant transport process in wastes is required for developing better predictions about potential length of the long term aftercare of landfills, currently measured in timescales of centuries. Three tracers (lithium, bromide and deuterium) were used. Lithium did not behave conservatively. Given that lithium has been used extensively for tracing in landfill wastes, the tracer itself and the findings of previous tests which assume that it has behaved conservatively may need revisiting. The smaller column test could not be fitted with continuum models, probably because the volume of waste was below a representative elemental volume. Modelling compared advection-dispersion (AD), dual porosity (DP) and hybrid AD–DP models. Of these models, the DP model was found to be the most suitable. Although there is good evidence to suggest that diffusion is an important transport mechanism, the breakthrough curves of the different tracers did not differ from each other as would be predicted based on the free-water diffusion coefficients. This suggested that solute diffusion in wastes requires further study

  20. Multiple-tracer tests for contaminant transport process identification in saturated municipal solid waste

    Woodman, N.D., E-mail: n.d.woodman@soton.ac.uk; Rees-White, T.C.; Stringfellow, A.M.; Beaven, R.P.; Hudson, A.P.

    2015-04-15

    Highlights: • Multiple tracers were applied to saturated MSW to test dual-porosity properties. • Lithium demonstrated to be non-conservative as a tracer. • 260 mm diameter column too small to test transport properties of MSW. • The classical advection-dispersion mode was rejected due to high dispersivity. • Characteristic diffusion times did not vary with the tracer. - Abstract: Two column tests were performed in conditions emulating vertical flow beneath the leachate table in a biologically active landfill to determine dominant transport mechanisms occurring in landfills. An improved understanding of contaminant transport process in wastes is required for developing better predictions about potential length of the long term aftercare of landfills, currently measured in timescales of centuries. Three tracers (lithium, bromide and deuterium) were used. Lithium did not behave conservatively. Given that lithium has been used extensively for tracing in landfill wastes, the tracer itself and the findings of previous tests which assume that it has behaved conservatively may need revisiting. The smaller column test could not be fitted with continuum models, probably because the volume of waste was below a representative elemental volume. Modelling compared advection-dispersion (AD), dual porosity (DP) and hybrid AD–DP models. Of these models, the DP model was found to be the most suitable. Although there is good evidence to suggest that diffusion is an important transport mechanism, the breakthrough curves of the different tracers did not differ from each other as would be predicted based on the free-water diffusion coefficients. This suggested that solute diffusion in wastes requires further study.

  1. Level-set techniques for facies identification in reservoir modeling

    Iglesias, Marco A.; McLaughlin, Dennis

    2011-03-01

    In this paper we investigate the application of level-set techniques for facies identification in reservoir models. The identification of facies is a geometrical inverse ill-posed problem that we formulate in terms of shape optimization. The goal is to find a region (a geologic facies) that minimizes the misfit between predicted and measured data from an oil-water reservoir. In order to address the shape optimization problem, we present a novel application of the level-set iterative framework developed by Burger in (2002 Interfaces Free Bound. 5 301-29 2004 Inverse Problems 20 259-82) for inverse obstacle problems. The optimization is constrained by (the reservoir model) a nonlinear large-scale system of PDEs that describes the reservoir dynamics. We reformulate this reservoir model in a weak (integral) form whose shape derivative can be formally computed from standard results of shape calculus. At each iteration of the scheme, the current estimate of the shape derivative is utilized to define a velocity in the level-set equation. The proper selection of this velocity ensures that the new shape decreases the cost functional. We present results of facies identification where the velocity is computed with the gradient-based (GB) approach of Burger (2002) and the Levenberg-Marquardt (LM) technique of Burger (2004). While an adjoint formulation allows the straightforward application of the GB approach, the LM technique requires the computation of the large-scale Karush-Kuhn-Tucker system that arises at each iteration of the scheme. We efficiently solve this system by means of the representer method. We present some synthetic experiments to show and compare the capabilities and limitations of the proposed implementations of level-set techniques for the identification of geologic facies.

  2. Level-set techniques for facies identification in reservoir modeling

    Iglesias, Marco A; McLaughlin, Dennis

    2011-01-01

    In this paper we investigate the application of level-set techniques for facies identification in reservoir models. The identification of facies is a geometrical inverse ill-posed problem that we formulate in terms of shape optimization. The goal is to find a region (a geologic facies) that minimizes the misfit between predicted and measured data from an oil–water reservoir. In order to address the shape optimization problem, we present a novel application of the level-set iterative framework developed by Burger in (2002 Interfaces Free Bound. 5 301–29; 2004 Inverse Problems 20 259–82) for inverse obstacle problems. The optimization is constrained by (the reservoir model) a nonlinear large-scale system of PDEs that describes the reservoir dynamics. We reformulate this reservoir model in a weak (integral) form whose shape derivative can be formally computed from standard results of shape calculus. At each iteration of the scheme, the current estimate of the shape derivative is utilized to define a velocity in the level-set equation. The proper selection of this velocity ensures that the new shape decreases the cost functional. We present results of facies identification where the velocity is computed with the gradient-based (GB) approach of Burger (2002) and the Levenberg–Marquardt (LM) technique of Burger (2004). While an adjoint formulation allows the straightforward application of the GB approach, the LM technique requires the computation of the large-scale Karush–Kuhn–Tucker system that arises at each iteration of the scheme. We efficiently solve this system by means of the representer method. We present some synthetic experiments to show and compare the capabilities and limitations of the proposed implementations of level-set techniques for the identification of geologic facies

  3. Species-independent identification of known and novel recurrent genomic entities in multiple cancer patients

    Friis-Nielsen, Jens; Gonzalez-Izarzugaza, Jose Maria; Brunak, Søren

    2016-01-01

    Here we present a new method for the identification of recurrent genomic entities that play a causative role in the onset of disease. Our approach is particularly amenable for the analyses highthroughput sequencing data.......Here we present a new method for the identification of recurrent genomic entities that play a causative role in the onset of disease. Our approach is particularly amenable for the analyses highthroughput sequencing data....

  4. Multiple regression and beyond an introduction to multiple regression and structural equation modeling

    Keith, Timothy Z

    2014-01-01

    Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. Covers both MR and SEM, while explaining their relevance to one another Also includes path analysis, confirmatory factor analysis, and latent growth modeling Figures and tables throughout provide examples and illustrate key concepts and techniques For additional resources, please visit: http://tzkeith.com/.

  5. A collaborative scheduling model for the supply-hub with multiple suppliers and multiple manufacturers.

    Li, Guo; Lv, Fei; Guan, Xu

    2014-01-01

    This paper investigates a collaborative scheduling model in the assembly system, wherein multiple suppliers have to deliver their components to the multiple manufacturers under the operation of Supply-Hub. We first develop two different scenarios to examine the impact of Supply-Hub. One is that suppliers and manufacturers make their decisions separately, and the other is that the Supply-Hub makes joint decisions with collaborative scheduling. The results show that our scheduling model with the Supply-Hub is a NP-complete problem, therefore, we propose an auto-adapted differential evolution algorithm to solve this problem. Moreover, we illustrate that the performance of collaborative scheduling by the Supply-Hub is superior to separate decision made by each manufacturer and supplier. Furthermore, we also show that the algorithm proposed has good convergence and reliability, which can be applicable to more complicated supply chain environment.

  6. A Collaborative Scheduling Model for the Supply-Hub with Multiple Suppliers and Multiple Manufacturers

    Guo Li

    2014-01-01

    Full Text Available This paper investigates a collaborative scheduling model in the assembly system, wherein multiple suppliers have to deliver their components to the multiple manufacturers under the operation of Supply-Hub. We first develop two different scenarios to examine the impact of Supply-Hub. One is that suppliers and manufacturers make their decisions separately, and the other is that the Supply-Hub makes joint decisions with collaborative scheduling. The results show that our scheduling model with the Supply-Hub is a NP-complete problem, therefore, we propose an auto-adapted differential evolution algorithm to solve this problem. Moreover, we illustrate that the performance of collaborative scheduling by the Supply-Hub is superior to separate decision made by each manufacturer and supplier. Furthermore, we also show that the algorithm proposed has good convergence and reliability, which can be applicable to more complicated supply chain environment.

  7. A Collaborative Scheduling Model for the Supply-Hub with Multiple Suppliers and Multiple Manufacturers

    Lv, Fei; Guan, Xu

    2014-01-01

    This paper investigates a collaborative scheduling model in the assembly system, wherein multiple suppliers have to deliver their components to the multiple manufacturers under the operation of Supply-Hub. We first develop two different scenarios to examine the impact of Supply-Hub. One is that suppliers and manufacturers make their decisions separately, and the other is that the Supply-Hub makes joint decisions with collaborative scheduling. The results show that our scheduling model with the Supply-Hub is a NP-complete problem, therefore, we propose an auto-adapted differential evolution algorithm to solve this problem. Moreover, we illustrate that the performance of collaborative scheduling by the Supply-Hub is superior to separate decision made by each manufacturer and supplier. Furthermore, we also show that the algorithm proposed has good convergence and reliability, which can be applicable to more complicated supply chain environment. PMID:24892104

  8. Algorithm of Dynamic Model Structural Identification of the Multivariable Plant

    Л.М. Блохін

    2004-02-01

    Full Text Available  The new algorithm of dynamic model structural identification of the multivariable stabilized plant with observable and unobservable disturbances in the regular operating  modes is offered in this paper. With the help of the offered algorithm it is possible to define the “perturbed” models of dynamics not only of the plant, but also the dynamics characteristics of observable and unobservable casual disturbances taking into account the absence of correlation between themselves and control inputs with the unobservable perturbations.

  9. Double-multiple streamtube model for Darrieus in turbines

    Paraschivoiu, I.

    1981-01-01

    An analytical model is proposed for calculating the rotor performance and aerodynamic blade forces for Darrieus wind turbines with curved blades. The method of analysis uses a multiple-streamtube model, divided into two parts: one modeling the upstream half-cycle of the rotor and the other, the downstream half-cycle. The upwind and downwind components of the induced velocities at each level of the rotor were obtained using the principle of two actuator disks in tandem. Variation of the induced velocities in the two parts of the rotor produces larger forces in the upstream zone and smaller forces in the downstream zone. Comparisons of the overall rotor performance with previous methods and field test data show the important improvement obtained with the present model. The calculations were made using the computer code CARDAA developed at IREQ. The double-multiple streamtube model presented has two major advantages: it requires a much shorter computer time than the three-dimensional vortex model and is more accurate than multiple-streamtube model in predicting the aerodynamic blade loads.

  10. Diagnosis and Model Based Identification of a Coupling Misalignment

    P. Pennacchi

    2005-01-01

    Full Text Available This paper is focused on the application of two different diagnostic techniques aimed to identify the most important faults in rotating machinery as well as on the simulation and prediction of the frequency response of rotating machines. The application of the two diagnostics techniques, the orbit shape analysis and the model based identification in the frequency domain, is described by means of an experimental case study that concerns a gas turbine-generator unit of a small power plant whose rotor-train was affected by an angular misalignment in a flexible coupling, caused by a wrong machine assembling. The fault type is identified by means of the orbit shape analysis, then the equivalent bending moments, which enable the shaft experimental vibrations to be simulated, have been identified using a model based identification method. These excitations have been used to predict the machine vibrations in a large rotating speed range inside which no monitoring data were available. To the best of the authors' knowledge, this is the first case of identification of coupling misalignment and prediction of the consequent machine behaviour in an actual size rotating machinery. The successful results obtained emphasise the usefulness of integrating common condition monitoring techniques with diagnostic strategies.

  11. Rapid Classification and Identification of Multiple Microorganisms with Accurate Statistical Significance via High-Resolution Tandem Mass Spectrometry.

    Alves, Gelio; Wang, Guanghui; Ogurtsov, Aleksey Y; Drake, Steven K; Gucek, Marjan; Sacks, David B; Yu, Yi-Kuo

    2018-06-05

    Rapid and accurate identification and classification of microorganisms is of paramount importance to public health and safety. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is complicating correct microbial identification even in a simple sample due to the large number of candidates present. To properly untwine candidate microbes in samples containing one or more microbes, one needs to go beyond apparent morphology or simple "fingerprinting"; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptide-centric representations of microbes to better separate them and by augmenting our earlier analysis method that yields accurate statistical significance. Here, we present an updated analysis workflow that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using 226 MS/MS publicly available data files (each containing from 2500 to nearly 100,000 MS/MS spectra) and 4000 additional MS/MS data files, that the updated workflow can correctly identify multiple microbes at the genus and often the species level for samples containing more than one microbe. We have also shown that the proposed workflow computes accurate statistical significances, i.e., E values for identified peptides and unified E values for identified microbes. Our updated analysis workflow MiCId, a freely available software for Microorganism Classification and Identification, is available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html . Graphical Abstract ᅟ.

  12. Multiple commodities in statistical microeconomics: Model and market

    Baaquie, Belal E.; Yu, Miao; Du, Xin

    2016-11-01

    A statistical generalization of microeconomics has been made in Baaquie (2013). In Baaquie et al. (2015), the market behavior of single commodities was analyzed and it was shown that market data provides strong support for the statistical microeconomic description of commodity prices. The case of multiple commodities is studied and a parsimonious generalization of the single commodity model is made for the multiple commodities case. Market data shows that the generalization can accurately model the simultaneous correlation functions of up to four commodities. To accurately model five or more commodities, further terms have to be included in the model. This study shows that the statistical microeconomics approach is a comprehensive and complete formulation of microeconomics, and which is independent to the mainstream formulation of microeconomics.

  13. Risk Prediction Models for Other Cancers or Multiple Sites

    Developing statistical models that estimate the probability of developing other multiple cancers over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  14. An extension of the multiple-trapping model

    Shkilev, V. P.

    2012-01-01

    The hopping charge transport in disordered semiconductors is considered. Using the concept of the transport energy level, macroscopic equations are derived that extend a multiple-trapping model to the case of semiconductors with both energy and spatial disorders. It is shown that, although both types of disorder can cause dispersive transport, the frequency dependence of conductivity is determined exclusively by the spatial disorder.

  15. Selecting Tools to Model Integer and Binomial Multiplication

    Pratt, Sarah Smitherman; Eddy, Colleen M.

    2017-01-01

    Mathematics teachers frequently provide concrete manipulatives to students during instruction; however, the rationale for using certain manipulatives in conjunction with concepts may not be explored. This article focuses on area models that are currently used in classrooms to provide concrete examples of integer and binomial multiplication. The…

  16. Modeling single versus multiple systems in implicit and explicit memory.

    Starns, Jeffrey J; Ratcliff, Roger; McKoon, Gail

    2012-04-01

    It is currently controversial whether priming on implicit tasks and discrimination on explicit recognition tests are supported by a single memory system or by multiple, independent systems. In a Psychological Review article, Berry and colleagues used mathematical modeling to address this question and provide compelling evidence against the independent-systems approach. Copyright © 2012 Elsevier Ltd. All rights reserved.

  17. Green communication: The enabler to multiple business models

    Lindgren, Peter; Clemmensen, Suberia; Taran, Yariv

    2010-01-01

    Companies stand at the forefront of a new business model reality with new potentials - that will change their basic understanding and practice of running their business models radically. One of the drivers to this change is green communication, its strong relation to green business models and its...... possibility to enable lower energy consumption. This paper shows how green communication enables innovation of green business models and multiple business models running simultaneously in different markets to different customers.......Companies stand at the forefront of a new business model reality with new potentials - that will change their basic understanding and practice of running their business models radically. One of the drivers to this change is green communication, its strong relation to green business models and its...

  18. Infinite Multiple Membership Relational Modeling for Complex Networks

    Mørup, Morten; Schmidt, Mikkel Nørgaard; Hansen, Lars Kai

    Learning latent structure in complex networks has become an important problem fueled by many types of networked data originating from practically all fields of science. In this paper, we propose a new non-parametric Bayesian multiplemembership latent feature model for networks. Contrary to existing...... multiplemembership models that scale quadratically in the number of vertices the proposedmodel scales linearly in the number of links admittingmultiple-membership analysis in large scale networks. We demonstrate a connection between the single membership relational model and multiple membership models and show...

  19. Identification model of gifted students in secondary education

    Carmen Ferrándiz

    2010-04-01

    Full Text Available The aim of this article is to describe the identification and assessment procedure to identify high ability secondary school students in the Spanish region of Murcia. In the screening process questionaires addressed to parents, teachers, and pupils nad based on the Multiple Intelligences Theory were used. In the identification process two other instruments were used: a the Differential Aptitude Test (DAT aimed to assess the following areas: reasoning, verbal abilities, numerical and abstract reasoning, spatial aptitude, mechanical reasoning, attention and perceptive aptitudes, and b the TTCT (Torrance Test of Creative Thinking in order to assess the main abilities of creativity (fluency, flexibility, originality and elaboration. These two assessment tools will allow us to distinguish gifted from talented (Castelló and Batlle, 1998. In a third stage, the socio-emotional characteristics of the identified students are analysed using: c the BFQ-NA whose aim is to assess the personality dimensions (openness, conscientiousness, extraversion; agreeableness and neuroticism, and d emotional intelligence questionnaires (EQ-i:YV and EQ-i:YV-O Barón and Parker, 2000. 565 took part in this research. The students were aged 11-18 (M= 14.6 and SD= 1.08 and attended high schools of Compulsory Secondary Education (ESO of the Murcia Region. The results showed different profiles of gifted and talented stduents. The cognitive-emotional complexity of these exceptional students is discussed.

  20. Identification of suitable areas for the occurrence of Rift Valley fever outbreaks in Spain using a multiple criteria decision framework.

    Sánchez-Vizcaíno, Fernando; Martínez-López, Beatriz; Sánchez-Vizcaíno, José Manuel

    2013-07-26

    Rift Valley fever (RVF) is a mosquito-borne viral disease that may produce a considerable impact on the economy in affected countries. In the last decades, the geographic distribution of RVF virus has increased including most of the countries in Africa, Arabia Saudi and Yemen. This situation has raised the concerns regarding its potential introduction in the European Union (EU) countries where the high number of susceptible species and competent vectors may contribute to the spread of the disease and challenge its rapid control. Thus, the identification of the areas and time periods with highest suitability for RVF outbreak occurrence would be useful for improving the early detection and rapid response of the disease into free countries. The objective of this study was to identify suitable areas for the occurrence of RVF outbreaks in Spain using a multiple criteria decision making model based on weighted linear combination of factors in geographical information systems (GIS). To the best of the author's knowledge this is the first comprehensive GIS-based framework that provides risk maps for RVF suitability in an EU country. Spanish zones with the highest suitability for RVF were concentrated in the regions of Extremadura, south-western Castile and Leon, eastern Galicia, Asturias, Cantabria, Basque Country, northern-central and southern region of Andalusia and in the Balearic Islands. October and May were the most suitable months for RVF outbreak occurrence. Methods and results presented here may be useful to target risk-based surveillance strategies and to more cost-effectively control potential RVFV incursions into Spain. Copyright © 2013 Elsevier B.V. All rights reserved.

  1. Vehicle coordinated transportation dispatching model base on multiple crisis locations

    Tian, Ran; Li, Shanwei; Yang, Guoying

    2018-05-01

    Many disastrous events are often caused after unconventional emergencies occur, and the requirements of disasters are often different. It is difficult for a single emergency resource center to satisfy such requirements at the same time. Therefore, how to coordinate the emergency resources stored by multiple emergency resource centers to various disaster sites requires the coordinated transportation of emergency vehicles. In this paper, according to the problem of emergency logistics coordination scheduling, based on the related constraints of emergency logistics transportation, an emergency resource scheduling model based on multiple disasters is established.

  2. Multiple Surrogate Modeling for Wire-Wrapped Fuel Assembly Optimization

    Raza, Wasim; Kim, Kwang-Yong

    2007-01-01

    In this work, shape optimization of seven pin wire wrapped fuel assembly has been carried out in conjunction with RANS analysis in order to evaluate the performances of surrogate models. Previously, Ahmad and Kim performed the flow and heat transfer analysis based on the three-dimensional RANS analysis. But numerical optimization has not been applied to the design of wire-wrapped fuel assembly, yet. Surrogate models are being widely used in multidisciplinary optimization. Queipo et al. reviewed various surrogates based models used in aerospace applications. Goel et al. developed weighted average surrogate model based on response surface approximation (RSA), radial basis neural network (RBNN) and Krigging (KRG) models. In addition to the three basic models, RSA, RBNN and KRG, the multiple surrogate model, PBA also has been employed. Two geometric design variables and a multi-objective function with a weighting factor have been considered for this problem

  3. Groundwater Pollution Source Identification using Linked ANN-Optimization Model

    Ayaz, Md; Srivastava, Rajesh; Jain, Ashu

    2014-05-01

    Groundwater is the principal source of drinking water in several parts of the world. Contamination of groundwater has become a serious health and environmental problem today. Human activities including industrial and agricultural activities are generally responsible for this contamination. Identification of groundwater pollution source is a major step in groundwater pollution remediation. Complete knowledge of pollution source in terms of its source characteristics is essential to adopt an effective remediation strategy. Groundwater pollution source is said to be identified completely when the source characteristics - location, strength and release period - are known. Identification of unknown groundwater pollution source is an ill-posed inverse problem. It becomes more difficult for real field conditions, when the lag time between the first reading at observation well and the time at which the source becomes active is not known. We developed a linked ANN-Optimization model for complete identification of an unknown groundwater pollution source. The model comprises two parts- an optimization model and an ANN model. Decision variables of linked ANN-Optimization model contain source location and release period of pollution source. An objective function is formulated using the spatial and temporal data of observed and simulated concentrations, and then minimized to identify the pollution source parameters. In the formulation of the objective function, we require the lag time which is not known. An ANN model with one hidden layer is trained using Levenberg-Marquardt algorithm to find the lag time. Different combinations of source locations and release periods are used as inputs and lag time is obtained as the output. Performance of the proposed model is evaluated for two and three dimensional case with error-free and erroneous data. Erroneous data was generated by adding uniformly distributed random error (error level 0-10%) to the analytically computed concentration

  4. A Survey of Modelling and Identification of Quadrotor Robot

    Xiaodong Zhang

    2014-01-01

    Full Text Available A quadrotor is a rotorcraft capable of hover, forward flight, and VTOL and is emerging as a fundamental research and application platform at present with flexibility, adaptability, and ease of construction. Since a quadrotor is basically considered an unstable system with the characteristics of dynamics such as being intensively nonlinear, multivariable, strongly coupled, and underactuated, a precise and practical model is critical to control the vehicle which seems to be simple to operate. As a rotorcraft, the dynamics of a quadrotor is mainly dominated by the complicated aerodynamic effects of the rotors. This paper gives a tutorial of the platform configuration, methodology of modeling, comprehensive nonlinear model, the aerodynamic effects, and model identification for a quadrotor.

  5. A model for diagnosing and explaining multiple disorders.

    Jamieson, P W

    1991-08-01

    The ability to diagnose multiple interacting disorders and explain them in a coherent causal framework has only partially been achieved in medical expert systems. This paper proposes a causal model for diagnosing and explaining multiple disorders whose key elements are: physician-directed hypotheses generation, object-oriented knowledge representation, and novel explanation heuristics. The heuristics modify and link the explanations to make the physician aware of diagnostic complexities. A computer program incorporating the model currently is in use for diagnosing peripheral nerve and muscle disorders. The program successfully diagnoses and explains interactions between diseases in terms of underlying pathophysiologic concepts. The model offers a new architecture for medical domains where reasoning from first principles is difficult but explanation of disease interactions is crucial for the system's operation.

  6. The clandestine multiple graves in Malaysia: The first mass identification operation of human skeletal remains.

    Mohd Noor, Mohd Suhani; Khoo, Lay See; Zamaliana Alias, Wan Zafirah; Hasmi, Ahmad Hafizam; Ibrahim, Mohamad Azaini; Mahmood, Mohd Shah

    2017-09-01

    The first ever mass identification operation of skeletal remains conducted for the clandestine graves in Malaysia consisted of 165 individuals unearthed from 28 human trafficking transit camps located in Wang Kelian, along the Thai-Malaysia border. A DVI response was triggered in which expert teams comprising of pathologists, anthropologists, odontologists, radiologists and DNA experts were gathered at the identified operation centre. The Department of Forensic Medicine, Hospital Sultanah Bahiyah, Alor Star, Kedah, located approximately 75km away from Wang Kelian, was temporarily converted into a victim identification centre (VIC) as it is the nearest available forensic facility to the mass grave site. The mortuary operation was conducted over a period of 3 months from June to September 2015, and was divided into two phases; phase 1 involving the postmortem examination of the remains of 116 suspected individuals and for phase 2 the remains of 49 suspected individuals. The fact that the graves were of unknown individuals afforded the mass identification operation a sufficient duration of 2 weeks as preparatory phase enabling procedurals and daily victim identification workflow to be established, and the setting up of a temporary body storage for the designated mortuary. The temporary body storage has proven to be a significant factor in enabling the successful conclusion of the VIC operation to the final phase of temporary controlled burials. Recognition from two international observers, Mr. Andréas Patiño Umaña, from the International Committee of Red Cross (ICRC) and Prof. Noel Woodford from Victoria Institute of Forensic Medicine (VIFM) had proven the mortuary operation was in compliance to the international quality and standards. The overall victim identification and mortuary operation identified a number of significant challenges, in particular the management of commingled human remains as well as the compilation of postmortem data in the absence of

  7. MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method.

    Tuta, Jure; Juric, Matjaz B

    2018-03-24

    This paper presents MFAM (Multiple Frequency Adaptive Model-based localization method), a novel model-based indoor localization method that is capable of using multiple wireless signal frequencies simultaneously. It utilizes indoor architectural model and physical properties of wireless signal propagation through objects and space. The motivation for developing multiple frequency localization method lies in the future Wi-Fi standards (e.g., 802.11ah) and the growing number of various wireless signals present in the buildings (e.g., Wi-Fi, Bluetooth, ZigBee, etc.). Current indoor localization methods mostly rely on a single wireless signal type and often require many devices to achieve the necessary accuracy. MFAM utilizes multiple wireless signal types and improves the localization accuracy over the usage of a single frequency. It continuously monitors signal propagation through space and adapts the model according to the changes indoors. Using multiple signal sources lowers the required number of access points for a specific signal type while utilizing signals, already present in the indoors. Due to the unavailability of the 802.11ah hardware, we have evaluated proposed method with similar signals; we have used 2.4 GHz Wi-Fi and 868 MHz HomeMatic home automation signals. We have performed the evaluation in a modern two-bedroom apartment and measured mean localization error 2.0 to 2.3 m and median error of 2.0 to 2.2 m. Based on our evaluation results, using two different signals improves the localization accuracy by 18% in comparison to 2.4 GHz Wi-Fi-only approach. Additional signals would improve the accuracy even further. We have shown that MFAM provides better accuracy than competing methods, while having several advantages for real-world usage.

  8. MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method

    Jure Tuta

    2018-03-01

    Full Text Available This paper presents MFAM (Multiple Frequency Adaptive Model-based localization method, a novel model-based indoor localization method that is capable of using multiple wireless signal frequencies simultaneously. It utilizes indoor architectural model and physical properties of wireless signal propagation through objects and space. The motivation for developing multiple frequency localization method lies in the future Wi-Fi standards (e.g., 802.11ah and the growing number of various wireless signals present in the buildings (e.g., Wi-Fi, Bluetooth, ZigBee, etc.. Current indoor localization methods mostly rely on a single wireless signal type and often require many devices to achieve the necessary accuracy. MFAM utilizes multiple wireless signal types and improves the localization accuracy over the usage of a single frequency. It continuously monitors signal propagation through space and adapts the model according to the changes indoors. Using multiple signal sources lowers the required number of access points for a specific signal type while utilizing signals, already present in the indoors. Due to the unavailability of the 802.11ah hardware, we have evaluated proposed method with similar signals; we have used 2.4 GHz Wi-Fi and 868 MHz HomeMatic home automation signals. We have performed the evaluation in a modern two-bedroom apartment and measured mean localization error 2.0 to 2.3 m and median error of 2.0 to 2.2 m. Based on our evaluation results, using two different signals improves the localization accuracy by 18% in comparison to 2.4 GHz Wi-Fi-only approach. Additional signals would improve the accuracy even further. We have shown that MFAM provides better accuracy than competing methods, while having several advantages for real-world usage.

  9. Influences of multiple low-n modes on n=1 resistive wall mode identification and feedback control

    In, Y.; Kim, J.; Kim, J. S.; Garofalo, A. M.; Jackson, G. L.; La Haye, R. J.; Strait, E. J.; Okabayashi, M.; Reimerdes, H.

    2008-01-01

    It is well known in theory that even after the n=1 resistive wall mode (RWM) is suppressed, the other low-n modes, such as n=2 or 3, can appear sequentially, as β increases. In recent DIII-D experiments [J. L. Luxon, Nucl. Fusion 42, 614 (2002)], we found such an example that supports the theoretical prediction: while the n=1 mode was suppressed, an n=3 mode grew dominant, leading to a β collapse. The n=1 RWM suppression was likely due to a combination of rotational stabilization and n=1 RWM feedback. The multiple RWM identification was performed using an expanded matched filter, where n=1 and n=3 RWM basis vectors are simultaneously considered. Taking advantage of the expanded matched filter, we found that an n=3 mode following an edge-localized-mode burst grew almost linearly for several milliseconds without being hindered. This n=3 mode appeared responsible for the β collapse (down to the n=3 no-wall limit), as well as for a drop in toroidal rotation. A preliminary analysis suggests that the identity of the n=3 mode could be related to the n=3 RWM (possibly the first observation in tokamak experiments), while the impact of the n=3 mode was not as destructive as that of n=1 RWM. A numerical postprocessing of Mirnov probes showed that the n=2 mode was also unstable, consistent with the theoretical prediction. In practice, since the presence of an n=3 mode can interfere with the existing n=1 RWM identification, multiple low-n mode identification is deemed essential not only to detect n>1 mode, but also to provide accurate n=1 RWM identification and feedback control.

  10. Identification of gene expression patterns crucially involved in experimental autoimmune encephalomyelitis and multiple sclerosis

    Martin M. Herrmann

    2016-10-01

    Full Text Available After encounter with a central nervous system (CNS-derived autoantigen, lymphocytes leave the lymph nodes and enter the CNS. This event leads only rarely to subsequent tissue damage. Genes relevant to CNS pathology after cell infiltration are largely undefined. Myelin-oligodendrocyte-glycoprotein (MOG-induced experimental autoimmune encephalomyelitis (EAE is an animal model of multiple sclerosis (MS, a chronic autoimmune disease of the CNS that results in disability. To assess genes that are involved in encephalitogenicity and subsequent tissue damage mediated by CNS-infiltrating cells, we performed a DNA microarray analysis from cells derived from lymph nodes and eluted from CNS in LEW.1AV1 (RT1av1 rats immunized with MOG 91-108. The data was compared to immunizations with adjuvant alone or naive rats and to immunizations with the immunogenic but not encephalitogenic MOG 73-90 peptide. Here, we show involvement of Cd38, Cxcr4 and Akt and confirm these findings by the use of Cd38-knockout (B6.129P2-Cd38tm1Lnd/J mice, S1P-receptor modulation during EAE and quantitative expression analysis in individuals with MS. The hereby-defined underlying pathways indicate cellular activation and migration pathways mediated by G-protein-coupled receptors as crucial events in CNS tissue damage. These pathways can be further explored for novel therapeutic interventions.

  11. A method for model identification and parameter estimation

    Bambach, M; Heinkenschloss, M; Herty, M

    2013-01-01

    We propose and analyze a new method for the identification of a parameter-dependent model that best describes a given system. This problem arises, for example, in the mathematical modeling of material behavior where several competing constitutive equations are available to describe a given material. In this case, the models are differential equations that arise from the different constitutive equations, and the unknown parameters are coefficients in the constitutive equations. One has to determine the best-suited constitutive equations for a given material and application from experiments. We assume that the true model is one of the N possible parameter-dependent models. To identify the correct model and the corresponding parameters, we can perform experiments, where for each experiment we prescribe an input to the system and observe a part of the system state. Our approach consists of two stages. In the first stage, for each pair of models we determine the experiment, i.e. system input and observation, that best differentiates between the two models, and measure the distance between the two models. Then we conduct N(N − 1) or, depending on the approach taken, N(N − 1)/2 experiments and use the result of the experiments as well as the previously computed model distances to determine the true model. We provide sufficient conditions on the model distances and measurement errors which guarantee that our approach identifies the correct model. Given the model, we identify the corresponding model parameters in the second stage. The problem in the second stage is a standard parameter estimation problem and we use a method suitable for the given application. We illustrate our approach on three examples, including one where the models are elliptic partial differential equations with different parameterized right-hand sides and an example where we identify the constitutive equation in a problem from computational viscoplasticity. (paper)

  12. Dynamics of Practical Premixed Flames, Part I: Model Structure and Identification

    A. Huber

    2009-06-01

    Full Text Available For the analysis of thermoacoustic instabilities it is most important to determine the dynamic flame response to acoustic disturbances. Premixed flames are often modelled as single-input single-output system, where the “output” (the overall rate of heat release responds to a single “input” variable (often the velocity at the exit of the burner nozzle. However, for practical premixed flames, where perturbations of pressure or velocity at the fuel injector will modulate the fuel equivalence ratio, the heat release rate will respond to fluctuations of equivalence ratio as well as nozzle mass flow rate. In this case, a multiple-input, single-output (MISO model structure for the flame is appropriate. Such a model structure is developed in the present paper. Staged fuel injection as well as fuel line impedances can be taken into account, the integration with low-order or finite-element based models for stability analysis is straightforward. In order to determine unit impulse and frequency response functions for such a model structure, an identification scheme based on unsteady CFD calculation with broadband excitation followed by correlation analysis is proposed and validated successfully. Identification of MISO model coefficients is a challenging task, especially in the presence of noise. Therefore criteria are introduced which allow to ascertain a posteriori how well the identified model represents the true system dynamics. Using these criteria, it is investigated how excitation signal type, time series length and signal-to-noise ratio influence the results of the identification process. Consequences for passive design strategies based on multi-stage fuel injection and experimental work on practical premixed flame dynamics are discussed.

  13. Identification of walking human model using agent-based modelling

    Shahabpoor, Erfan; Pavic, Aleksandar; Racic, Vitomir

    2018-03-01

    The interaction of walking people with large vibrating structures, such as footbridges and floors, in the vertical direction is an important yet challenging phenomenon to describe mathematically. Several different models have been proposed in the literature to simulate interaction of stationary people with vibrating structures. However, the research on moving (walking) human models, explicitly identified for vibration serviceability assessment of civil structures, is still sparse. In this study, the results of a comprehensive set of FRF-based modal tests were used, in which, over a hundred test subjects walked in different group sizes and walking patterns on a test structure. An agent-based model was used to simulate discrete traffic-structure interactions. The occupied structure modal parameters found in tests were used to identify the parameters of the walking individual's single-degree-of-freedom (SDOF) mass-spring-damper model using 'reverse engineering' methodology. The analysis of the results suggested that the normal distribution with the average of μ = 2.85Hz and standard deviation of σ = 0.34Hz can describe human SDOF model natural frequency. Similarly, the normal distribution with μ = 0.295 and σ = 0.047 can describe the human model damping ratio. Compared to the previous studies, the agent-based modelling methodology proposed in this paper offers significant flexibility in simulating multi-pedestrian walking traffics, external forces and simulating different mechanisms of human-structure and human-environment interaction at the same time.

  14. Line impedance estimation using model based identification technique

    Ciobotaru, Mihai; Agelidis, Vassilios; Teodorescu, Remus

    2011-01-01

    The estimation of the line impedance can be used by the control of numerous grid-connected systems, such as active filters, islanding detection techniques, non-linear current controllers, detection of the on/off grid operation mode. Therefore, estimating the line impedance can add extra functions...... into the operation of the grid-connected power converters. This paper describes a quasi passive method for estimating the line impedance of the distribution electricity network. The method uses the model based identification technique to obtain the resistive and inductive parts of the line impedance. The quasi...

  15. Parameter Identification for Nonlinear Circuit Models of Power BAW Resonator

    CONSTANTINESCU, F.

    2011-02-01

    Full Text Available The large signal operation of the bulk acoustic wave (BAW resonators is characterized by the amplitude-frequency effect and the intermodulation effect. The measurement of these effects, together with that of the small signal frequency characteristic, are used in this paper for the parameter identification of the nonlinear circuit models developed previously by authors. As the resonator has been connected to the measurement bench by wire bonding, the parasitic elements of this connection have been taken into account, being estimated solving some electrical and magnetic field problems.

  16. Text-Independent Speaker Identification Using the Histogram Transform Model

    Ma, Zhanyu; Yu, Hong; Tan, Zheng-Hua

    2016-01-01

    In this paper, we propose a novel probabilistic method for the task of text-independent speaker identification (SI). In order to capture the dynamic information during SI, we design a super-MFCCs features by cascading three neighboring Mel-frequency Cepstral coefficients (MFCCs) frames together....... These super-MFCC vectors are utilized for probabilistic model training such that the speaker’s characteristics can be sufficiently captured. The probability density function (PDF) of the aforementioned super-MFCCs features is estimated by the recently proposed histogram transform (HT) method. To recedes...

  17. Model identification methodology for fluid-based inerters

    Liu, Xiaofu; Jiang, Jason Zheng; Titurus, Branislav; Harrison, Andrew

    2018-06-01

    Inerter is the mechanical dual of the capacitor via the force-current analogy. It has the property that the force across the terminals is proportional to their relative acceleration. Compared with flywheel-based inerters, fluid-based forms have advantages of improved durability, inherent damping and simplicity of design. In order to improve the understanding of the physical behaviour of this fluid-based device, especially caused by the hydraulic resistance and inertial effects in the external tube, this work proposes a comprehensive model identification methodology. Firstly, a modelling procedure is established, which allows the topological arrangement of the mechanical networks to be obtained by mapping the damping, inertance and stiffness effects directly to their respective hydraulic counterparts. Secondly, an experimental sequence is followed, which separates the identification of friction, stiffness and various damping effects. Furthermore, an experimental set-up is introduced, where two pressure gauges are used to accurately measure the pressure drop across the external tube. The theoretical models with improved confidence are obtained using the proposed methodology for a helical-tube fluid inerter prototype. The sources of remaining discrepancies are further analysed.

  18. Identification of swine influenza virus epitopes and analysis of multiple specificities expressed by cytotoxic T cell subsets

    Pedersen, Lasse Eggers; Breum, Solvej Østergaard; Riber, Ulla

    2014-01-01

    Background: Major histocompatibility complex (MHC) class I peptide binding and presentation are essential for antigen-specific activation of cytotoxic T lymphocytes (CTLs) and swine MHC class I molecules, also termed swine leukocyte antigens (SLA), thus play a crucial role in the process that leads...... to elimination of viruses such as swine influenza virus (SwIV). This study describes the identification of SLA-presented peptide epitopes that are targets for a swine CTL response, and further analyses multiple specificities expressed by SwIV activated CTL subsets. Findings: Four SwIV derived peptides were...

  19. A PDP model of the simultaneous perception of multiple objects

    Henderson, Cynthia M.; McClelland, James L.

    2011-06-01

    Illusory conjunctions in normal and simultanagnosic subjects are two instances where the visual features of multiple objects are incorrectly 'bound' together. A connectionist model explores how multiple objects could be perceived correctly in normal subjects given sufficient time, but could give rise to illusory conjunctions with damage or time pressure. In this model, perception of two objects benefits from lateral connections between hidden layers modelling aspects of the ventral and dorsal visual pathways. As with simultanagnosia, simulations of dorsal lesions impair multi-object recognition. In contrast, a large ventral lesion has minimal effect on dorsal functioning, akin to dissociations between simple object manipulation (retained in visual form agnosia and semantic dementia) and object discrimination (impaired in these disorders) [Hodges, J.R., Bozeat, S., Lambon Ralph, M.A., Patterson, K., and Spatt, J. (2000), 'The Role of Conceptual Knowledge: Evidence from Semantic Dementia', Brain, 123, 1913-1925; Milner, A.D., and Goodale, M.A. (2006), The Visual Brain in Action (2nd ed.), New York: Oxford]. It is hoped that the functioning of this model might suggest potential processes underlying dorsal and ventral contributions to the correct perception of multiple objects.

  20. Supersymmetric U(1)' model with multiple dark matters

    Hur, Taeil; Lee, Hye-Sung; Nasri, Salah

    2008-01-01

    We consider a scenario where a supersymmetric model has multiple dark matter particles. Adding a U(1) ' gauge symmetry is a well-motivated extension of the minimal supersymmetric standard model (MSSM). It can cure the problems of the MSSM such as the μ problem or the proton decay problem with high-dimensional lepton number and baryon number violating operators which R parity allows. An extra parity (U parity) may arise as a residual discrete symmetry after U(1) ' gauge symmetry is spontaneously broken. The lightest U-parity particle (LUP) is stable under the new parity becoming a new dark matter candidate. Up to three massive particles can be stable in the presence of the R parity and the U parity. We numerically illustrate that multiple stable particles in our model can satisfy both constraints from the relic density and the direct detection, thus providing a specific scenario where a supersymmetric model has well-motivated multiple dark matters consistent with experimental constraints. The scenario provides new possibilities in the present and upcoming dark matter searches in the direct detection and collider experiments

  1. Identification of grid model parameters using synchrophasor measurements

    Boicea, Valentin; Albu, Mihaela [Politehnica University of Bucharest (Romania)

    2012-07-01

    Presently a critical element of the energy networks is represented by the active distribution grids, where generation intermittency and controllable loads contribute to a stochastic varability of the quantities characterizing the grid operation. The capability of controlling the electrical energy transfer is also limited by the incomplete knowledge of the detailed electrical model of each of the grid components. Asset management in distribution grids has to consider dynamic loads, while high loading of network sections might already have degraded some of the assets. Moreover, in case of functional microgrids, all elements need to be modelled accurately and an appropriate measurement layer enabling online control needs to be deployed. In this paper a method for online identification of the actual parameter values in grid electrical models is proposed. Laboratory results validating the proposed method are presented. (orig.)

  2. Identification of neutral biochemical network models from time series data

    Maia Marco

    2009-05-01

    Full Text Available Abstract Background The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. Results In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. Conclusion The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.

  3. Identification of neutral biochemical network models from time series data.

    Vilela, Marco; Vinga, Susana; Maia, Marco A Grivet Mattoso; Voit, Eberhard O; Almeida, Jonas S

    2009-05-05

    The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.

  4. Nonlinear State Space Modeling and System Identification for Electrohydraulic Control

    Jun Yan

    2013-01-01

    Full Text Available The paper deals with nonlinear modeling and identification of an electrohydraulic control system for improving its tracking performance. We build the nonlinear state space model for analyzing the highly nonlinear system and then develop a Hammerstein-Wiener (H-W model which consists of a static input nonlinear block with two-segment polynomial nonlinearities, a linear time-invariant dynamic block, and a static output nonlinear block with single polynomial nonlinearity to describe it. We simplify the H-W model into a linear-in-parameters structure by using the key term separation principle and then use a modified recursive least square method with iterative estimation of internal variables to identify all the unknown parameters simultaneously. It is found that the proposed H-W model approximates the actual system better than the independent Hammerstein, Wiener, and ARX models. The prediction error of the H-W model is about 13%, 54%, and 58% less than the Hammerstein, Wiener, and ARX models, respectively.

  5. Challenges in LCA modelling of multiple loops for aluminium cans

    Niero, Monia; Olsen, Stig Irving

    considered the case of closed-loop recycling for aluminium cans, where body and lid are different alloys, and discussed the abovementioned challenge. The Life Cycle Inventory (LCI) modelling of aluminium processes is traditionally based on a pure aluminium flow, therefore neglecting the presence of alloying...... elements. We included the effect of alloying elements on the LCA modelling of aluminium can recycling. First, we performed a mass balance of the main alloying elements (Mn, Fe, Si, Cu) in aluminium can recycling at increasing levels of recycling rate. The analysis distinguished between different aluminium...... packaging scrap sources (i.e. used beverage can and mixed aluminium packaging) to understand the limiting factors for multiple loop aluminium can recycling. Secondly, we performed a comparative LCA of aluminium can production and recycling in multiple loops considering the two aluminium packaging scrap...

  6. Mixed models, linear dependency, and identification in age-period-cohort models.

    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.

  7. Model-based monitoring of rotors with multiple coexisting faults

    Rossner, Markus

    2015-01-01

    Monitoring systems are applied to many rotors, but only few monitoring systems can separate coexisting errors and identify their quantity. This research project solves this problem using a combination of signal-based and model-based monitoring. The signal-based part performs a pre-selection of possible errors; these errors are further separated with model-based methods. This approach is demonstrated for the errors unbalance, bow, stator-fixed misalignment, rotor-fixed misalignment and roundness errors. For the model-based part, unambiguous error definitions and models are set up. The Ritz approach reduces the model order and therefore speeds up the diagnosis. Identification algorithms are developed for the different rotor faults. Hereto, reliable damage indicators and proper sub steps of the diagnosis have to be defined. For several monitoring problems, measuring both deflection and bearing force is very useful. The monitoring system is verified by experiments on an academic rotor test rig. The interpretation of the measurements requires much knowledge concerning the dynamics of the rotor. Due to the model-based approach, the system can separate errors with similar signal patterns and identify bow and roundness error online at operation speed. [de

  8. Dealing with Multiple Solutions in Structural Vector Autoregressive Models.

    Beltz, Adriene M; Molenaar, Peter C M

    2016-01-01

    Structural vector autoregressive models (VARs) hold great potential for psychological science, particularly for time series data analysis. They capture the magnitude, direction of influence, and temporal (lagged and contemporaneous) nature of relations among variables. Unified structural equation modeling (uSEM) is an optimal structural VAR instantiation, according to large-scale simulation studies, and it is implemented within an SEM framework. However, little is known about the uniqueness of uSEM results. Thus, the goal of this study was to investigate whether multiple solutions result from uSEM analysis and, if so, to demonstrate ways to select an optimal solution. This was accomplished with two simulated data sets, an empirical data set concerning children's dyadic play, and modifications to the group iterative multiple model estimation (GIMME) program, which implements uSEMs with group- and individual-level relations in a data-driven manner. Results revealed multiple solutions when there were large contemporaneous relations among variables. Results also verified several ways to select the correct solution when the complete solution set was generated, such as the use of cross-validation, maximum standardized residuals, and information criteria. This work has immediate and direct implications for the analysis of time series data and for the inferences drawn from those data concerning human behavior.

  9. Dynamic biometric identification from multiple views using the GLBP-TOP method.

    Wang, Yu; Shen, Xuanjing; Chen, Haipeng; Zhai, Yujie

    2014-01-01

    To realize effective and rapid dynamic biometric identification with low computational complexity, a video-based facial texture program that extracts local binary patterns from three orthogonal planes in the frequency domain of the Gabor transform (GLBP-TOP) was proposed. Firstly, each normalized face was transformed by Gabor wavelet to get the enhanced Gabor magnitude map, and then the LBP-TOP operator was applied to the maps to extract video texture. Finally, weighted Chi square statistics based on the Fisher Criterion were used to realize the identification. The proposed algorithm was proved effective through the biometric experiments using the Honda/UCSD database, and was robust against changes of illumination and expressions.

  10. Control Valve Stiction Identification, Modelling, Quantification and Control - A Review

    Srinivasan Arumugam

    2011-09-01

    Full Text Available Most of the processes found in process industries exhibit undesirable nonlinearity due to backlash, saturation, hysteresis, stiction (friction, dead-zone and stuck-fault existing in control valves. The control valve is the actuator for most process control loops and, as the only moving part in the loop, its function is to implement the control action. If the control valve malfunctions, the performance of the control loop is likely to deteriorate, no matter how good the controller is. Commonly encountered control valve problems include nonlinear responses to the demand signal caused by effects such as stiction, dead-band or saturation. Because of these problems, the control loop may be oscillatory, which in turn may cause oscillations in many process variables causing a range of operational problems including increased valve wear. Understanding nonlinear behaviour of control valves in order to maintain the quality of the end products in the industry, this review article surveys the identification, modelling, estimation and design of dynamic models of stiction nonlinearity and providing appropriate controller to obtain optimum responses of the process. The primary objective of this work is to present state-of-art-review of common nonlinear problems associated with mechanical and chemical processes for encouraging researchers, practicing engineers working in this field, so that readers can invent their goals for future research work on nonlinear systems identification and control.

  11. Pescara benchmark: overview of modelling, testing and identification

    Bellino, A; Garibaldi, L; Marchesiello, S; Brancaleoni, F; Gabriele, S; Spina, D; Bregant, L; Carminelli, A; Catania, G; Sorrentino, S; Di Evangelista, A; Valente, C; Zuccarino, L

    2011-01-01

    The 'Pescara benchmark' is part of the national research project 'BriViDi' (BRIdge VIbrations and DIagnosis) supported by the Italian Ministero dell'Universita e Ricerca. The project is aimed at developing an integrated methodology for the structural health evaluation of railway r/c, p/c bridges. The methodology should provide for applicability in operating conditions, easy data acquisition through common industrial instrumentation, robustness and reliability against structural and environmental uncertainties. The Pescara benchmark consisted in lab tests to get a consistent and large experimental data base and subsequent data processing. Special tests were devised to simulate the train transit effects in actual field conditions. Prestressed concrete beams of current industrial production both sound and damaged at various severity corrosion levels were tested. The results were collected either in a deterministic setting and in a form suitable to deal with experimental uncertainties. Damage identification was split in two approaches: with or without a reference model. In the first case f.e. models were used in conjunction with non conventional updating techniques. In the second case, specialized output-only identification techniques capable to deal with time-variant and possibly non linear systems were developed. The lab tests allowed validating the above approaches and the performances of classical modal based damage indicators.

  12. Automated identification and quantification of glycerophospholipid molecular species by multiple precursor ion scanning

    Ejsing, Christer S.; Duchoslav, Eva; Sampaio, Julio

    2006-01-01

    We report a method for the identification and quantification of glycerophospholipid molecular species that is based on the simultaneous automated acquisition and processing of 41 precursor ion spectra, specific for acyl anions of common fatty acids moieties and several lipid class-specific fragment...... of glycerophospholipids. The automated analysis of total lipid extracts was powered by a robotic nanoflow ion source and produced currently the most detailed description of the glycerophospholipidome....

  13. Combining Multiple Features for Text-Independent Writer Identification and Verification

    Bulacu , Marius; Schomaker , Lambert

    2006-01-01

    http://www.suvisoft.com; In recent years, we proposed a number of new and very effective features for automatic writer identification and verification. They are probability distribution functions (PDFs) extracted from the handwriting images and characterize writer individuality independently of the textual content of the written samples. In this paper, we perform an extensive analysis of feature combinations. In our fusion scheme, the final unique distance between two handwritten samples is c...

  14. Customizable PCR-microplate array for differential identification of multiple pathogens.

    Woubit, Abdela; Yehualaeshet, Teshome; Roberts, Sherrelle; Graham, Martha; Kim, Moonil; Samuel, Temesgen

    2013-11-01

    Customizable PCR-microplate arrays were developed for the rapid identification of Salmonella Typhimurium, Salmonella Saintpaul, Salmonella Typhi, Shigella dysenteriae, Escherichia coli O157:H7, Francisella tularensis subsp. tularensis, Francisella tularensis subsp. novicida, Vibrio cholerae, Vibrio parahaemolyticus, Yersinia pestis, and Yersinia pseudotuberculosis. Previously, we identified highly specific primers targeting each of these pathogens. Here, we report the development of customizable PCR-microplate arrays for simultaneous identification of the pathogens using the primers identified. A mixed aliquot of genomic DNA from 38 strains was used to validate three PCR-microplate array formats. Identical PCR conditions were used to run all the samples on the three formats. Specific amplifications were obtained on all three custom plates. In preliminary tests performed to evaluate the sensitivity of these assays in samples inoculated in the laboratory with Salmonella Typhimurium, amplifications were obtained from 1 g of beef hot dog inoculated at as low as 9 CFU/ml or from milk inoculated at as low as 78 CFU/ml. Such microplate arrays could be valuable tools for initial identification or secondary confirmation of contamination by these pathogens.

  15. Automatic Generation of 3D Building Models with Multiple Roofs

    Kenichi Sugihara; Yoshitugu Hayashi

    2008-01-01

    Based on building footprints (building polygons) on digital maps, we are proposing the GIS and CG integrated system that automatically generates 3D building models with multiple roofs. Most building polygons' edges meet at right angles (orthogonal polygon). The integrated system partitions orthogonal building polygons into a set of rectangles and places rectangular roofs and box-shaped building bodies on these rectangles. In order to partition an orthogonal polygon, we proposed a useful polygon expression in deciding from which vertex a dividing line is drawn. In this paper, we propose a new scheme for partitioning building polygons and show the process of creating 3D roof models.

  16. A tactical supply chain planning model with multiple flexibility options

    Esmaeilikia, Masoud; Fahimnia, Behnam; Sarkis, Joeseph

    2016-01-01

    Supply chain flexibility is widely recognized as an approach to manage uncertainty. Uncertainty in the supply chain may arise from a number of sources such as demand and supply interruptions and lead time variability. A tactical supply chain planning model with multiple flexibility options...... incorporated in sourcing, manufacturing and logistics functions can be used for the analysis of flexibility adjustment in an existing supply chain. This paper develops such a tactical supply chain planning model incorporating a realistic range of flexibility options. A novel solution method is designed...

  17. Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation

    Scarpa, G.; Gaetano, R.; Haindl, Michal; Zerubia, J.

    2009-01-01

    Roč. 18, č. 8 (2009), s. 1830-1843 ISSN 1057-7149 R&D Projects: GA ČR GA102/08/0593 EU Projects: European Commission(XE) 507752 - MUSCLE Institutional research plan: CEZ:AV0Z10750506 Keywords : Classification * texture analysis * segmentation * hierarchical image models * Markov process Subject RIV: BD - Theory of Information Impact factor: 2.848, year: 2009 http://library.utia.cas.cz/separaty/2009/RO/haindl-hierarchical multiple markov chain model for unsupervised texture segmentation.pdf

  18. Feedback structure based entropy approach for multiple-model estimation

    Shen-tu Han; Xue Anke; Guo Yunfei

    2013-01-01

    The variable-structure multiple-model (VSMM) approach, one of the multiple-model (MM) methods, is a popular and effective approach in handling problems with mode uncertainties. The model sequence set adaptation (MSA) is the key to design a better VSMM. However, MSA methods in the literature have big room to improve both theoretically and practically. To this end, we propose a feedback structure based entropy approach that could find the model sequence sets with the smallest size under certain conditions. The filtered data are fed back in real time and can be used by the minimum entropy (ME) based VSMM algorithms, i.e., MEVSMM. Firstly, the full Markov chains are used to achieve optimal solutions. Secondly, the myopic method together with particle filter (PF) and the challenge match algorithm are also used to achieve sub-optimal solutions, a trade-off between practicability and optimality. The numerical results show that the proposed algorithm provides not only refined model sets but also a good robustness margin and very high accuracy.

  19. Study on validation method for femur finite element model under multiple loading conditions

    Guan, Fengjiao; Zhang, Guanjun; Liu, Jie; Wang, Shujing; Luo, Xu

    2018-03-01

    Acquisition of accurate and reliable constitutive parameters related to bio-tissue materials was beneficial to improve biological fidelity of a Finite Element (FE) model and predict impact damages more effectively. In this paper, a femur FE model was established under multiple loading conditions with diverse impact positions. Then, based on sequential response surface method and genetic algorithms, the material parameters identification was transformed to a multi-response optimization problem. Finally, the simulation results successfully coincided with force-displacement curves obtained by numerous experiments. Thus, computational accuracy and efficiency of the entire inverse calculation process were enhanced. This method was able to effectively reduce the computation time in the inverse process of material parameters. Meanwhile, the material parameters obtained by the proposed method achieved higher accuracy.

  20. Efficient Parameterization for Grey-box Model Identification of Complex Physical Systems

    Blanke, Mogens; Knudsen, Morten Haack

    2006-01-01

    Grey box model identification preserves known physical structures in a model but with limits to the possible excitation, all parameters are rarely identifiable, and different parametrizations give significantly different model quality. Convenient methods to show which parameterizations are the be...... that need be constrained to achieve satisfactory convergence. Identification of nonlinear models for a ship illustrate the concept....

  1. Modelling and Identification for Control of Gas Bearings

    Theisen, Lukas Roy Svane; Niemann, Hans Henrik; Santos, Ilmar

    2015-01-01

    Gas bearings are popular for their high speed capabilities, low friction and clean operation, but suffer from poor damping, which poses challenges for safe operation in presence of disturbances. Enhanced damping can be achieved through active lubrication techniques using feedback control laws....... Such control design requires models with low complexity, able to describe the dominant dynamics from actuator input to sensor output over the relevant range of operation. The mathematical models based on first principles are not easy to obtain, and in many cases, they cannot be directly used for control design...... to industrial rotating machinery with gas bearings and to allow for subsequent control design. The paper shows how piezoelectric actuators in a gas bearing are efficiently used to perturb the gas film for identification over relevant ranges of rotational speed and gas injection pressure. Parameter...

  2. A treatment model for craving identification and management.

    Stalcup, S Alex; Christian, Darrell; Stalcup, Janice; Brown, Michelle; Galloway, Gantt P

    2006-06-01

    This article presents an addiction treatment model based on craving identification and management (CIM). Craving is broadly defined as the desire to use alcohol or other drugs; it increases the likelihood of use of these substances. In the CIM Model treatment interventions are referenced to craving, i.e., helping clients to identify their craving level and equipping them with strategies to avoid use. Four causes of craving are identified: (1) environmental cues (triggers): exposure to people, places, and things associated with prior drug-using experiences may cause immediate and overwhelming craving; (2) stress: addicted persons experience stress as craving; (3) mental illness; and (4) drug withdrawal: symptoms of both mental illness and withdrawal lead to craving if clients associate use with relief of these symptoms. The CIM Model incorporates four service delivery elements: Relapse Prevention Workshop, individual counseling, medical/psychiatric services, and screening for ongoing drug use. At its core, the CIM Model asks clients to be aware of craving, analyze its causes, and, based on those causes, implement specific strategies to prevent and manage craving. The CIM Model combines several treatment components, including control of exposure to environmental cues, establishment of a daily schedule, the use of behaviors that dissipate craving (tools), and treatment (with medications when appropriate) of mental health and withdrawal symptoms. The CIM Model is a client-derived approach to achieving and maintaining sobriety based on a process of analyzing craving and managing it with an individualized program of recovery activities.

  3. Applying the Team Identification-Social Psychological Health Model to Older Sport Fans

    Wann, Daniel L.; Rogers, Kelly; Dooley, Keith; Foley, Mary

    2011-01-01

    According to the Team Identification-Social Psychological Health Model (Wann, 2006b), team identification and social psychological health should be positively correlated because identification leads to important social connections which, in turn, facilitate well-being. Although past research substantiates the hypothesized positive relationship…

  4. Modelling of diffuse solar fraction with multiple predictors

    Ridley, Barbara; Boland, John [Centre for Industrial and Applied Mathematics, University of South Australia, Mawson Lakes Boulevard, Mawson Lakes, SA 5095 (Australia); Lauret, Philippe [Laboratoire de Physique du Batiment et des Systemes, University of La Reunion, Reunion (France)

    2010-02-15

    For some locations both global and diffuse solar radiation are measured. However, for many locations, only global radiation is measured, or inferred from satellite data. For modelling solar energy applications, the amount of radiation on a tilted surface is needed. Since only the direct component on a tilted surface can be calculated from direct on some other plane using trigonometry, we need to have diffuse radiation on the horizontal plane available. There are regression relationships for estimating the diffuse on a tilted surface from diffuse on the horizontal. Models for estimating the diffuse on the horizontal from horizontal global that have been developed in Europe or North America have proved to be inadequate for Australia. Boland et al. developed a validated model for Australian conditions. Boland et al. detailed our recent advances in developing the theoretical framework for the use of the logistic function instead of piecewise linear or simple nonlinear functions and was the first step in identifying the means for developing a generic model for estimating diffuse from global and other predictors. We have developed a multiple predictor model, which is much simpler than previous models, and uses hourly clearness index, daily clearness index, solar altitude, apparent solar time and a measure of persistence of global radiation level as predictors. This model performs marginally better than currently used models for locations in the Northern Hemisphere and substantially better for Southern Hemisphere locations. We suggest it can be used as a universal model. (author)

  5. Identification of differentially expressed placental transcripts during multiple gestations in the Eurasian beaver (Castor fiber L.).

    Lipka, A; Paukszto, L; Majewska, M; Jastrzebski, J P; Myszczynski, K; Panasiewicz, G; Szafranska, B

    2017-09-01

    The Eurasian beaver is one of the largest rodents that, despite its high impact on the environment, is a non-model species that lacks a reference genome. Characterising genes critical for pregnancy outcome can serve as a basis for identifying mechanisms underlying effective reproduction, which is required for the success of endangered species conservation programs. In the present study, high-throughput RNA sequencing (RNA-seq) was used to analyse global changes in the Castor fiber subplacenta transcriptome during multiple pregnancy. De novo reconstruction of the C. fiber subplacenta transcriptome was used to identify genes that were differentially expressed in placentas (n=5) from two females (in advanced twin and triple pregnancy). Analyses of the expression values revealed 124 contigs with significantly different expression; of these, 55 genes were identified using MegaBLAST. Within this group of differentially expressed genes (DEGs), 18 were upregulated and 37 were downregulated in twins. Most DEGs were associated with the following gene ontology terms: cellular process, single organism process, response to stimulus, metabolic process and biological regulation. Some genes were also assigned to the developmental process, the reproductive process or reproduction. Among this group, four genes (namely keratin 19 (Krt19) and wingless-type MMTV integration site family - member 2 (Wnt2), which were downregulated in twins, and Nik-related kinase (Nrk) and gap junction protein β2 (Gjb2), which were upregulated in twins) were assigned to placental development and nine (Krt19, Wnt2 and integrin α 7 (Itga7), downregulated in twins, and Nrk, gap junction protein β6 (Gjb6), GATA binding protein 6 (Gata6), apolipoprotein A-I (ApoA1), apolipoprotein B (ApoB) and haemoglobin subunit α 1 (HbA1), upregulated in twins) were assigned to embryo development. The results of the present study indicate that the number of fetuses affects the expression profile in the C. fiber

  6. Rapidity correlations at fixed multiplicity in cluster emission models

    Berger, M C

    1975-01-01

    Rapidity correlations in the central region among hadrons produced in proton-proton collisions of fixed final state multiplicity n at NAL and ISR energies are investigated in a two-step framework in which clusters of hadrons are emitted essentially independently, via a multiperipheral-like model, and decay isotropically. For n>or approximately=/sup 1///sub 2/(n), these semi-inclusive distributions are controlled by the reaction mechanism which dominates production in the central region. Thus, data offer cleaner insight into the properties of this mechanism than can be obtained from fully inclusive spectra. A method of experimental analysis is suggested to facilitate the extraction of new dynamical information. It is shown that the n independence of the magnitude of semi-inclusive correlation functions reflects directly the structure of the internal cluster multiplicity distribution. This conclusion is independent of certain assumptions concerning the form of the single cluster density in rapidity space. (23 r...

  7. Multiplicative Attribute Graph Model of Real-World Networks

    Kim, Myunghwan [Stanford Univ., CA (United States); Leskovec, Jure [Stanford Univ., CA (United States)

    2010-10-20

    Large scale real-world network data, such as social networks, Internet andWeb graphs, is ubiquitous in a variety of scientific domains. The study of such social and information networks commonly finds patterns and explain their emergence through tractable models. In most networks, especially in social networks, nodes also have a rich set of attributes (e.g., age, gender) associatedwith them. However, most of the existing network models focus only on modeling the network structure while ignoring the features of nodes in the network. Here we present a class of network models that we refer to as the Multiplicative Attribute Graphs (MAG), which naturally captures the interactions between the network structure and node attributes. We consider a model where each node has a vector of categorical features associated with it. The probability of an edge between a pair of nodes then depends on the product of individual attributeattribute similarities. The model yields itself to mathematical analysis as well as fit to real data. We derive thresholds for the connectivity, the emergence of the giant connected component, and show that the model gives rise to graphs with a constant diameter. Moreover, we analyze the degree distribution to show that the model can produce networks with either lognormal or power-law degree distribution depending on certain conditions.

  8. MEASURE: An integrated data-analysis and model identification facility

    Singh, Jaidip; Iyer, Ravi K.

    1990-01-01

    The first phase of the development of MEASURE, an integrated data analysis and model identification facility is described. The facility takes system activity data as input and produces as output representative behavioral models of the system in near real time. In addition a wide range of statistical characteristics of the measured system are also available. The usage of the system is illustrated on data collected via software instrumentation of a network of SUN workstations at the University of Illinois. Initially, statistical clustering is used to identify high density regions of resource-usage in a given environment. The identified regions form the states for building a state-transition model to evaluate system and program performance in real time. The model is then solved to obtain useful parameters such as the response-time distribution and the mean waiting time in each state. A graphical interface which displays the identified models and their characteristics (with real time updates) was also developed. The results provide an understanding of the resource-usage in the system under various workload conditions. This work is targeted for a testbed of UNIX workstations with the initial phase ported to SUN workstations on the NASA, Ames Research Center Advanced Automation Testbed.

  9. Identification of reverse logistics decision types from mathematical models

    Pascual Cortés Pellicer

    2018-04-01

    Full Text Available Purpose: The increase in social awareness, politics and environmental regulation, the scarcity of raw materials and the desired “green” image, are some of the reasons that lead companies to decide for implement processes of Reverse Logistics (RL. At the time when incorporate new RL processes as key business processes, new and important decisions need to be made. Identification and knowledge of these decisions, including the information available and the implications for the company or supply chain, will be fundamental for decision-makers to achieve the best results. In the present work, the main types of RL decisions are identified. Design/methodology/approach: This paper is based on the analysis of mathematical models designed as tools to aid decision making in the field of RL. Once the types of interest work to be analyzed are defined, those studies that really deal about the object of study are searched and analyzed. The decision variables that are taken at work are identified and grouped according to the type of decision and, finally, are showed the main types of decisions used in mathematical models developed in the field of RL.     Findings: The principal conclusion of the research is that the most commonly addressed decisions with mathematical models in the field of RL are those related to the network’s configuration, followed by tactical/operative decisions such as the selections of product’s treatments to realize and the policy of returns or prices, among other decisions. Originality/value: The identification of the main decisions types of the reverse logistics will allow the managers of these processes to know and understand them better, while offer an integrated vision of them, favoring the achievement of better results.

  10. Dynamic coordinated control laws in multiple agent models

    Morgan, David S.; Schwartz, Ira B.

    2005-01-01

    We present an active control scheme of a kinetic model of swarming. It has been shown previously that the global control scheme for the model, presented in [Systems Control Lett. 52 (2004) 25], gives rise to spontaneous collective organization of agents into a unified coherent swarm, via steering controls and utilizing long-range attractive and short-range repulsive interactions. We extend these results by presenting control laws whereby a single swarm is broken into independently functioning subswarm clusters. The transition between one coordinated swarm and multiple clustered subswarms is managed simply with a homotopy parameter. Additionally, we present as an alternate formulation, a local control law for the same model, which implements dynamic barrier avoidance behavior, and in which swarm coherence emerges spontaneously

  11. Laplace transform analysis of a multiplicative asset transfer model

    Sokolov, Andrey; Melatos, Andrew; Kieu, Tien

    2010-07-01

    We analyze a simple asset transfer model in which the transfer amount is a fixed fraction f of the giver’s wealth. The model is analyzed in a new way by Laplace transforming the master equation, solving it analytically and numerically for the steady-state distribution, and exploring the solutions for various values of f∈(0,1). The Laplace transform analysis is superior to agent-based simulations as it does not depend on the number of agents, enabling us to study entropy and inequality in regimes that are costly to address with simulations. We demonstrate that Boltzmann entropy is not a suitable (e.g. non-monotonic) measure of disorder in a multiplicative asset transfer system and suggest an asymmetric stochastic process that is equivalent to the asset transfer model.

  12. Modeling Spatial Dependence of Rainfall Extremes Across Multiple Durations

    Le, Phuong Dong; Leonard, Michael; Westra, Seth

    2018-03-01

    Determining the probability of a flood event in a catchment given that another flood has occurred in a nearby catchment is useful in the design of infrastructure such as road networks that have multiple river crossings. These conditional flood probabilities can be estimated by calculating conditional probabilities of extreme rainfall and then transforming rainfall to runoff through a hydrologic model. Each catchment's hydrological response times are unlikely to be the same, so in order to estimate these conditional probabilities one must consider the dependence of extreme rainfall both across space and across critical storm durations. To represent these types of dependence, this study proposes a new approach for combining extreme rainfall across different durations within a spatial extreme value model using max-stable process theory. This is achieved in a stepwise manner. The first step defines a set of common parameters for the marginal distributions across multiple durations. The parameters are then spatially interpolated to develop a spatial field. Storm-level dependence is represented through the max-stable process for rainfall extremes across different durations. The dependence model shows a reasonable fit between the observed pairwise extremal coefficients and the theoretical pairwise extremal coefficient function across all durations. The study demonstrates how the approach can be applied to develop conditional maps of the return period and return level across different durations.

  13. Protein Structure Classification and Loop Modeling Using Multiple Ramachandran Distributions

    Najibi, Seyed Morteza

    2017-02-08

    Recently, the study of protein structures using angular representations has attracted much attention among structural biologists. The main challenge is how to efficiently model the continuous conformational space of the protein structures based on the differences and similarities between different Ramachandran plots. Despite the presence of statistical methods for modeling angular data of proteins, there is still a substantial need for more sophisticated and faster statistical tools to model the large-scale circular datasets. To address this need, we have developed a nonparametric method for collective estimation of multiple bivariate density functions for a collection of populations of protein backbone angles. The proposed method takes into account the circular nature of the angular data using trigonometric spline which is more efficient compared to existing methods. This collective density estimation approach is widely applicable when there is a need to estimate multiple density functions from different populations with common features. Moreover, the coefficients of adaptive basis expansion for the fitted densities provide a low-dimensional representation that is useful for visualization, clustering, and classification of the densities. The proposed method provides a novel and unique perspective to two important and challenging problems in protein structure research: structure-based protein classification and angular-sampling-based protein loop structure prediction.

  14. A multiple relevance feedback strategy with positive and negative models.

    Yunlong Ma

    Full Text Available A commonly used strategy to improve search accuracy is through feedback techniques. Most existing work on feedback relies on positive information, and has been extensively studied in information retrieval. However, when a query topic is difficult and the results from the first-pass retrieval are very poor, it is impossible to extract enough useful terms from a few positive documents. Therefore, the positive feedback strategy is incapable to improve retrieval in this situation. Contrarily, there is a relatively large number of negative documents in the top of the result list, and it has been confirmed that negative feedback strategy is an important and useful way for adapting this scenario by several recent studies. In this paper, we consider a scenario when the search results are so poor that there are at most three relevant documents in the top twenty documents. Then, we conduct a novel study of multiple strategies for relevance feedback using both positive and negative examples from the first-pass retrieval to improve retrieval accuracy for such difficult queries. Experimental results on these TREC collections show that the proposed language model based multiple model feedback method which is generally more effective than both the baseline method and the methods using only positive or negative model.

  15. Protein Structure Classification and Loop Modeling Using Multiple Ramachandran Distributions

    Najibi, Seyed Morteza; Maadooliat, Mehdi; Zhou, Lan; Huang, Jianhua Z.; Gao, Xin

    2017-01-01

    Recently, the study of protein structures using angular representations has attracted much attention among structural biologists. The main challenge is how to efficiently model the continuous conformational space of the protein structures based on the differences and similarities between different Ramachandran plots. Despite the presence of statistical methods for modeling angular data of proteins, there is still a substantial need for more sophisticated and faster statistical tools to model the large-scale circular datasets. To address this need, we have developed a nonparametric method for collective estimation of multiple bivariate density functions for a collection of populations of protein backbone angles. The proposed method takes into account the circular nature of the angular data using trigonometric spline which is more efficient compared to existing methods. This collective density estimation approach is widely applicable when there is a need to estimate multiple density functions from different populations with common features. Moreover, the coefficients of adaptive basis expansion for the fitted densities provide a low-dimensional representation that is useful for visualization, clustering, and classification of the densities. The proposed method provides a novel and unique perspective to two important and challenging problems in protein structure research: structure-based protein classification and angular-sampling-based protein loop structure prediction.

  16. Using the domain identification model to study major and career decision-making processes

    Tendhar, Chosang; Singh, Kusum; Jones, Brett D.

    2018-03-01

    The purpose of this study was to examine the extent to which (1) a domain identification model could be used to predict students' engineering major and career intentions and (2) the MUSIC Model of Motivation components could be used to predict domain identification. The data for this study were collected from first-year engineering students. We used a structural equation model to test the hypothesised relationship between variables in the partial domain identification model. The findings suggested that engineering identification significantly predicted engineering major intentions and career intentions and had the highest effect on those two variables compared to other motivational constructs. Furthermore, results suggested that success, interest, and caring are plausible contributors to students' engineering identification. Overall, there is strong evidence that the domain identification model can be used as a lens to study career decision-making processes in engineering, and potentially, in other fields as well.

  17. Tension-compression asymmetry modelling: strategies for anisotropy parameters identification.

    Barros Pedro

    2016-01-01

    Full Text Available This work presents details concerning the strategies and algorithms adopted in the fully implicit FE solver DD3IMP to model the orthotropic behavior of metallic sheets and the procedure for anisotropy parameters identification. The work is focused on the yield criterion developed by Cazacu, Plunkett and Barlat, 2006 [1], which accounts for both tension–compression asymmetry and orthotropic plastic behavior. The anisotropy parameters for a 2090-T3 aluminum alloy are identified accounting, or not, for the tension-compression asymmetry. The numerical simulation of a cup drawing is performed for this material, highlighting the importance of considering tension-compression asymmetry in the prediction of the earing profile, for materials with cubic structure, even if this phenomenon is relatively small.

  18. The Inverse Problem of Identification of Hydrogen Permeability Model

    Yury V. Zaika

    2018-01-01

    Full Text Available One of the technological challenges for hydrogen materials science is the currently active search for structural materials with important applications (including the ITER project and gas-separation plants. One had to estimate the parameters of diffusion and sorption to numerically model the different scenarios and experimental conditions of the material usage (including extreme ones. The article presents boundary value problems of hydrogen permeability and thermal desorption with dynamical boundary conditions. A numerical method is developed for TDS spectrum simulation, where only integration of a nonlinear system of low order ordinary differential equations is required. The main final output of the article is a noise-resistant algorithm for solving the inverse problem of parametric identification for the aggregated experiment where desorption and diffusion are dynamically interrelated (without the artificial division of studies into the diffusion limited regime (DLR and the surface limited regime (SLR.

  19. Identification of the reduced order models of a BWR reactor

    Hernandez S, A.

    2004-01-01

    The present work has as objective to analyze the relative stability of a BWR type reactor. It is analyzed that so adaptive it turns out to identify the parameters of a model of reduced order so that this it reproduces a condition of given uncertainty. This will take of a real fact happened in the La Salle plant under certain operation conditions of power and flow of coolant. The parametric identification is carried out by means of an algorithm of recursive least square and an Output Error model (Output Error), measuring the output power of the reactor when the instability is present, and considering that it is produced by a change in the reactivity of the system in the same way that a sign of type step. Also it is carried out an analytic comparison of the relative stability, analyzing two types of answers: the original answer of the uncertainty of the reactor vs. the obtained response identifying the parameters of the model of reduced order, reaching the conclusion that it is very viable to adapt a model of reduced order to study the stability of a reactor, under the only condition to consider that the dynamics of the reactivity is of step type. (Author)

  20. Deciphering the crowd: modeling and identification of pedestrian group motion.

    Yücel, Zeynep; Zanlungo, Francesco; Ikeda, Tetsushi; Miyashita, Takahiro; Hagita, Norihiro

    2013-01-14

    Associating attributes to pedestrians in a crowd is relevant for various areas like surveillance, customer profiling and service providing. The attributes of interest greatly depend on the application domain and might involve such social relations as friends or family as well as the hierarchy of the group including the leader or subordinates. Nevertheless, the complex social setting inherently complicates this task. We attack this problem by exploiting the small group structures in the crowd. The relations among individuals and their peers within a social group are reliable indicators of social attributes. To that end, this paper identifies social groups based on explicit motion models integrated through a hypothesis testing scheme. We develop two models relating positional and directional relations. A pair of pedestrians is identified as belonging to the same group or not by utilizing the two models in parallel, which defines a compound hypothesis testing scheme. By testing the proposed approach on three datasets with different environmental properties and group characteristics, it is demonstrated that we achieve an identification accuracy of 87% to 99%. The contribution of this study lies in its definition of positional and directional relation models, its description of compound evaluations, and the resolution of ambiguities with our proposed uncertainty measure based on the local and global indicators of group relation.

  1. Deciphering the Crowd: Modeling and Identification of Pedestrian Group Motion

    Norihiro Hagita

    2013-01-01

    Full Text Available Associating attributes to pedestrians in a crowd is relevant for various areas like surveillance, customer profiling and service providing. The attributes of interest greatly depend on the application domain and might involve such social relations as friends or family as well as the hierarchy of the group including the leader or subordinates. Nevertheless, the complex social setting inherently complicates this task. We attack this problem by exploiting the small group structures in the crowd. The relations among individuals and their peers within a social group are reliable indicators of social attributes. To that end, this paper identifies social groups based on explicit motion models integrated through a hypothesis testing scheme. We develop two models relating positional and directional relations. A pair of pedestrians is identified as belonging to the same group or not by utilizing the two models in parallel, which defines a compound hypothesis testing scheme. By testing the proposed approach on three datasets with different environmental properties and group characteristics, it is demonstrated that we achieve an identification accuracy of 87% to 99%. The contribution of this study lies in its definition of positional and directional relation models, its description of compound evaluations, and the resolution of ambiguities with our proposed uncertainty measure based on the local and global indicators of group relation.

  2. Many-electron model for multiple ionization in atomic collisions

    Archubi, C D; Montanari, C C; Miraglia, J E

    2007-01-01

    We have developed a many-electron model for multiple ionization of heavy atoms bombarded by bare ions. It is based on the transport equation for an ion in an inhomogeneous electronic density. Ionization probabilities are obtained by employing the shell-to-shell local plasma approximation with the Levine and Louie dielectric function to take into account the binding energy of each shell. Post-collisional contributions due to Auger-like processes are taken into account by employing recent photoemission data. Results for single-to-quadruple ionization of Ne, Ar, Kr and Xe by protons are presented showing a very good agreement with experimental data

  3. Many-electron model for multiple ionization in atomic collisions

    Archubi, C D [Instituto de AstronomIa y Fisica del Espacio, Casilla de Correo 67, Sucursal 28 (C1428EGA) Buenos Aires (Argentina); Montanari, C C [Instituto de AstronomIa y Fisica del Espacio, Casilla de Correo 67, Sucursal 28 (C1428EGA) Buenos Aires (Argentina); Miraglia, J E [Instituto de AstronomIa y Fisica del Espacio, Casilla de Correo 67, Sucursal 28 (C1428EGA) Buenos Aires (Argentina)

    2007-03-14

    We have developed a many-electron model for multiple ionization of heavy atoms bombarded by bare ions. It is based on the transport equation for an ion in an inhomogeneous electronic density. Ionization probabilities are obtained by employing the shell-to-shell local plasma approximation with the Levine and Louie dielectric function to take into account the binding energy of each shell. Post-collisional contributions due to Auger-like processes are taken into account by employing recent photoemission data. Results for single-to-quadruple ionization of Ne, Ar, Kr and Xe by protons are presented showing a very good agreement with experimental data.

  4. Model selection in Bayesian segmentation of multiple DNA alignments.

    Oldmeadow, Christopher; Keith, Jonathan M

    2011-03-01

    The analysis of multiple sequence alignments is allowing researchers to glean valuable insights into evolution, as well as identify genomic regions that may be functional, or discover novel classes of functional elements. Understanding the distribution of conservation levels that constitutes the evolutionary landscape is crucial to distinguishing functional regions from non-functional. Recent evidence suggests that a binary classification of evolutionary rates is inappropriate for this purpose and finds only highly conserved functional elements. Given that the distribution of evolutionary rates is multi-modal, determining the number of modes is of paramount concern. Through simulation, we evaluate the performance of a number of information criterion approaches derived from MCMC simulations in determining the dimension of a model. We utilize a deviance information criterion (DIC) approximation that is more robust than the approximations from other information criteria, and show our information criteria approximations do not produce superfluous modes when estimating conservation distributions under a variety of circumstances. We analyse the distribution of conservation for a multiple alignment comprising four primate species and mouse, and repeat this on two additional multiple alignments of similar species. We find evidence of six distinct classes of evolutionary rates that appear to be robust to the species used. Source code and data are available at http://dl.dropbox.com/u/477240/changept.zip.

  5. Two-component network model in voice identification technologies

    Edita K. Kuular

    2018-03-01

    Full Text Available Among the most important parameters of biometric systems with voice modalities that determine their effectiveness, along with reliability and noise immunity, a speed of identification and verification of a person has been accentuated. This parameter is especially sensitive while processing large-scale voice databases in real time regime. Many research studies in this area are aimed at developing new and improving existing algorithms for presentation and processing voice records to ensure high performance of voice biometric systems. Here, it seems promising to apply a modern approach, which is based on complex network platform for solving complex massive problems with a large number of elements and taking into account their interrelationships. Thus, there are known some works which while solving problems of analysis and recognition of faces from photographs, transform images into complex networks for their subsequent processing by standard techniques. One of the first applications of complex networks to sound series (musical and speech analysis are description of frequency characteristics by constructing network models - converting the series into networks. On the network ontology platform a previously proposed technique of audio information representation aimed on its automatic analysis and speaker recognition has been developed. This implies converting information into the form of associative semantic (cognitive network structure with amplitude and frequency components both. Two speaker exemplars have been recorded and transformed into pertinent networks with consequent comparison of their topological metrics. The set of topological metrics for each of network models (amplitude and frequency one is a vector, and together  those combine a matrix, as a digital "network" voiceprint. The proposed network approach, with its sensitivity to personal conditions-physiological, psychological, emotional, might be useful not only for person identification

  6. System identification and the modeling of sailing yachts

    Legursky, Katrina

    This research represents an exploration of sailing yacht dynamics with full-scale sailing motion data, physics-based models, and system identification techniques. The goal is to provide a method of obtaining and validating suitable physics-based dynamics models for use in control system design on autonomous sailing platforms, which have the capacity to serve as mobile, long range, high endurance autonomous ocean sensing platforms. The primary contributions of this study to the state-of-the-art are the formulation of a five degree-of-freedom (DOF) linear multi-input multi-output (MIMO) state space model of sailing yacht dynamics, the process for identification of this model from full-scale data, a description of the maneuvers performed during on-water tests, and an analysis method to validate estimated models. The techniques and results described herein can be directly applied to and tested on existing autonomous sailing platforms. A full-scale experiment on a 23ft monohull sailing yacht is developed to collect motion data for physics-based model identification. Measurements include 3 axes of accelerations, velocities, angular rates, and attitude angles in addition to apparent wind speed and direction. The sailing yacht herein is treated as a dynamic system with two control inputs, the rudder angle, deltaR, and the mainsail angle, delta B, which are also measured. Over 20 hours of full scale sailing motion data is collected, representing three sail configurations corresponding to a range of wind speeds: the Full Main and Genoa (abbrev. Genoa) for lower wind speeds, the Full Main and Jib (abbrev. Jib) for mid-range wind speeds, and the Reefed Main and Jib (abbrev. Reef) for the highest wind speeds. The data also covers true wind angles from upwind through a beam reach. A physics-based non-linear model to describe sailing yacht motion is outlined, including descriptions of methods to model the aerodynamics and hydrodynamics of a sailing yacht in surge, sway, roll, and

  7. A multiple-location model for natural gas forward curves

    Buffington, J.C.

    1999-06-01

    This thesis presents an approach for financial modelling of natural gas in which connections between locations are incorporated and the complexities of forward curves in natural gas are considered. Apart from electricity, natural gas is the most volatile commodity traded. Its price is often dependent on the weather and price shocks can be felt across several geographic locations. This modelling approach incorporates multiple risk factors that correspond to various locations. One of the objectives was to determine if the model could be used for closed-form option prices. It was suggested that an adequate model for natural gas must consider 3 statistical properties: volatility term structure, backwardation and contango, and stochastic basis. Data from gas forward prices at Chicago, NYMEX and AECO were empirically tested to better understand these 3 statistical properties at each location and to verify if the proposed model truly incorporates these properties. In addition, this study examined the time series property of the difference of two locations (the basis) and determines that these empirical properties are consistent with the model properties. Closed-form option solutions were also developed for call options of forward contracts and call options on forward basis. The options were calibrated and compared to other models. The proposed model is capable of pricing options, but the prices derived did not pass the test of economic reasonableness. However, the model was able to capture the effect of transportation as well as aspects of seasonality which is a benefit over other existing models. It was determined that modifications will be needed regarding the estimation of the convenience yields. 57 refs., 2 tabs., 7 figs., 1 append

  8. Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

    Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

    2000-01-01

    Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

  9. Multiplicative point process as a model of trading activity

    Gontis, V.; Kaulakys, B.

    2004-11-01

    Signals consisting of a sequence of pulses show that inherent origin of the 1/ f noise is a Brownian fluctuation of the average interevent time between subsequent pulses of the pulse sequence. In this paper, we generalize the model of interevent time to reproduce a variety of self-affine time series exhibiting power spectral density S( f) scaling as a power of the frequency f. Furthermore, we analyze the relation between the power-law correlations and the origin of the power-law probability distribution of the signal intensity. We introduce a stochastic multiplicative model for the time intervals between point events and analyze the statistical properties of the signal analytically and numerically. Such model system exhibits power-law spectral density S( f)∼1/ fβ for various values of β, including β= {1}/{2}, 1 and {3}/{2}. Explicit expressions for the power spectra in the low-frequency limit and for the distribution density of the interevent time are obtained. The counting statistics of the events is analyzed analytically and numerically, as well. The specific interest of our analysis is related with the financial markets, where long-range correlations of price fluctuations largely depend on the number of transactions. We analyze the spectral density and counting statistics of the number of transactions. The model reproduces spectral properties of the real markets and explains the mechanism of power-law distribution of trading activity. The study provides evidence that the statistical properties of the financial markets are enclosed in the statistics of the time interval between trades. A multiplicative point process serves as a consistent model generating this statistics.

  10. Guideline validation in multiple trauma care through business process modeling.

    Stausberg, Jürgen; Bilir, Hüseyin; Waydhas, Christian; Ruchholtz, Steffen

    2003-07-01

    Clinical guidelines can improve the quality of care in multiple trauma. In our Department of Trauma Surgery a specific guideline is available paper-based as a set of flowcharts. This format is appropriate for the use by experienced physicians but insufficient for electronic support of learning, workflow and process optimization. A formal and logically consistent version represented with a standardized meta-model is necessary for automatic processing. In our project we transferred the paper-based into an electronic format and analyzed the structure with respect to formal errors. Several errors were detected in seven error categories. The errors were corrected to reach a formally and logically consistent process model. In a second step the clinical content of the guideline was revised interactively using a process-modeling tool. Our study reveals that guideline development should be assisted by process modeling tools, which check the content in comparison to a meta-model. The meta-model itself could support the domain experts in formulating their knowledge systematically. To assure sustainability of guideline development a representation independent of specific applications or specific provider is necessary. Then, clinical guidelines could be used for eLearning, process optimization and workflow management additionally.

  11. Rank-based model selection for multiple ions quantum tomography

    Guţă, Mădălin; Kypraios, Theodore; Dryden, Ian

    2012-01-01

    The statistical analysis of measurement data has become a key component of many quantum engineering experiments. As standard full state tomography becomes unfeasible for large dimensional quantum systems, one needs to exploit prior information and the ‘sparsity’ properties of the experimental state in order to reduce the dimensionality of the estimation problem. In this paper we propose model selection as a general principle for finding the simplest, or most parsimonious explanation of the data, by fitting different models and choosing the estimator with the best trade-off between likelihood fit and model complexity. We apply two well established model selection methods—the Akaike information criterion (AIC) and the Bayesian information criterion (BIC)—two models consisting of states of fixed rank and datasets such as are currently produced in multiple ions experiments. We test the performance of AIC and BIC on randomly chosen low rank states of four ions, and study the dependence of the selected rank with the number of measurement repetitions for one ion states. We then apply the methods to real data from a four ions experiment aimed at creating a Smolin state of rank 4. By applying the two methods together with the Pearson χ 2 test we conclude that the data can be suitably described with a model whose rank is between 7 and 9. Additionally we find that the mean square error of the maximum likelihood estimator for pure states is close to that of the optimal over all possible measurements. (paper)

  12. Identification of multiple intelligences for high school students in theoretical and applied science courses

    Wiseman, D. Kim

    Historically educators in the United States have used the Stanford-Binet intelligence test to measure a students' ability in logical/mathematical and linguistic domains. This measurement is being used by a society that has evolved from agrarian and industrial-based economies to what is presently labeled a technological society. As society has changed so have the educational needs of the students who will live in this technological society. This study assessed the multiple intelligences of high school students enrolled in theoretical and applied science (physics and applied physics) courses. Studies have verified that performance and outcomes of students enrolled in these courses are similar in standardized testing but instructional methodology and processes are dissimilar. Analysis of multiple intelligence profiles collected from this study found significant differences in logical/mathematical, bodily/kinesthetic and intrapersonal multiple intelligences of students in theoretical science courses compared to students in applied science courses. Those differences clearly illustrate why it is imperative for educators to expand the definition of intelligence for students entering the new millennium.

  13. [Crop geometry identification based on inversion of semiempirical BRDF models].

    Zhao, Chun-jiang; Huang, Wen-jiang; Mu, Xu-han; Wang, Jin-diz; Wang, Ji-hua

    2009-09-01

    With the rapid development of remote sensing technology, the application of remote sensing has extended from single view angle to multi-view angles. It was studied for the qualitative and quantitative effect of average leaf angle (ALA) on crop canopy reflected spectrum. Effect of ALA on canopy reflected spectrum can not be ignored with inversion of leaf area index (LAI) and monitoring of crop growth condition by remote sensing technology. Investigations of the effect of erective and horizontal varieties were conducted by bidirectional canopy reflected spectrum and semiempirical bidirectional reflectance distribution function (BRDF) models. The sensitive analysis was done based on the weight for the volumetric kernel (fvol), the weight for the geometric kernel (fgeo), and the weight for constant corresponding to isotropic reflectance (fiso) at red band (680 nm) and near infrared band (800 nm). By combining the weights of the red and near-infrared bands, the semiempirical models can obtain structural information by retrieving biophysical parameters from the physical BRDF model and a number of bidirectional observations. So, it will allow an on-site and non-sampling mode of crop ALA identification, which is useful for using remote sensing for crop growth monitoring and for improving the LAI inversion accuracy, and it will help the farmers in guiding the fertilizer and irrigation management in the farmland without a priori knowledge.

  14. A Hybrid Multiple Criteria Decision Making Model for Supplier Selection

    Chung-Min Wu

    2013-01-01

    Full Text Available The sustainable supplier selection would be the vital part in the management of a sustainable supply chain. In this study, a hybrid multiple criteria decision making (MCDM model is applied to select optimal supplier. The fuzzy Delphi method, which can lead to better criteria selection, is used to modify criteria. Considering the interdependence among the selection criteria, analytic network process (ANP is then used to obtain their weights. To avoid calculation and additional pairwise comparisons of ANP, a technique for order preference by similarity to ideal solution (TOPSIS is used to rank the alternatives. The use of a combination of the fuzzy Delphi method, ANP, and TOPSIS, proposing an MCDM model for supplier selection, and applying these to a real case are the unique features of this study.

  15. Multiple Scattering Model for Optical Coherence Tomography with Rytov Approximation

    Li, Muxingzi

    2017-04-24

    Optical Coherence Tomography (OCT) is a coherence-gated, micrometer-resolution imaging technique that focuses a broadband near-infrared laser beam to penetrate into optical scattering media, e.g. biological tissues. The OCT resolution is split into two parts, with the axial resolution defined by half the coherence length, and the depth-dependent lateral resolution determined by the beam geometry, which is well described by a Gaussian beam model. The depth dependence of lateral resolution directly results in the defocusing effect outside the confocal region and restricts current OCT probes to small numerical aperture (NA) at the expense of lateral resolution near the focus. Another limitation on OCT development is the presence of a mixture of speckles due to multiple scatterers within the coherence length, and other random noise. Motivated by the above two challenges, a multiple scattering model based on Rytov approximation and Gaussian beam optics is proposed for the OCT setup. Some previous papers have adopted the first Born approximation with the assumption of small perturbation of the incident field in inhomogeneous media. The Rytov method of the same order with smooth phase perturbation assumption benefits from a wider spatial range of validity. A deconvolution method for solving the inverse problem associated with the first Rytov approximation is developed, significantly reducing the defocusing effect through depth and therefore extending the feasible range of NA.

  16. Resveratrol Neuroprotection in a Chronic Mouse Model of Multiple Sclerosis

    Zoe eFonseca-Kelly

    2012-05-01

    Full Text Available Resveratrol is a naturally-occurring polyphenol that activates SIRT1, an NAD-dependent deacetylase. SRT501, a pharmaceutical formulation of resveratrol with enhanced systemic absorption, prevents neuronal loss without suppressing inflammation in mice with relapsing experimental autoimmune encephalomyelitis (EAE, a model of multiple sclerosis. In contrast, resveratrol has been reported to suppress inflammation in chronic EAE, although neuroprotective effects were not evaluated. The current studies examine potential neuroprotective and immunomodulatory effects of resveratrol in chronic EAE induced by immunization with myelin oligodendroglial glycoprotein peptide in C57/Bl6 mice. Effects of two distinct formulations of resveratrol administered daily orally were compared. Resveratrol delayed the onset of EAE compared to vehicle-treated EAE mice, but did not prevent or alter the phenotype of inflammation in spinal cords or optic nerves. Significant neuroprotective effects were observed, with higher numbers of retinal ganglion cells found in eyes of resveratrol-treated EAE mice with optic nerve inflammation. Results demonstrate that resveratrol prevents neuronal loss in this chronic demyelinating disease model, similar to its effects in relapsing EAE. Differences in immunosuppression compared with prior studies suggest that immunomodulatory effects may be limited and may depend on specific immunization parameters or timing of treatment. Importantly, neuroprotective effects can occur without immunosuppression, suggesting a potential additive benefit of resveratrol in combination with anti-inflammatory therapies for multiple sclerosis.

  17. Model for CO2 leakage including multiple geological layers and multiple leaky wells.

    Nordbotten, Jan M; Kavetski, Dmitri; Celia, Michael A; Bachu, Stefan

    2009-02-01

    Geological storage of carbon dioxide (CO2) is likely to be an integral component of any realistic plan to reduce anthropogenic greenhouse gas emissions. In conjunction with large-scale deployment of carbon storage as a technology, there is an urgent need for tools which provide reliable and quick assessments of aquifer storage performance. Previously, abandoned wells from over a century of oil and gas exploration and production have been identified as critical potential leakage paths. The practical importance of abandoned wells is emphasized by the correlation of heavy CO2 emitters (typically associated with industrialized areas) to oil and gas producing regions in North America. Herein, we describe a novel framework for predicting the leakage from large numbers of abandoned wells, forming leakage paths connecting multiple subsurface permeable formations. The framework is designed to exploit analytical solutions to various components of the problem and, ultimately, leads to a grid-free approximation to CO2 and brine leakage rates, as well as fluid distributions. We apply our model in a comparison to an established numerical solverforthe underlying governing equations. Thereafter, we demonstrate the capabilities of the model on typical field data taken from the vicinity of Edmonton, Alberta. This data set consists of over 500 wells and 7 permeable formations. Results show the flexibility and utility of the solution methods, and highlight the role that analytical and semianalytical solutions can play in this important problem.

  18. Positive identification by a skull with multiple epigenetic traits and abnormal structure of the neurocranium, viscerocranium, and the skeleton.

    Kuharić, Josip; Kovacic, Natasa; Marusic, Petar; Marusic, Ana; Petrovecki, Vedrana

    2011-05-01

    Wormian bones are small ossicles appearing within the cranial sutures in more than 40% of skulls, most commonly at the lambdoid suture and pterion. During the skeletal analysis of an unidentified male war victim, we observed multiple wormian bones and a patent metopic suture. Additionally, the right elbow was deformed, probably as a consequence of an old trauma. The skull was analyzed by cranial measurements and computerized tomography, revealing the presence of cranial deformities including hyperbrachicrania, localized reduction in hemispheral widths, increased cranial capacity, and sclerosis of the viscerocranium. Besides unique anatomical features and their anthropological value, such skeletal abnormalities also have a forensic value as the evidence to support the final identification of the victim. © 2011 American Academy of Forensic Sciences.

  19. Rapid identification and simultaneous analysis of multiple constituents from Rheum tanguticum Maxim. ex Balf. by UPLC/Q-TOF-MS.

    Gao, Liang-Liang; Guo, Tao; Xu, Xu-Dong; Yang, Jun-Shan

    2017-07-01

    Rhubarb contains biologically active compounds such as anthraquinones, anthrones, stilbenes and tannins. A rapid and efficient UPLC/Q-TOF-MS/MS method was developed and applied towards identifying the constituents of Rheum tanguticum Maxim. ex Balf. for the first time. Chemical constituents were separated and investigated by UPLC/Q-TOF-MS/MS in the negative ion mode. The ESI-MS 2 fragmentation pathways of four types of compounds were interpreted, providing a very useful guidance for the characterisation of different types of compounds. Based on the exact mass information, fragmentation characteristic and LC retention time of 7 reference standards, 30 constituents were tentatively identified from the methanol extract of R. tanguticum. Among them, seven compounds were described for the first time from R. tanguticum and two from the genus Rheum were described for the first time. The analytical tool used here is valuable for the rapid separation and identification of multiple and minor constituents in methanol extracts of R. tanguticum.

  20. A Multiple Indicators Multiple Causes (MIMIC) model of internal barriers to drug treatment in China.

    Qi, Chang; Kelly, Brian C; Liao, Yanhui; He, Haoyu; Luo, Tao; Deng, Huiqiong; Liu, Tieqiao; Hao, Wei; Wang, Jichuan

    2015-03-01

    Although evidence exists for distinct barriers to drug abuse treatment (BDATs), investigations of their inter-relationships and the effect of individual characteristics on the barrier factors have been sparse, especially in China. A Multiple Indicators Multiple Causes (MIMIC) model is applied for this target. A sample of 262 drug users were recruited from three drug rehabilitation centers in Hunan Province, China. We applied a MIMIC approach to investigate the effect of gender, age, marital status, education, primary substance use, duration of primary drug use, and drug treatment experience on the internal barrier factors: absence of problem (AP), negative social support (NSS), fear of treatment (FT), and privacy concerns (PC). Drug users of various characteristics were found to report different internal barrier factors. Younger participants were more likely to report NSS (-0.19, p=0.038) and PC (-0.31, p<0.001). Compared to other drug users, ice users were more likely to report AP (0.44, p<0.001) and NSS (0.25, p=0.010). Drug treatment experiences related to AP (0.20, p=0.012). In addition, differential item functioning (DIF) occurred in three items when participant from groups with different duration of drug use, ice use, or marital status. Individual characteristics had significant effects on internal barriers to drug treatment. On this basis, BDAT perceived by different individuals could be assessed before tactics were utilized to successfully remove perceived barriers to drug treatment. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  1. Bayesian models and meta analysis for multiple tissue gene expression data following corticosteroid administration

    Kelemen Arpad

    2008-08-01

    Full Text Available Abstract Background This paper addresses key biological problems and statistical issues in the analysis of large gene expression data sets that describe systemic temporal response cascades to therapeutic doses in multiple tissues such as liver, skeletal muscle, and kidney from the same animals. Affymetrix time course gene expression data U34A are obtained from three different tissues including kidney, liver and muscle. Our goal is not only to find the concordance of gene in different tissues, identify the common differentially expressed genes over time and also examine the reproducibility of the findings by integrating the results through meta analysis from multiple tissues in order to gain a significant increase in the power of detecting differentially expressed genes over time and to find the differential differences of three tissues responding to the drug. Results and conclusion Bayesian categorical model for estimating the proportion of the 'call' are used for pre-screening genes. Hierarchical Bayesian Mixture Model is further developed for the identifications of differentially expressed genes across time and dynamic clusters. Deviance information criterion is applied to determine the number of components for model comparisons and selections. Bayesian mixture model produces the gene-specific posterior probability of differential/non-differential expression and the 95% credible interval, which is the basis for our further Bayesian meta-inference. Meta-analysis is performed in order to identify commonly expressed genes from multiple tissues that may serve as ideal targets for novel treatment strategies and to integrate the results across separate studies. We have found the common expressed genes in the three tissues. However, the up/down/no regulations of these common genes are different at different time points. Moreover, the most differentially expressed genes were found in the liver, then in kidney, and then in muscle.

  2. Multiple-relaxation-time lattice Boltzmann model for compressible fluids

    Chen Feng; Xu Aiguo; Zhang Guangcai; Li Yingjun

    2011-01-01

    We present an energy-conserving multiple-relaxation-time finite difference lattice Boltzmann model for compressible flows. The collision step is first calculated in the moment space and then mapped back to the velocity space. The moment space and corresponding transformation matrix are constructed according to the group representation theory. Equilibria of the nonconserved moments are chosen according to the need of recovering compressible Navier-Stokes equations through the Chapman-Enskog expansion. Numerical experiments showed that compressible flows with strong shocks can be well simulated by the present model. The new model works for both low and high speeds compressible flows. It contains more physical information and has better numerical stability and accuracy than its single-relaxation-time version. - Highlights: → We present an energy-conserving MRT finite-difference LB model. → The moment space is constructed according to the group representation theory. → The new model works for both low and high speeds compressible flows. → It has better numerical stability and wider applicable range than its SRT version.

  3. Optimal Retail Price Model for Partial Consignment to Multiple Retailers

    Po-Yu Chen

    2017-01-01

    Full Text Available This paper investigates the product pricing decision-making problem under a consignment stock policy in a two-level supply chain composed of one supplier and multiple retailers. The effects of the supplier’s wholesale prices and its partial inventory cost absorption of the retail prices of retailers with different market shares are investigated. In the partial product consignment model this paper proposes, the seller and the retailers each absorb part of the inventory costs. This model also provides general solutions for the complete product consignment and the traditional policy that adopts no product consignment. In other words, both the complete consignment and nonconsignment models are extensions of the proposed model (i.e., special cases. Research results indicated that the optimal retail price must be between 1/2 (50% and 2/3 (66.67% times the upper limit of the gross profit. This study also explored the results and influence of parameter variations on optimal retail price in the model.

  4. A Hybrid Fuzzy Time Series Approach Based on Fuzzy Clustering and Artificial Neural Network with Single Multiplicative Neuron Model

    Ozge Cagcag Yolcu

    2013-01-01

    Full Text Available Particularly in recent years, artificial intelligence optimization techniques have been used to make fuzzy time series approaches more systematic and improve forecasting performance. Besides, some fuzzy clustering methods and artificial neural networks with different structures are used in the fuzzification of observations and determination of fuzzy relationships, respectively. In approaches considering the membership values, the membership values are determined subjectively or fuzzy outputs of the system are obtained by considering that there is a relation between membership values in identification of relation. This necessitates defuzzification step and increases the model error. In this study, membership values were obtained more systematically by using Gustafson-Kessel fuzzy clustering technique. The use of artificial neural network with single multiplicative neuron model in identification of fuzzy relation eliminated the architecture selection problem as well as the necessity for defuzzification step by constituting target values from real observations of time series. The training of artificial neural network with single multiplicative neuron model which is used for identification of fuzzy relation step is carried out with particle swarm optimization. The proposed method is implemented using various time series and the results are compared with those of previous studies to demonstrate the performance of the proposed method.

  5. Balancing precision and risk: should multiple detection methods be analyzed separately in N-mixture models?

    Tabitha A Graves

    Full Text Available Using multiple detection methods can increase the number, kind, and distribution of individuals sampled, which may increase accuracy and precision and reduce cost of population abundance estimates. However, when variables influencing abundance are of interest, if individuals detected via different methods are influenced by the landscape differently, separate analysis of multiple detection methods may be more appropriate. We evaluated the effects of combining two detection methods on the identification of variables important to local abundance using detections of grizzly bears with hair traps (systematic and bear rubs (opportunistic. We used hierarchical abundance models (N-mixture models with separate model components for each detection method. If both methods sample the same population, the use of either data set alone should (1 lead to the selection of the same variables as important and (2 provide similar estimates of relative local abundance. We hypothesized that the inclusion of 2 detection methods versus either method alone should (3 yield more support for variables identified in single method analyses (i.e. fewer variables and models with greater weight, and (4 improve precision of covariate estimates for variables selected in both separate and combined analyses because sample size is larger. As expected, joint analysis of both methods increased precision as well as certainty in variable and model selection. However, the single-method analyses identified different variables and the resulting predicted abundances had different spatial distributions. We recommend comparing single-method and jointly modeled results to identify the presence of individual heterogeneity between detection methods in N-mixture models, along with consideration of detection probabilities, correlations among variables, and tolerance to risk of failing to identify variables important to a subset of the population. The benefits of increased precision should be weighed

  6. Genome-wide SNP identification in multiple morphotypes of allohexaploid tall fescue (Festuca arundinacea Schreb

    Hand Melanie L

    2012-06-01

    Full Text Available Abstract Background Single nucleotide polymorphisms (SNPs provide essential tools for the advancement of research in plant genomics, and the development of SNP resources for many species has been accelerated by the capabilities of second-generation sequencing technologies. The current study aimed to develop and use a novel bioinformatic pipeline to generate a comprehensive collection of SNP markers within the agriculturally important pasture grass tall fescue; an outbreeding allopolyploid species displaying three distinct morphotypes: Continental, Mediterranean and rhizomatous. Results A bioinformatic pipeline was developed that successfully identified SNPs within genotypes from distinct tall fescue morphotypes, following the sequencing of 414 polymerase chain reaction (PCR – generated amplicons using 454 GS FLX technology. Equivalent amplicon sets were derived from representative genotypes of each morphotype, including six Continental, five Mediterranean and one rhizomatous. A total of 8,584 and 2,292 SNPs were identified with high confidence within the Continental and Mediterranean morphotypes respectively. The success of the bioinformatic approach was demonstrated through validation (at a rate of 70% of a subset of 141 SNPs using both SNaPshot™ and GoldenGate™ assay chemistries. Furthermore, the quantitative genotyping capability of the GoldenGate™ assay revealed that approximately 30% of the putative SNPs were accessible to co-dominant scoring, despite the hexaploid genome structure. The sub-genome-specific origin of each SNP validated from Continental tall fescue was predicted using a phylogenetic approach based on comparison with orthologous sequences from predicted progenitor species. Conclusions Using the appropriate bioinformatic approach, amplicon resequencing based on 454 GS FLX technology is an effective method for the identification of polymorphic SNPs within the genomes of Continental and Mediterranean tall fescue. The

  7. Identification of a new mutation in an Iranian family with hereditary multiple osteochondromas

    Akbaroghli S

    2016-12-01

    Full Text Available Susan Akbaroghli,1,* Maryam Balali,2,* Behnam Kamalidehghan,3,4 Siamak Saber,4 Omid Aryani,5 Goh Yong Meng,6 Massoud Houshmand4 1Mofid Children’s Hospital, Shahid Beheshti University of Medical Sciences, 2ENT and Head & Neck Research Center and Department, Iran University of Medical Sciences (IUMS, 3Medical Genetics Department, School of Medicine, Shahid Beheshti University of Medical Sciences, 4Medical Genetics Department, National Institute for Genetic Engineering and Biotechnology, 5Department of Neuroscience, Iran Medical University, Tehran, Iran; 6Department of Veterinary Preclinical Sciences, Faculty of Veterinary Medicine, Universiti Putra Malaysia (UPM, Serdang, Malaysia *These authors contributed equally to this work Background: Hereditary multiple osteochondromas (HMO, previously named hereditary multiple exostoses (HME, is an autosomal dominant skeletal disorder characterized by the growth of multiple osteochondromas and is associated with bony deformity, skeletal growth reduction, nerve compression, restriction of joint motion, and premature osteoarthrosis. HMO is genetically heterogeneous, localized on at least three chromosomal loci including 8q24.1 (EXT1, 11p11-p13 (EXT2, and 19p (EXT3. The median age of diagnosis is 3 years; almost all affected individuals are diagnosed by age 12. The risk for malignant degeneration to osteochondrosarcoma increases with age, although the lifetime risk of malignant degeneration is low (~1%.Methods and results: This study was performed on an Iranian family with nine affected individuals from three consecutive generations. Here, the proband was an affected woman who received genetic counseling prior to pregnancy. All exons of the three genes were examined in the proband using polymerase chain reaction and sequencing methods (the last member of this family is a male with severe deformities and lesions, especially around his large joints. Exon 4 of EXT1 (c.1235 G>A was changed in affected

  8. The effect of exposure to multiple lineups on face identification accuracy.

    Hinz, T; Pezdek, K

    2001-04-01

    This study examines the conditions under which an intervening lineup affects identification accuracy on a subsequent lineup. One hundred and sixty adults observed a photograph of one target individual for 60 s. One week later, they viewed an intervening target-absent lineup and were asked to identify the target individual. Two days later, participants were shown one of three 6-person lineups that included a different photograph of the target face (present or absent), a foil face from the intervening lineup (present or absent), plus additional foil faces. The hit rate was higher when the foil face from the intervening lineup was absent from the test lineup and the false alarm rate was greater when the target face was absent from the test lineup. The results suggest that simply being exposed to an innocent suspect in an intervening lineup, whether that innocent suspect is identified by the witness or not, increases the probability of misidentifying the innocent suspect and decreases the probability of correctly identifying the true perpetrator in a subsequent test lineup. The implications of these findings both for police lineup procedures and for the interpretation of lineup results in the courtroom are discussed.

  9. Identification and characterization of multiple conserved nuclear localization signals within adenovirus E1A

    Marshall, Kris S.; Cohen, Michael J.; Fonseca, Greg J.; Todorovic, Biljana; King, Cason R. [Department of Microbiology and Immunology, Western University, London Regional Cancer Program, London, ON, Canada N6A 4L6 (Canada); Yousef, Ahmed F. [Department of Chemical and Environmental Engineering, Masdar Institute, Abu Dhabi (United Arab Emirates); Zhang, Zhiying [College of Animal Science and Technologies, Northwest A and F University, Yangling, Shaanxi 712100 (China); Mymryk, Joe S., E-mail: jmymryk@uwo.ca [Department of Microbiology and Immunology, Western University, London Regional Cancer Program, London, ON, Canada N6A 4L6 (Canada); Department of Oncology, Western University, London Regional Cancer Program, London, ON, Canada N6A 4L6 (Canada)

    2014-04-15

    The human adenovirus 5 (HAdV-5) E1A protein has a well defined canonical nuclear localization signal (NLS) located at its C-terminus. We used a genetic assay in the yeast Saccharomyces cerevisiae to demonstrate that the canonical NLS is present and functional in the E1A proteins of each of the six HAdV species. This assay also detects a previously described non-canonical NLS within conserved region 3 and a novel active NLS within the N-terminal/conserved region 1 portion of HAdV-5 E1A. These activities were also present in the E1A proteins of each of the other five HAdV species. These results demonstrate that, despite substantial differences in primary sequence, HAdV E1A proteins are remarkably consistent in that they contain one canonical and two non-canonical NLSs. By utilizing independent mechanisms, these multiple NLSs ensure nuclear localization of E1A in the infected cell. - Highlights: • HAdV E1A uses multiple mechanisms for nuclear import. • We identified an additional non-canonical NLS in the N-terminal/CR1 portion of E1A. • The new NLS does not contact importin-alpha directly. • All NLSs are functionally conserved in the E1A proteins of all 6 HAdV species.

  10. PSP: rapid identification of orthologous coding genes under positive selection across multiple closely related prokaryotic genomes.

    Su, Fei; Ou, Hong-Yu; Tao, Fei; Tang, Hongzhi; Xu, Ping

    2013-12-27

    With genomic sequences of many closely related bacterial strains made available by deep sequencing, it is now possible to investigate trends in prokaryotic microevolution. Positive selection is a sub-process of microevolution, in which a particular mutation is favored, causing the allele frequency to continuously shift in one direction. Wide scanning of prokaryotic genomes has shown that positive selection at the molecular level is much more frequent than expected. Genes with significant positive selection may play key roles in bacterial adaption to different environmental pressures. However, selection pressure analyses are computationally intensive and awkward to configure. Here we describe an open access web server, which is designated as PSP (Positive Selection analysis for Prokaryotic genomes) for performing evolutionary analysis on orthologous coding genes, specially designed for rapid comparison of dozens of closely related prokaryotic genomes. Remarkably, PSP facilitates functional exploration at the multiple levels by assignments and enrichments of KO, GO or COG terms. To illustrate this user-friendly tool, we analyzed Escherichia coli and Bacillus cereus genomes and found that several genes, which play key roles in human infection and antibiotic resistance, show significant evidence of positive selection. PSP is freely available to all users without any login requirement at: http://db-mml.sjtu.edu.cn/PSP/. PSP ultimately allows researchers to do genome-scale analysis for evolutionary selection across multiple prokaryotic genomes rapidly and easily, and identify the genes undergoing positive selection, which may play key roles in the interactions of host-pathogen and/or environmental adaptation.

  11. Parameter identification in ODE models with oscillatory dynamics: a Fourier regularization approach

    Chiara D'Autilia, Maria; Sgura, Ivonne; Bozzini, Benedetto

    2017-12-01

    In this paper we consider a parameter identification problem (PIP) for data oscillating in time, that can be described in terms of the dynamics of some ordinary differential equation (ODE) model, resulting in an optimization problem constrained by the ODEs. In problems with this type of data structure, simple application of the direct method of control theory (discretize-then-optimize) yields a least-squares cost function exhibiting multiple ‘low’ minima. Since in this situation any optimization algorithm is liable to fail in the approximation of a good solution, here we propose a Fourier regularization approach that is able to identify an iso-frequency manifold {{ S}} of codimension-one in the parameter space \

  12. A latent class multiple constraint multiple discrete-continuous extreme value model of time use and goods consumption.

    2016-06-01

    This paper develops a microeconomic theory-based multiple discrete continuous choice model that considers: (a) that both goods consumption and time allocations (to work and non-work activities) enter separately as decision variables in the utility fu...

  13. Using hidden Markov models to align multiple sequences.

    Mount, David W

    2009-07-01

    A hidden Markov model (HMM) is a probabilistic model of a multiple sequence alignment (msa) of proteins. In the model, each column of symbols in the alignment is represented by a frequency distribution of the symbols (called a "state"), and insertions and deletions are represented by other states. One moves through the model along a particular path from state to state in a Markov chain (i.e., random choice of next move), trying to match a given sequence. The next matching symbol is chosen from each state, recording its probability (frequency) and also the probability of going to that state from a previous one (the transition probability). State and transition probabilities are multiplied to obtain a probability of the given sequence. The hidden nature of the HMM is due to the lack of information about the value of a specific state, which is instead represented by a probability distribution over all possible values. This article discusses the advantages and disadvantages of HMMs in msa and presents algorithms for calculating an HMM and the conditions for producing the best HMM.

  14. Analysis and application of opinion model with multiple topic interactions.

    Xiong, Fei; Liu, Yun; Wang, Liang; Wang, Ximeng

    2017-08-01

    To reveal heterogeneous behaviors of opinion evolution in different scenarios, we propose an opinion model with topic interactions. Individual opinions and topic features are represented by a multidimensional vector. We measure an agent's action towards a specific topic by the product of opinion and topic feature. When pairs of agents interact for a topic, their actions are introduced to opinion updates with bounded confidence. Simulation results show that a transition from a disordered state to a consensus state occurs at a critical point of the tolerance threshold, which depends on the opinion dimension. The critical point increases as the dimension of opinions increases. Multiple topics promote opinion interactions and lead to the formation of macroscopic opinion clusters. In addition, more topics accelerate the evolutionary process and weaken the effect of network topology. We use two sets of large-scale real data to evaluate the model, and the results prove its effectiveness in characterizing a real evolutionary process. Our model achieves high performance in individual action prediction and even outperforms state-of-the-art methods. Meanwhile, our model has much smaller computational complexity. This paper provides a demonstration for possible practical applications of theoretical opinion dynamics.

  15. Subspace identification of Hammer stein models using support vector machines

    Al-Dhaifallah, Mujahed

    2011-01-01

    System identification is the art of finding mathematical tools and algorithms that build an appropriate mathematical model of a system from measured input and output data. Hammerstein model, consisting of a memoryless nonlinearity followed by a dynamic linear element, is often a good trade-off as it can represent some dynamic nonlinear systems very accurately, but is nonetheless quite simple. Moreover, the extensive knowledge about LTI system representations can be applied to the dynamic linear block. On the other hand, finding an effective representation for the nonlinearity is an active area of research. Recently, support vector machines (SVMs) and least squares support vector machines (LS-SVMs) have demonstrated powerful abilities in approximating linear and nonlinear functions. In contrast with other approximation methods, SVMs do not require a-priori structural information. Furthermore, there are well established methods with guaranteed convergence (ordinary least squares, quadratic programming) for fitting LS-SVMs and SVMs. The general objective of this research is to develop new subspace algorithms for Hammerstein systems based on SVM regression.

  16. Mobile Application Identification based on Hidden Markov Model

    Yang Xinyan

    2018-01-01

    Full Text Available With the increasing number of mobile applications, there has more challenging network management tasks to resolve. Users also face security issues of the mobile Internet application when enjoying the mobile network resources. Identifying applications that correspond to network traffic can help network operators effectively perform network management. The existing mobile application recognition technology presents new challenges in extensibility and applications with encryption protocols. For the existing mobile application recognition technology, there are two problems, they can not recognize the application which using the encryption protocol and their scalability is poor. In this paper, a mobile application identification method based on Hidden Markov Model(HMM is proposed to extract the defined statistical characteristics from different network flows generated when each application starting. According to the time information of different network flows to get the corresponding time series, and then for each application to be identified separately to establish the corresponding HMM model. Then, we use 10 common applications to test the method proposed in this paper. The test results show that the mobile application recognition method proposed in this paper has a high accuracy and good generalization ability.

  17. Skin image illumination modeling and chromophore identification for melanoma diagnosis

    Liu, Zhao; Zerubia, Josiane

    2015-05-01

    The presence of illumination variation in dermatological images has a negative impact on the automatic detection and analysis of cutaneous lesions. This paper proposes a new illumination modeling and chromophore identification method to correct lighting variation in skin lesion images, as well as to extract melanin and hemoglobin concentrations of human skin, based on an adaptive bilateral decomposition and a weighted polynomial curve fitting, with the knowledge of a multi-layered skin model. Different from state-of-the-art approaches based on the Lambert law, the proposed method, considering both specular reflection and diffuse reflection of the skin, enables us to address highlight and strong shading effects usually existing in skin color images captured in an uncontrolled environment. The derived melanin and hemoglobin indices, directly relating to the pathological tissue conditions, tend to be less influenced by external imaging factors and are more efficient in describing pigmentation distributions. Experiments show that the proposed method gave better visual results and superior lesion segmentation, when compared to two other illumination correction algorithms, both designed specifically for dermatological images. For computer-aided diagnosis of melanoma, sensitivity achieves 85.52% when using our chromophore descriptors, which is 8~20% higher than those derived from other color descriptors. This demonstrates the benefit of the proposed method for automatic skin disease analysis.

  18. Talent identification model for sprinter using discriminant factor

    Kusnanik, N. W.; Hariyanto, A.; Herdyanto, Y.; Satia, A.

    2018-01-01

    The main purpose of this study was to identify young talented sprinter using discriminant factor. The research was conducted in 3 steps including item pool, screening of item pool, and trial of instruments at the small and big size of samples. 315 male elementary school students participated in this study with mean age of 11-13 years old. Data were collected by measuring anthropometry (standing height, sitting height, body mass, and leg length); testing physical fitness (40m sprint for speed, shuttle run for agility, standing broad jump for power, multistage fitness test for endurance). Data were analyzed using discriminant factor. The result of this study found that there were 5 items that selected as an instrument to identify young talented sprinter: sitting height, body mass, leg length, sprint 40m, and multistage fitness test. Model of Discriminant for talent identification in sprinter was D = -24,497 + (0,155 sitting height) + (0,080 body mass) + (0,148 leg length) + (-1,225 Sprint 40m) + (0,563 MFT). The conclusion of this study: instrument tests that have been selected and discriminant model that have been found can be applied to identify young talented as a sprinter.

  19. Experimental identification and mathematical modeling of viscoplastic material behavior

    Haupt, P.; Lion, A.

    1995-03-01

    Uniaxial torsion and biaxial torsion-tension experiments on thin-walled tubes were carried out to investigate the viscoplastic behavior of stainless steel XCrNi18.9. A series of monotonic tests under strain and stress control shows nonlinear rate dependence and suggests the existence of equilibrium states, which are asymptotically approached during relaxation and creep processes. Strain controlled cyclic experiments display various hardening and softening phenomena that depend on strain amplitude and mean strain. All experiments indicate that the equilibrium states within the material depend on the history of the input process, whereas the history-dependence of the relaxation and creep behavior appears less significant. From the experiments the design of a constitutive model of viscoplasticity is motivated: The basic assumption is a decomposition of the total stress into an equilibrium stress and a non-equilibrium overstress: At constant strain, the overstress relaxes to zero, where the relaxation time depends on the overstress in order to account for the nonlinear rate-dependence. The equilibrium stress is assumed to be a rate independent functional of the total strain history. Classical plasticity is utilized with a kinematic hardening rule of the Armstrong-Frederick type. In order to incorporate the amplitude-dependent hardening and softening behavior, a generalized arc length representation is applied [14]. The introduction of an additional kinematic hardening variable facilitates consideration of additional hardening effects resulting from the non-radiality of the input process. Apart from the common yield and loading criterion of classical plasticity, the proposed constitutive model does not contain any further distinction of different cases. The experimental data are sufficient to identify the material parameters of the constitutive model. The results of the identification procedure demonstrate the ability of the model to represent the observed phenomena

  20. Using the Domain Identification Model to Study Major and Career Decision-Making Processes

    Tendhar, Chosang; Singh, Kusum; Jones, Brett D.

    2018-01-01

    The purpose of this study was to examine the extent to which (1) a domain identification model could be used to predict students' engineering major and career intentions and (2) the MUSIC Model of Motivation components could be used to predict domain identification. The data for this study were collected from first-year engineering students. We…

  1. Student Identification with Business Education Models: Measurement and Relationship to Educational Outcomes

    Halbesleben, Jonathon R. B.; Wheeler, Anthony R.

    2009-01-01

    Although management scholars have provided a variety of metaphors to describe the role of students in management courses, researchers have yet to explore students' identification with the models and how they are linked to educational outcomes. This article develops a measurement tool for students' identification with business education models and…

  2. Identification of Civil Engineering Structures using Multivariate ARMAV and RARMAV Models

    Kirkegaard, Poul Henning; Andersen, P.; Brincker, Rune

    This paper presents how to make system identification of civil engineering structures using multivariate auto-regressive moving-average vector (ARMAV) models. Further, the ARMAV technique is extended to a recursive technique (RARMAV). The ARMAV model is used to identify measured stationary data....... The results show the usefulness of the approaches for identification of civil engineering structures excited by natural excitation...

  3. Integrating multiple distribution models to guide conservation efforts of an endangered toad

    Treglia, Michael L.; Fisher, Robert N.; Fitzgerald, Lee A.

    2015-01-01

    Species distribution models are used for numerous purposes such as predicting changes in species’ ranges and identifying biodiversity hotspots. Although implications of distribution models for conservation are often implicit, few studies use these tools explicitly to inform conservation efforts. Herein, we illustrate how multiple distribution models developed using distinct sets of environmental variables can be integrated to aid in identification sites for use in conservation. We focus on the endangered arroyo toad (Anaxyrus californicus), which relies on open, sandy streams and surrounding floodplains in southern California, USA, and northern Baja California, Mexico. Declines of the species are largely attributed to habitat degradation associated with vegetation encroachment, invasive predators, and altered hydrologic regimes. We had three main goals: 1) develop a model of potential habitat for arroyo toads, based on long-term environmental variables and all available locality data; 2) develop a model of the species’ current habitat by incorporating recent remotely-sensed variables and only using recent locality data; and 3) integrate results of both models to identify sites that may be employed in conservation efforts. We used a machine learning technique, Random Forests, to develop the models, focused on riparian zones in southern California. We identified 14.37% and 10.50% of our study area as potential and current habitat for the arroyo toad, respectively. Generally, inclusion of remotely-sensed variables reduced modeled suitability of sites, thus many areas modeled as potential habitat were not modeled as current habitat. We propose such sites could be made suitable for arroyo toads through active management, increasing current habitat by up to 67.02%. Our general approach can be employed to guide conservation efforts of virtually any species with sufficient data necessary to develop appropriate distribution models.

  4. Multiple system responses program: Identification of concerns related to a number of specific regulatory issues

    Murphy, G.A.; Casada, M.L.; Palko, L.E.; Roberts, M.W.

    1989-10-01

    This document describes the activities and results of the Multiple System Responses (MSR) program conducted by the Nuclear Operations Analysis Center (NOAC) at Oak Ridge National Laboratory (ORNL). The objective of the MSR program was to gather and review documentation for several unresolved safety issues and related programs of interest, and from that documentation, describe any additional potential safety concerns. The MSR program provides information that will aid the NRC staff in making an assessment of the relative safety significance of the concerns through the established prioritization process. Judgments were not made regarding the validity of the concerns expressed by others. Rather, the concerns were documented and potential safety issues were developed and defined as specifically as possible. Twenty-one potential safety issues were developed from the documented concerns. Additional information was gathered to support the NRC efforts in reviewing these issues for prioritization. 73 refs., 2 tabs

  5. Multiple pathways to identification: exploring the multidimensionality of academic identity formation in ethnic minority males.

    Matthews, Jamaal S

    2014-04-01

    Empirical trends denote the academic underachievement of ethnic minority males across various academic domains. Identity-based explanations for this persistent phenomenon describe ethnic minority males as disidentified with academics, alienated, and oppositional. The present work interrogates these theoretical explanations and empirically substantiates a multidimensional lens for discussing academic identity formation within 330 African American and Latino early-adolescent males. Both hierarchical and iterative person-centered methods were utilized and reveal 5 distinct profiles derived from 6 dimensions of academic identity. These profiles predict self-reported classroom grades, mastery orientation, and self-handicapping in meaningful and varied ways. The results demonstrate multiple pathways to motivation and achievement, challenging previous oversimplified stereotypes of marginalized males. This exploratory study triangulates unique interpersonal and intrapersonal attributes for promoting healthy identity development and academic achievement among ethnic minority adolescent males.

  6. Direction of Effects in Multiple Linear Regression Models.

    Wiedermann, Wolfgang; von Eye, Alexander

    2015-01-01

    Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. The present contribution extends the direction of dependence methodology to a multiple linear regression setting by analyzing distributional properties of residuals of competing multiple regression models. It is shown that, under certain conditions, the third central moments of estimated regression residuals can be used to decide upon direction of effects. In addition, three different approaches for statistical inference are discussed: a combined D'Agostino normality test, a skewness difference test, and a bootstrap difference test. Type I error and power of the procedures are assessed using Monte Carlo simulations, and an empirical example is provided for illustrative purposes. In the discussion, issues concerning the quality of psychological data, possible extensions of the proposed methods to the fourth central moment of regression residuals, and potential applications are addressed.

  7. Investigating multiple solutions in the constrained minimal supersymmetric standard model

    Allanach, B.C. [DAMTP, CMS, University of Cambridge,Wilberforce Road, Cambridge, CB3 0HA (United Kingdom); George, Damien P. [DAMTP, CMS, University of Cambridge,Wilberforce Road, Cambridge, CB3 0HA (United Kingdom); Cavendish Laboratory, University of Cambridge,JJ Thomson Avenue, Cambridge, CB3 0HE (United Kingdom); Nachman, Benjamin [SLAC, Stanford University,2575 Sand Hill Rd, Menlo Park, CA 94025 (United States)

    2014-02-07

    Recent work has shown that the Constrained Minimal Supersymmetric Standard Model (CMSSM) can possess several distinct solutions for certain values of its parameters. The extra solutions were not previously found by public supersymmetric spectrum generators because fixed point iteration (the algorithm used by the generators) is unstable in the neighbourhood of these solutions. The existence of the additional solutions calls into question the robustness of exclusion limits derived from collider experiments and cosmological observations upon the CMSSM, because limits were only placed on one of the solutions. Here, we map the CMSSM by exploring its multi-dimensional parameter space using the shooting method, which is not subject to the stability issues which can plague fixed point iteration. We are able to find multiple solutions where in all previous literature only one was found. The multiple solutions are of two distinct classes. One class, close to the border of bad electroweak symmetry breaking, is disfavoured by LEP2 searches for neutralinos and charginos. The other class has sparticles that are heavy enough to evade the LEP2 bounds. Chargino masses may differ by up to around 10% between the different solutions, whereas other sparticle masses differ at the sub-percent level. The prediction for the dark matter relic density can vary by a hundred percent or more between the different solutions, so analyses employing the dark matter constraint are incomplete without their inclusion.

  8. FDRAnalysis: a tool for the integrated analysis of tandem mass spectrometry identification results from multiple search engines.

    Wedge, David C; Krishna, Ritesh; Blackhurst, Paul; Siepen, Jennifer A; Jones, Andrew R; Hubbard, Simon J

    2011-04-01

    Confident identification of peptides via tandem mass spectrometry underpins modern high-throughput proteomics. This has motivated considerable recent interest in the postprocessing of search engine results to increase confidence and calculate robust statistical measures, for example through the use of decoy databases to calculate false discovery rates (FDR). FDR-based analyses allow for multiple testing and can assign a single confidence value for both sets and individual peptide spectrum matches (PSMs). We recently developed an algorithm for combining the results from multiple search engines, integrating FDRs for sets of PSMs made by different search engine combinations. Here we describe a web-server and a downloadable application that makes this routinely available to the proteomics community. The web server offers a range of outputs including informative graphics to assess the confidence of the PSMs and any potential biases. The underlying pipeline also provides a basic protein inference step, integrating PSMs into protein ambiguity groups where peptides can be matched to more than one protein. Importantly, we have also implemented full support for the mzIdentML data standard, recently released by the Proteomics Standards Initiative, providing users with the ability to convert native formats to mzIdentML files, which are available to download.

  9. A Nonlinear Ship Manoeuvering Model: Identification and adaptive control with experiments for a model ship

    Roger Skjetne

    2004-01-01

    Full Text Available Complete nonlinear dynamic manoeuvering models of ships, with numerical values, are hard to find in the literature. This paper presents a modeling, identification, and control design where the objective is to manoeuver a ship along desired paths at different velocities. Material from a variety of references have been used to describe the ship model, its difficulties, limitations, and possible simplifications for the purpose of automatic control design. The numerical values of the parameters in the model is identified in towing tests and adaptive manoeuvering experiments for a small ship in a marine control laboratory.

  10. Characterising and modelling regolith stratigraphy using multiple geophysical techniques

    Thomas, M.; Cremasco, D.; Fotheringham, T.; Hatch, M. A.; Triantifillis, J.; Wilford, J.

    2013-12-01

    Regolith is the weathered, typically mineral-rich layer from fresh bedrock to land surface. It encompasses soil (A, E and B horizons) that has undergone pedogenesis. Below is the weathered C horizon that retains at least some of the original rocky fabric and structure. At the base of this is the lower regolith boundary of continuous hard bedrock (the R horizon). Regolith may be absent, e.g. at rocky outcrops, or may be many 10's of metres deep. Comparatively little is known about regolith, and critical questions remain regarding composition and characteristics - especially deeper where the challenge of collecting reliable data increases with depth. In Australia research is underway to characterise and map regolith using consistent methods at scales ranging from local (e.g. hillslope) to continental scales. These efforts are driven by many research needs, including Critical Zone modelling and simulation. Pilot research in South Australia using digitally-based environmental correlation techniques modelled the depth to bedrock to 9 m for an upland area of 128 000 ha. One finding was the inability to reliably model local scale depth variations over horizontal distances of 2 - 3 m and vertical distances of 1 - 2 m. The need to better characterise variations in regolith to strengthen models at these fine scales was discussed. Addressing this need, we describe high intensity, ground-based multi-sensor geophysical profiling of three hillslope transects in different regolith-landscape settings to characterise fine resolution (i.e. a number of frequencies; multiple frequency, multiple coil electromagnetic induction; and high resolution resistivity. These were accompanied by georeferenced, closely spaced deep cores to 9 m - or to core refusal. The intact cores were sub-sampled to standard depths and analysed for regolith properties to compile core datasets consisting of: water content; texture; electrical conductivity; and weathered state. After preprocessing (filtering, geo

  11. Applying the Team Identification-Social Psychological Health Model to older sport fans.

    Wann, Daniel L; Rogers, Kelly; Dooley, Keith; Foley, Mary

    2011-01-01

    According to the Team Identification-Social Psychological Health Model (Wann, 2006b), team identification and social psychological health should be positively correlated because identification leads to important social connections which, in turn, facilitate well-being. Although past research substantiates the hypothesized positive relationship between team identification and well-being, earlier studies focused solely on college student populations. The current study extended past work in this area by investigating the team identification/well-being relationship among older sport fans. A sample of older adults (N = 96; M age = 70.82) completed scales assessing demographics, identification with a local college basketball team, and measures of social psychological well-being. As hypothesized, team identification accounted for a significant proportion of unique variance in two measures of social psychological health (collective self-esteem and loneliness).

  12. Interaction of multiple biomimetic antimicrobial polymers with model bacterial membranes

    Baul, Upayan, E-mail: upayanb@imsc.res.in; Vemparala, Satyavani, E-mail: vani@imsc.res.in [The Institute of Mathematical Sciences, C.I.T. Campus, Taramani, Chennai 600113 (India); Kuroda, Kenichi, E-mail: kkuroda@umich.edu [Department of Biologic and Materials Sciences, University of Michigan School of Dentistry, Ann Arbor, Michigan 48109 (United States)

    2014-08-28

    Using atomistic molecular dynamics simulations, interaction of multiple synthetic random copolymers based on methacrylates on prototypical bacterial membranes is investigated. The simulations show that the cationic polymers form a micellar aggregate in water phase and the aggregate, when interacting with the bacterial membrane, induces clustering of oppositely charged anionic lipid molecules to form clusters and enhances ordering of lipid chains. The model bacterial membrane, consequently, develops lateral inhomogeneity in membrane thickness profile compared to polymer-free system. The individual polymers in the aggregate are released into the bacterial membrane in a phased manner and the simulations suggest that the most probable location of the partitioned polymers is near the 1-palmitoyl-2-oleoyl-phosphatidylglycerol (POPG) clusters. The partitioned polymers preferentially adopt facially amphiphilic conformations at lipid-water interface, despite lacking intrinsic secondary structures such as α-helix or β-sheet found in naturally occurring antimicrobial peptides.

  13. The intergenerational multiple deficit model and the case of dyslexia

    Elsje evan Bergen

    2014-06-01

    Full Text Available Which children go on to develop dyslexia? Since dyslexia has a multifactorial aetiology, this question can be restated as: What are the factors that put children at high risk for developing dyslexia? It is argued that a useful theoretical framework to address this question is Pennington’s (2006 multiple deficit model (MDM. This model replaces models that attribute dyslexia to a single underlying cause. Subsequently, the generalist genes hypothesis for learning (disabilities (Plomin & Kovas, 2005 is described and integrated with the MDM. Finally, findings are presented from a longitudinal study with children at family risk for dyslexia. Such studies can contribute to testing and specifying the MDM. In this study, risk factors at both the child and family level were investigated. This led to the proposed intergenerational MDM, in which both parents confer liability via intertwined genetic and environmental pathways. Future scientific directions are discussed to investigate parent-offspring resemblance and transmission patterns, which will shed new light on disorder aetiology.

  14. An Advanced N -body Model for Interacting Multiple Stellar Systems

    Brož, Miroslav [Astronomical Institute of the Charles University, Faculty of Mathematics and Physics, V Holešovičkách 2, CZ-18000 Praha 8 (Czech Republic)

    2017-06-01

    We construct an advanced model for interacting multiple stellar systems in which we compute all trajectories with a numerical N -body integrator, namely the Bulirsch–Stoer from the SWIFT package. We can then derive various observables: astrometric positions, radial velocities, minima timings (TTVs), eclipse durations, interferometric visibilities, closure phases, synthetic spectra, spectral energy distribution, and even complete light curves. We use a modified version of the Wilson–Devinney code for the latter, in which the instantaneous true phase and inclination of the eclipsing binary are governed by the N -body integration. If all of these types of observations are at one’s disposal, a joint χ {sup 2} metric and an optimization algorithm (a simplex or simulated annealing) allow one to search for a global minimum and construct very robust models of stellar systems. At the same time, our N -body model is free from artifacts that may arise if mutual gravitational interactions among all components are not self-consistently accounted for. Finally, we present a number of examples showing dynamical effects that can be studied with our code and we discuss how systematic errors may affect the results (and how to prevent this from happening).

  15. Negative binomial models for abundance estimation of multiple closed populations

    Boyce, Mark S.; MacKenzie, Darry I.; Manly, Bryan F.J.; Haroldson, Mark A.; Moody, David W.

    2001-01-01

    Counts of uniquely identified individuals in a population offer opportunities to estimate abundance. However, for various reasons such counts may be burdened by heterogeneity in the probability of being detected. Theoretical arguments and empirical evidence demonstrate that the negative binomial distribution (NBD) is a useful characterization for counts from biological populations with heterogeneity. We propose a method that focuses on estimating multiple populations by simultaneously using a suite of models derived from the NBD. We used this approach to estimate the number of female grizzly bears (Ursus arctos) with cubs-of-the-year in the Yellowstone ecosystem, for each year, 1986-1998. Akaike's Information Criteria (AIC) indicated that a negative binomial model with a constant level of heterogeneity across all years was best for characterizing the sighting frequencies of female grizzly bears. A lack-of-fit test indicated the model adequately described the collected data. Bootstrap techniques were used to estimate standard errors and 95% confidence intervals. We provide a Monte Carlo technique, which confirms that the Yellowstone ecosystem grizzly bear population increased during the period 1986-1998.

  16. A diagnostic tree model for polytomous responses with multiple strategies.

    Ma, Wenchao

    2018-04-23

    Constructed-response items have been shown to be appropriate for cognitively diagnostic assessments because students' problem-solving procedures can be observed, providing direct evidence for making inferences about their proficiency. However, multiple strategies used by students make item scoring and psychometric analyses challenging. This study introduces the so-called two-digit scoring scheme into diagnostic assessments to record both students' partial credits and their strategies. This study also proposes a diagnostic tree model (DTM) by integrating the cognitive diagnosis models with the tree model to analyse the items scored using the two-digit rubrics. Both convergent and divergent tree structures are considered to accommodate various scoring rules. The MMLE/EM algorithm is used for item parameter estimation of the DTM, and has been shown to provide good parameter recovery under varied conditions in a simulation study. A set of data from TIMSS 2007 mathematics assessment is analysed to illustrate the use of the two-digit scoring scheme and the DTM. © 2018 The British Psychological Society.

  17. A minimal model for multiple epidemics and immunity spreading.

    Kim Sneppen

    Full Text Available Pathogens and parasites are ubiquitous in the living world, being limited only by availability of suitable hosts. The ability to transmit a particular disease depends on competing infections as well as on the status of host immunity. Multiple diseases compete for the same resource and their fate is coupled to each other. Such couplings have many facets, for example cross-immunization between related influenza strains, mutual inhibition by killing the host, or possible even a mutual catalytic effect if host immunity is impaired. We here introduce a minimal model for an unlimited number of unrelated pathogens whose interaction is simplified to simple mutual exclusion. The model incorporates an ongoing development of host immunity to past diseases, while leaving the system open for emergence of new diseases. The model exhibits a rich dynamical behavior with interacting infection waves, leaving broad trails of immunization in the host population. This obtained immunization pattern depends only on the system size and on the mutation rate that initiates new diseases.

  18. A minimal model for multiple epidemics and immunity spreading.

    Sneppen, Kim; Trusina, Ala; Jensen, Mogens H; Bornholdt, Stefan

    2010-10-18

    Pathogens and parasites are ubiquitous in the living world, being limited only by availability of suitable hosts. The ability to transmit a particular disease depends on competing infections as well as on the status of host immunity. Multiple diseases compete for the same resource and their fate is coupled to each other. Such couplings have many facets, for example cross-immunization between related influenza strains, mutual inhibition by killing the host, or possible even a mutual catalytic effect if host immunity is impaired. We here introduce a minimal model for an unlimited number of unrelated pathogens whose interaction is simplified to simple mutual exclusion. The model incorporates an ongoing development of host immunity to past diseases, while leaving the system open for emergence of new diseases. The model exhibits a rich dynamical behavior with interacting infection waves, leaving broad trails of immunization in the host population. This obtained immunization pattern depends only on the system size and on the mutation rate that initiates new diseases.

  19. Modeling Pan Evaporation for Kuwait by Multiple Linear Regression

    Almedeij, Jaber

    2012-01-01

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

  20. Characterization of halogenated DBPs and identification of new DBPs trihalomethanols in chlorine dioxide treated drinking water with multiple extractions.

    Han, Jiarui; Zhang, Xiangru; Liu, Jiaqi; Zhu, Xiaohu; Gong, Tingting

    2017-08-01

    Chlorine dioxide (ClO 2 ) is a widely used alternative disinfectant due to its high biocidal efficiency and low-level formation of trihalomethanes and haloacetic acids. A major portion of total organic halogen (TOX), a collective parameter for all halogenated DBPs, formed in ClO 2 -treated drinking water is still unknown. A commonly used pretreatment method for analyzing halogenated DBPs in drinking water is one-time liquid-liquid extraction (LLE), which may lead to a substantial loss of DBPs prior to analysis. In this study, characterization and identification of polar halogenated DBPs in a ClO 2 -treated drinking water sample were conducted by pretreating the sample with multiple extractions. Compared to one-time LLE, the combined four-time LLEs improved the recovery of TOX by 2.3 times. The developmental toxicity of the drinking water sample pretreated with the combined four-time LLEs was 1.67 times higher than that pretreated with one-time LLE. With the aid of ultra-performance liquid chromatography/electrospray ionization-triple quadrupole mass spectrometry, a new group of polar halogenated DBPs, trihalomethanols, were detected in the drinking water sample pretreated with multiple extractions; two of them, trichloromethanol and bromodichloromethanol, were identified with synthesized standard compounds. Moreover, these trihalomethanols were found to be the transformation products of trihalomethanes formed during ClO 2 disinfection. The results indicate that multiple LLEs can significantly improve extraction efficiencies of polar halogenated DBPs and is a better pretreatment method for characterizing and identifying new polar halogenated DBPs in drinking water. Copyright © 2017. Published by Elsevier B.V.

  1. Identification of Multiple Druggable Secondary Sites by Fragment Screening against DC-SIGN.

    Aretz, Jonas; Baukmann, Hannes; Shanina, Elena; Hanske, Jonas; Wawrzinek, Robert; Zapol'skii, Viktor A; Seeberger, Peter H; Kaufmann, Dieter E; Rademacher, Christoph

    2017-06-12

    DC-SIGN is a cell-surface receptor for several pathogenic threats, such as HIV, Ebola virus, or Mycobacterium tuberculosis. Multiple attempts to develop inhibitors of the underlying carbohydrate-protein interactions have been undertaken in the past fifteen years. Still, drug-like DC-SIGN ligands are sparse, which is most likely due to its hydrophilic, solvent-exposed carbohydrate-binding site. Herein, we report on a parallel fragment screening against DC-SIGN applying SPR and a reporter displacement assay, which complements previous screenings using 19 F NMR spectroscopy and chemical fragment microarrays. Hit validation by SPR and 1 H- 15 N HSQC NMR spectroscopy revealed that although no fragment bound in the primary carbohydrate site, five secondary sites are available to harbor drug-like molecules. Building on key interactions of the reported fragment hits, these pockets will be targeted in future approaches to accelerate the development of DC-SIGN inhibitors. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  2. Identification of multiple sites suitable for insertion of foreign genes in herpes simplex virus genomes.

    Morimoto, Tomomi; Arii, Jun; Akashi, Hiroomi; Kawaguchi, Yasushi

    2009-03-01

    Information on sites in HSV genomes at which foreign gene(s) can be inserted without disrupting viral genes or affecting properties of the parental virus are important for basic research on HSV and development of HSV-based vectors for human therapy. The intergenic region between HSV-1 UL3 and UL4 genes has been reported to satisfy the requirements for such an insertion site. The UL3 and UL4 genes are oriented toward the intergenic region and, therefore, insertion of a foreign gene(s) into the region between the UL3 and UL4 polyadenylation signals should not disrupt any viral genes or transcriptional units. HSV-1 and HSV-2 each have more than 10 additional regions structurally similar to the intergenic region between UL3 and UL4. In the studies reported here, it has been demonstrated that insertion of a reporter gene expression cassette into several of the HSV-1 and HSV-2 intergenic regions has no effect on viral growth in cell culture or virulence in mice, suggesting that these multiple intergenic regions may be suitable HSV sites for insertion of foreign genes.

  3. Identification of targets and new developments in the treatment of multiple sclerosis – focus on cladribine

    Clemens Warnke

    2010-06-01

    Full Text Available Clemens Warnke1, Heinz Wiendl2, Hans-Peter Hartung1, Olaf Stüve3, Bernd C Kieseier11Department of Neurology, Heinrich-Heine University Düsseldorf, Germany; 2Department of Neurology – Inflammatory Disorders of the Nervous System and Neurooncology, University of Münster, Germany; 3Department of Neurology, Dallas VA Medical Center and UT Southwestern Medical Center, Dallas, Texas, USAAbstract: Orally available disease-modifying drugs for relapsing-remitting multiple sclerosis (MS represent an unmet need for this chronic and debilitating disease. Among 5 currently investigated drugs at phase 3 clinical stage, promising efficacy data for fingolimod and oral cladribine have recently been published. However, benefits need to be weighed against the risks to define the role of these compounds within current treatment regimens. In this review, data on the efficacy of a promising compound, oral cladribine, are discussed and balanced with known and anticipated risks in a postmarketing era, and finally gives an outlook on the potential place of this drug in treatment algorithms for MS in the future.Keywords: immunosuppressant, oral drugs, risk–benefit, safety

  4. Identification and characterization of novel multiple bacteriocins produced by Leuconostoc pseudomesenteroides QU 15.

    Sawa, N; Okamura, K; Zendo, T; Himeno, K; Nakayama, J; Sonomoto, K

    2010-07-01

    To characterize novel multiple bacteriocins produced by Leuconostoc pseudomesenteroides QU 15. Leuconostoc pseudomesenteroides QU 15 isolated from Nukadoko (rice bran bed) produced novel bacteriocins. By using three purification steps, four antimicrobial peptides termed leucocin A (ΔC7), leucocin A-QU 15, leucocin Q and leucocin N were purified from the culture supernatant. The amino acid sequences of leucocin A (ΔC7) and leucocin A-QU 15 were identical to that of leucocin A-UAL 187 belonging to class IIa bacteriocins, but leucocin A (ΔC7) was deficient in seven C-terminal residues. Leucocin Q and leucocin N are novel class IId bacteriocins. Moreover, the DNA sequences encoding three bacteriocins, leucocin A-QU 15, leucocin Q and leucocin N were obtained. These bacteriocins including two novel bacteriocins were identified from Leuc. pseudomesenteroides QU 15. They showed similar antimicrobial spectra, but their intensities differed. The C-terminal region of leucocin A-QU 15 was important for its antimicrobial activity. Leucocins Q and N were encoded by adjacent open reading frames (ORFs) in the same operon, but leucocin A-QU 15 was not. These leucocins were produced concomitantly by the same strain. Although the two novel bacteriocins were encoded by adjacent ORFs, a characteristic of class IIb bacteriocins, they did not show synergistic activity. © 2010 The Authors. Journal compilation © 2010 The Society for Applied Microbiology.

  5. Inverse identification of intensity distributions from multiple flux maps in concentrating solar applications

    Erickson, Ben; Petrasch, Jörg

    2012-01-01

    Radiative flux measurements at the focal plane of solar concentrators are typically performed using digital cameras in conjunction with Lambertian targets. To accurately predict flux distributions on arbitrary receiver geometries directional information about the radiation is required. Currently, the directional characteristics of solar concentrating systems are predicted via ray tracing simulations. No direct experimental technique to determine intensities of concentrating solar systems is available. In the current paper, multiple parallel flux measurements at varying distances from the focal plane together with a linear inverse method and Tikhonov regularization are used to identify the directional and spatial intensity distribution at the solution plane. The directional binning feature of an in-house Monte Carlo ray tracing program is used to provide a reference solution. The method has been successfully applied to two-dimensional concentrators, namely parabolic troughs and elliptical troughs using forward Monte Carlo ray tracing simulations that provide the flux maps as well as consistent, associated intensity distribution for validation. In the two-dimensional case, intensity distributions obtained from the inverse method approach the Monte Carlo forward solution. In contrast, the method has not been successful for three dimensional and circular symmetric concentrator geometries.

  6. Decreasing Multicollinearity: A Method for Models with Multiplicative Functions.

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

    1979-01-01

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

  7. System health monitoring using multiple-model adaptive estimation techniques

    Sifford, Stanley Ryan

    Monitoring system health for fault detection and diagnosis by tracking system parameters concurrently with state estimates is approached using a new multiple-model adaptive estimation (MMAE) method. This novel method is called GRid-based Adaptive Parameter Estimation (GRAPE). GRAPE expands existing MMAE methods by using new techniques to sample the parameter space. GRAPE expands on MMAE with the hypothesis that sample models can be applied and resampled without relying on a predefined set of models. GRAPE is initially implemented in a linear framework using Kalman filter models. A more generalized GRAPE formulation is presented using extended Kalman filter (EKF) models to represent nonlinear systems. GRAPE can handle both time invariant and time varying systems as it is designed to track parameter changes. Two techniques are presented to generate parameter samples for the parallel filter models. The first approach is called selected grid-based stratification (SGBS). SGBS divides the parameter space into equally spaced strata. The second approach uses Latin Hypercube Sampling (LHS) to determine the parameter locations and minimize the total number of required models. LHS is particularly useful when the parameter dimensions grow. Adding more parameters does not require the model count to increase for LHS. Each resample is independent of the prior sample set other than the location of the parameter estimate. SGBS and LHS can be used for both the initial sample and subsequent resamples. Furthermore, resamples are not required to use the same technique. Both techniques are demonstrated for both linear and nonlinear frameworks. The GRAPE framework further formalizes the parameter tracking process through a general approach for nonlinear systems. These additional methods allow GRAPE to either narrow the focus to converged values within a parameter range or expand the range in the appropriate direction to track the parameters outside the current parameter range boundary

  8. Systematic identification of yeast cell cycle transcription factors using multiple data sources

    Li Wen-Hsiung

    2008-12-01

    Full Text Available Abstract Background Eukaryotic cell cycle is a complex process and is precisely regulated at many levels. Many genes specific to the cell cycle are regulated transcriptionally and are expressed just before they are needed. To understand the cell cycle process, it is important to identify the cell cycle transcription factors (TFs that regulate the expression of cell cycle-regulated genes. Results We developed a method to identify cell cycle TFs in yeast by integrating current ChIP-chip, mutant, transcription factor binding site (TFBS, and cell cycle gene expression data. We identified 17 cell cycle TFs, 12 of which are known cell cycle TFs, while the remaining five (Ash1, Rlm1, Ste12, Stp1, Tec1 are putative novel cell cycle TFs. For each cell cycle TF, we assigned specific cell cycle phases in which the TF functions and identified the time lag for the TF to exert regulatory effects on its target genes. We also identified 178 novel cell cycle-regulated genes, among which 59 have unknown functions, but they may now be annotated as cell cycle-regulated genes. Most of our predictions are supported by previous experimental or computational studies. Furthermore, a high confidence TF-gene regulatory matrix is derived as a byproduct of our method. Each TF-gene regulatory relationship in this matrix is supported by at least three data sources: gene expression, TFBS, and ChIP-chip or/and mutant data. We show that our method performs better than four existing methods for identifying yeast cell cycle TFs. Finally, an application of our method to different cell cycle gene expression datasets suggests that our method is robust. Conclusion Our method is effective for identifying yeast cell cycle TFs and cell cycle-regulated genes. Many of our predictions are validated by the literature. Our study shows that integrating multiple data sources is a powerful approach to studying complex biological systems.

  9. Aspirin acetylates multiple cellular proteins in HCT-116 colon cancer cells: Identification of novel targets.

    Marimuthu, Srinivasan; Chivukula, Raghavender S V; Alfonso, Lloyd F; Moridani, Majid; Hagen, Fred K; Bhat, G Jayarama

    2011-11-01

    Epidemiological and clinical observations provide consistent evidence that regular intake of aspirin may effectively inhibit the occurrence of epithelial tumors; however, the molecular mechanisms are not completely understood. In the present study, we determined the ability of aspirin to acetylate and post-translationally modify cellular proteins in HCT-116 human colon cancer cells to understand the potential mechanisms by which it may exerts anti-cancer effects. Using anti-acetyl lysine antibodies, here we demonstrate that aspirin causes the acetylation of multiple proteins whose molecular weight ranged from 20 to 200 kDa. The identity of these proteins was determined, using immuno-affinity purification, mass spectrometry and immuno-blotting. A total of 33 cellular proteins were potential targets of aspirin-mediated acetylation, while 16 were identified as common to both the control and aspirin-treated samples. These include enzymes of glycolytic pathway, cytoskeleton proteins, histones, ribosomal and mitochondrial proteins. The glycolytic enzymes which were identified include aldolase, glyceraldehyde-3-phosphate dehydrogenase, enolase, pyruvate kinase M2, and lactate dehydrogenase A and B chains. Immunoblotting experiment showed that aspirin also acetylated glucose-6-phosphate dehydrogenase and transketolase, both enzymes of pentose phosphate pathway involved in ribonucleotide biosynthesis. In vitro assays of these enzymes revealed that aspirin did not affect pyruvate kinase and lactate dehydrogenase activity; however, it decreased glucose 6 phosphate dehydrogenase activity. Similar results were also observed in HT-29 human colon cancer cells. Selective inhibition of glucose-6-phosphate dehydrogenase may represent an important mechanism by which aspirin may exert its anti-cancer effects through inhibition of ribonucleotide synthesis.

  10. Identification of Flap Motion Parameters for Vibration Reduction in Helicopter Rotors with Multiple Active Trailing Edge Flaps

    Uğbreve;ur Dalli

    2011-01-01

    Full Text Available An active control method utilizing the multiple trailing edge flap configuration for rotorcraft vibration suppression and blade loads control is presented. A comprehensive model for rotor blade with active trailing edge flaps is used to calculate the vibration characteristics, natural frequencies and mode shapes of any complex composite helicopter rotor blade. A computer program is developed to calculate the system response, rotor blade root forces and moments under aerodynamic forcing conditions. Rotor blade system response is calculated using the proposed solution method and the developed program depending on any structural and aerodynamic properties of rotor blades, structural properties of trailing edge flaps and properties of trailing edge flap actuator inputs. Rotor blade loads are determined first on a nominal rotor blade without multiple active trailing edge flaps and then the effects of the active flap motions on the existing rotor blade loads are investigated. Multiple active trailing edge flaps are controlled by using open loop controllers to identify the effects of the actuator signal output properties such as frequency, amplitude and phase on the system response. Effects of using multiple trailing edge flaps on controlling rotor blade vibrations are investigated and some design criteria are determined for the design of trailing edge flap controller that will provide actuator signal outputs to minimize the rotor blade root loads. It is calculated that using the developed active trailing edge rotor blade model, helicopter rotor blade vibrations can be reduced up to 36% of the nominal rotor blade vibrations.

  11. Multiple model analysis with discriminatory data collection (MMA-DDC): A new method for improving measurement selection

    Kikuchi, C.; Ferre, P. A.; Vrugt, J. A.

    2011-12-01

    Hydrologic models are developed, tested, and refined based on the ability of those models to explain available hydrologic data. The optimization of model performance based upon mismatch between model outputs and real world observations has been extensively studied. However, identification of plausible models is sensitive not only to the models themselves - including model structure and model parameters - but also to the location, timing, type, and number of observations used in model calibration. Therefore, careful selection of hydrologic observations has the potential to significantly improve the performance of hydrologic models. In this research, we seek to reduce prediction uncertainty through optimization of the data collection process. A new tool - multiple model analysis with discriminatory data collection (MMA-DDC) - was developed to address this challenge. In this approach, multiple hydrologic models are developed and treated as competing hypotheses. Potential new data are then evaluated on their ability to discriminate between competing hypotheses. MMA-DDC is well-suited for use in recursive mode, in which new observations are continuously used in the optimization of subsequent observations. This new approach was applied to a synthetic solute transport experiment, in which ranges of parameter values constitute the multiple hydrologic models, and model predictions are calculated using likelihood-weighted model averaging. MMA-DDC was used to determine the optimal location, timing, number, and type of new observations. From comparison with an exhaustive search of all possible observation sequences, we find that MMA-DDC consistently selects observations which lead to the highest reduction in model prediction uncertainty. We conclude that using MMA-DDC to evaluate potential observations may significantly improve the performance of hydrologic models while reducing the cost associated with collecting new data.

  12. Shared mental models of integrated care: aligning multiple stakeholder perspectives.

    Evans, Jenna M; Baker, G Ross

    2012-01-01

    Health service organizations and professionals are under increasing pressure to work together to deliver integrated patient care. A common understanding of integration strategies may facilitate the delivery of integrated care across inter-organizational and inter-professional boundaries. This paper aims to build a framework for exploring and potentially aligning multiple stakeholder perspectives of systems integration. The authors draw from the literature on shared mental models, strategic management and change, framing, stakeholder management, and systems theory to develop a new construct, Mental Models of Integrated Care (MMIC), which consists of three types of mental models, i.e. integration-task, system-role, and integration-belief. The MMIC construct encompasses many of the known barriers and enablers to integrating care while also providing a comprehensive, theory-based framework of psychological factors that may influence inter-organizational and inter-professional relations. While the existing literature on integration focuses on optimizing structures and processes, the MMIC construct emphasizes the convergence and divergence of stakeholders' knowledge and beliefs, and how these underlying cognitions influence interactions (or lack thereof) across the continuum of care. MMIC may help to: explain what differentiates effective from ineffective integration initiatives; determine system readiness to integrate; diagnose integration problems; and develop interventions for enhancing integrative processes and ultimately the delivery of integrated care. Global interest and ongoing challenges in integrating care underline the need for research on the mental models that characterize the behaviors of actors within health systems; the proposed framework offers a starting point for applying a cognitive perspective to health systems integration.

  13. A model structure for identification of linear models of the UH-60 helicopter in hover and forward flight

    1995-08-01

    A linear model structure applicable to identification of the UH-60 flight : dynamics in hover and forward flight without rotor-state data is developed. The : structure of the model is determined through consideration of the important : dynamic modes ...

  14. Identification of Ohnolog Genes Originating from Whole Genome Duplication in Early Vertebrates, Based on Synteny Comparison across Multiple Genomes.

    Singh, Param Priya; Arora, Jatin; Isambert, Hervé

    2015-07-01

    Whole genome duplications (WGD) have now been firmly established in all major eukaryotic kingdoms. In particular, all vertebrates descend from two rounds of WGDs, that occurred in their jawless ancestor some 500 MY ago. Paralogs retained from WGD, also coined 'ohnologs' after Susumu Ohno, have been shown to be typically associated with development, signaling and gene regulation. Ohnologs, which amount to about 20 to 35% of genes in the human genome, have also been shown to be prone to dominant deleterious mutations and frequently implicated in cancer and genetic diseases. Hence, identifying ohnologs is central to better understand the evolution of vertebrates and their susceptibility to genetic diseases. Early computational analyses to identify vertebrate ohnologs relied on content-based synteny comparisons between the human genome and a single invertebrate outgroup genome or within the human genome itself. These approaches are thus limited by lineage specific rearrangements in individual genomes. We report, in this study, the identification of vertebrate ohnologs based on the quantitative assessment and integration of synteny conservation between six amniote vertebrates and six invertebrate outgroups. Such a synteny comparison across multiple genomes is shown to enhance the statistical power of ohnolog identification in vertebrates compared to earlier approaches, by overcoming lineage specific genome rearrangements. Ohnolog gene families can be browsed and downloaded for three statistical confidence levels or recompiled for specific, user-defined, significance criteria at http://ohnologs.curie.fr/. In the light of the importance of WGD on the genetic makeup of vertebrates, our analysis provides a useful resource for researchers interested in gaining further insights on vertebrate evolution and genetic diseases.

  15. Model Identification using Continuous Glucose Monitoring Data for Type 1 Diabetes

    Boiroux, Dimitri; Hagdrup, Morten; Mahmoudi, Zeinab

    2016-01-01

    This paper addresses model identification of continuous-discrete nonlinear models for people with type 1 diabetes using sampled data from a continuous glucose monitor (CGM). We compare five identification techniques: least squares, weighted least squares, Huber regression, maximum likelihood...... with extended Kalman filter and maximum likelihood with unscented Kalman filter. We perform the identification on a 24-hour simulation of a stochastic differential equation (SDE) version of the Medtronic Virtual Patient (MVP) model including process and output noise. We compare the fits with the actual CGM......, such as parameter tracking, population modeling and handling of outliers....

  16. Eye Movement Abnormalities in Multiple Sclerosis: Pathogenesis, Modeling, and Treatment

    Alessandro Serra

    2018-02-01

    Full Text Available Multiple sclerosis (MS commonly causes eye movement abnormalities that may have a significant impact on patients’ disability. Inflammatory demyelinating lesions, especially occurring in the posterior fossa, result in a wide range of disorders, spanning from acquired pendular nystagmus (APN to internuclear ophthalmoplegia (INO, among the most common. As the control of eye movements is well understood in terms of anatomical substrate and underlying physiological network, studying ocular motor abnormalities in MS provides a unique opportunity to gain insights into mechanisms of disease. Quantitative measurement and modeling of eye movement disorders, such as INO, may lead to a better understanding of common symptoms encountered in MS, such as Uhthoff’s phenomenon and fatigue. In turn, the pathophysiology of a range of eye movement abnormalities, such as APN, has been clarified based on correlation of experimental model with lesion localization by neuroimaging in MS. Eye movement disorders have the potential of being utilized as structural and functional biomarkers of early cognitive deficit, and possibly help in assessing disease status and progression, and to serve as platform and functional outcome to test novel therapeutic agents for MS. Knowledge of neuropharmacology applied to eye movement dysfunction has guided testing and use of a number of pharmacological agents to treat some eye movement disorders found in MS, such as APN and other forms of central nystagmus.

  17. Probability of identification (POI): a statistical model for the validation of qualitative botanical identification methods

    A qualitative botanical identification method (BIM) is an analytical procedure which returns a binary result (1 = Identified, 0 = Not Identified). A BIM may be used by a buyer, manufacturer, or regulator to determine whether a botanical material being tested is the same as the target (desired) mate...

  18. Identification of goat milk powder by manufacturer using multiple chemical parameters.

    McLeod, Rebecca J; Prosser, Colin G; Wakefield, Joshua W

    2016-02-01

    Concentrations of multiple elements and ratios of stable isotopes of carbon and nitrogen were measured and combined to create a chemical fingerprint of production batches of goat whole milk powder (WMP) produced by different manufacturers. Our objectives were to determine whether or not differences exist in the chemical fingerprint among samples of goat WMP produced at different sites, and assess temporal changes in the chemical fingerprint in product manufactured at one site. In total, 58 samples of goat WMP were analyzed by inductively coupled plasma-mass spectrometry as well as isotope ratio mass spectrometry and a suite of 13 elements (Li, Na, Mg, K, Ca, Mn, Cu, Zn, Rb, Sr, Mo, Cs, and Ba), δ(13)C, and δ(15)N selected to create the chemical fingerprint. Differences in the chemical fingerprint of samples between sites and over time were assessed using principal components analysis and canonical analysis of principal coordinates. Differences in the chemical fingerprints of samples between production sites provided a classification success rate (leave-one-out classification) of 98.1%, providing a basis for using the approach to test the authenticity of product manufactured at a site. Within one site, the chemical fingerprint of samples produced at the beginning of the production season differed from those produced in the middle and late season, driven predominantly by lower concentrations of Na, Mg, K, Mn, and Rb, and higher concentrations of Ba and Cu. This observed temporal variability highlights the importance of obtaining samples from throughout the season to ensure a representative chemical fingerprint is obtained for goat WMP from a single manufacturing site. The reconstitution and spray drying of samples from one manufacturer by the other manufacturer enabled the relative influence of the manufacturing process on the chemical fingerprint to be examined. It was found that such reprocessing altered the chemical fingerprint, although the degree of alteration

  19. One for All and All for One: Using Multiple Identification Theory Simulations to Build Cooperative Attitudes and Behaviors in a Middle Eastern Conflict Scenario

    Williams, Robert Howard; Williams, Alexander Jonathan

    2010-01-01

    The authors previously developed multiple identification theory (MIT) as a system of simulation game design intended to promote attitude change. The present study further tests MIT's effectiveness. The authors created a game (CULTURE & CREED) via MIT as a complex simulation of Middle Eastern conflict resolution, designed to change attitudes…

  20. Identification of Flap Motion Parameters for Vibration Reduction in Helicopter Rotors with Multiple Active Trailing Edge Flaps

    Dalli, Uğbreve;ur; Yüksel, Şcedilefaatdin

    2011-01-01

    An active control method utilizing the multiple trailing edge flap configuration for rotorcraft vibration suppression and blade loads control is presented. A comprehensive model for rotor blade with active trailing edge flaps is used to calculate the vibration characteristics, natural frequencies and mode shapes of any complex composite helicopter rotor blade. A computer program is developed to calculate the system response, rotor blade root forces and moments under aerodynamic forcing condit...

  1. Simulacioni model višelamelastih frikcionih sklopova / Simulation model of multiple plate friction clutches and brakes

    Aleksandar Grkić

    2009-01-01

    Full Text Available Višelamelasti frikcioni sklopovi koriste se za promenu stepena prenosa u planetarnim menjačkim prenosnicima motornih vozila. Razvijeni simulacioni model frikcione spojnice i kočnice omogućava simulaciju rada menjačkog prenosnika pri promeni stepena prenosa. Primenom razvijenog modela moguće je na bazi simulacije analizirati prelazni proces pri promeni stepena prenosa i obezbediti identifikaciju relevantnih parametara bez izrade većeg broja fizičkih prototipova. Na taj način obezbeđuje se smanjenje troškova i skraćenje procesa razvoja novih prenosnika snage, uz poboljšanje upotrebnog kvaliteta. Simulacioni model može da se koristi i pri razvoju upravljačkog sistema menjačkog prenosnika za definisanje potrebnih karakteristika njegovih komponenata. / Multiple plate friction clutches and brakes are used for gear shifting within planetary gear trains of motor vehicles. The developed simulation model of the friction clutch and brake enables the simulation and the analysis of the planetary gear train transitional processes during gear shifting and provides identification of relevant parameters without making numerous physical prototypes. Costs are thus reduced and time for developing new gear trains shortened, while the product quality is increased. The simulation model can be use additionally in developing steering systems of planetary gear trains for defining characteristics of their components.

  2. Identification of multiple magnetizations of the Ediacaran strata in South China

    Jing, Xianqing; Yang, Zhenyu; Tong, Yabo; Wang, Heng; Xu, Yingchao

    2018-01-01

    multiple generations of oil and gas in the Ediacaran and Cambrian strata are suggested as the main mechanism for remagnetization. Despite the pervasive Silurian remagnetization of the Ediacaran strata, most of the HTC from the thick-bedded dolostone of Doushantuo Formation Member 3 at the JLWS section appears to carry a primary remanence, because its pole differs from other poles of South China and the results pass both the fold and reversal tests. The relatively low-geothermic conditions and the absence of both hydrocarbon and smectite/illite explain why most results from the Doushantuo Member 3 of JLWS section were not affected by the Silurian remagnetization. This new Ediacaran pole supersedes the previous suspected remagnetized poles, which can be used to constrain the palaeoposition of South China both in Rodinia and Gondwana.

  3. Nonlinear modeling and identification of a DC motor for bidirectional operation with real time experiments

    Kara, Tolgay; Eker, Ilyas

    2004-01-01

    Modeling and identification of mechanical systems constitute an essential stage in practical control design and applications. Controllers commanding systems that operate at varying conditions or require high precision operation raise the need for a nonlinear approach in modeling and identification. Most mechanical systems used in industry are composed of masses moving under the action of position and velocity dependent forces. These forces exhibit nonlinear behavior in certain regions of operation. For a multi-mass rotational system, the nonlinearities, like Coulomb friction and dead zone, significantly influence the system operation when the rotation changes direction. The paper presents nonlinear modeling and identification of a DC motor rotating in two directions together with real time experiments. Linear and nonlinear models for the system are obtained for identification purposes, and the major nonlinearities in the system, such as Coulomb friction and dead zone, are investigated and integrated in the nonlinear model. The Hammerstein nonlinear system approach is used for identification of the nonlinear system model. Online identification of the linear and nonlinear system models is performed using the recursive least squares method. Results of the real time experiments are graphically and numerically presented, and the advantages of the nonlinear identification approach are revealed

  4. Mathematical Modeling of Loop Heat Pipes with Multiple Capillary Pumps and Multiple Condensers. Part 1; Stead State Stimulations

    Hoang, Triem T.; OConnell, Tamara; Ku, Jentung

    2004-01-01

    Loop Heat Pipes (LHPs) have proven themselves as reliable and robust heat transport devices for spacecraft thermal control systems. So far, the LHPs in earth-orbit satellites perform very well as expected. Conventional LHPs usually consist of a single capillary pump for heat acquisition and a single condenser for heat rejection. Multiple pump/multiple condenser LHPs have shown to function very well in ground testing. Nevertheless, the test results of a dual pump/condenser LHP also revealed that the dual LHP behaved in a complicated manner due to the interaction between the pumps and condensers. Thus it is redundant to say that more research is needed before they are ready for 0-g deployment. One research area that perhaps compels immediate attention is the analytical modeling of LHPs, particularly the transient phenomena. Modeling a single pump/single condenser LHP is difficult enough. Only a handful of computer codes are available for both steady state and transient simulations of conventional LHPs. No previous effort was made to develop an analytical model (or even a complete theory) to predict the operational behavior of the multiple pump/multiple condenser LHP systems. The current research project offered a basic theory of the multiple pump/multiple condenser LHP operation. From it, a computer code was developed to predict the LHP saturation temperature in accordance with the system operating and environmental conditions.

  5. Covariance approximation for large multivariate spatial data sets with an application to multiple climate model errors

    Sang, Huiyan; Jun, Mikyoung; Huang, Jianhua Z.

    2011-01-01

    This paper investigates the cross-correlations across multiple climate model errors. We build a Bayesian hierarchical model that accounts for the spatial dependence of individual models as well as cross-covariances across different climate models

  6. Encrypted data stream identification using randomness sparse representation and fuzzy Gaussian mixture model

    Zhang, Hong; Hou, Rui; Yi, Lei; Meng, Juan; Pan, Zhisong; Zhou, Yuhuan

    2016-07-01

    The accurate identification of encrypted data stream helps to regulate illegal data, detect network attacks and protect users' information. In this paper, a novel encrypted data stream identification algorithm is introduced. The proposed method is based on randomness characteristics of encrypted data stream. We use a l1-norm regularized logistic regression to improve sparse representation of randomness features and Fuzzy Gaussian Mixture Model (FGMM) to improve identification accuracy. Experimental results demonstrate that the method can be adopted as an effective technique for encrypted data stream identification.

  7. A comparative study of non-parametric models for identification of ...

    However, the frequency response method using random binary signals was good for unpredicted white noise characteristics and considered the best method for non-parametric system identifica-tion. The autoregressive external input (ARX) model was very useful for system identification, but on applicati-on, few input ...

  8. Identification of Characterization Factor for Power System Oscillation Based on Multiple Synchronized Phasor Measurements

    Hashiguchi, Takuhei; Watanabe, Masayuki; Matsushita, Akihiro; Mitani, Yasunori; Saeki, Osamu; Tsuji, Kiichiro; Hojo, Masahide; Ukai, Hiroyuki

    Electric power systems in Japan are composed of remote and distributed location of generators and loads mainly concentrated in large demand areas. The structures having long distance transmission tend to produce heavy power flow with increasing electric power demand. In addition, some independent power producers (IPP) and power producer and suppliers (PPS) are participating in the power generation business, which makes power system dynamics more complex. However, there was little observation as a whole power system. In this paper the authors present a global monitoring system of power system dynamics by using the synchronized phasor measurement of demand side outlets. Phasor Measurement Units (PMU) are synchronized based on the global positioning system (GPS). The purpose of this paper is to show oscillation characteristics and methods for processing original data obtained from PMU after certain power system disturbances triggered by some accidents. This analysis resulted in the observation of the lowest and the second lowest frequency mode. The derivation of eigenvalue with two degree of freedom model brings a monitoring of two oscillation modes. Signal processing based on Wavelet analysis and simulation studies to illustrate the obtained phenomena are demonstrated in detail.

  9. Model Identification for Control of Display Units in Supermarket Refrigeration Systems

    O'Connell, Niamh; Madsen, Henrik; Andersen, Philip Hvidthøft Delff

    in a supermarket refrigeration system. The grey-box modelling approach is adopted, using stochastic differential equations to define the dynamics of the model, combining prior knowledge of the physical system with data-driven modelling. Model identification is performed using the forward selection method...... model can contribute to the extension of the control capabilities of the entire supermarket refrigeration system....

  10. Stabilization of multiple rib fractures in a canine model.

    Huang, Ke-Nan; Xu, Zhi-Fei; Sun, Ju-Xian; Ding, Xin-Yu; Wu, Bin; Li, Wei; Qin, Xiong; Tang, Hua

    2014-12-01

    Operative stabilization is frequently used in the clinical treatment of multiple rib fractures (MRF); however, no ideal material exists for use in this fixation. This study investigates a newly developed biodegradable plate system for the stabilization of MRF. Silk fiber-reinforced polycaprolactone (SF/PCL) plates were developed for rib fracture stabilization and studied using a canine flail chest model. Adult mongrel dogs were divided into three groups: one group received the SF/PCL plates, one group received standard clinical steel plates, and the final group did not undergo operative fracture stabilization (n = 6 for each group). Radiographic, mechanical, and histologic examination was performed to evaluate the effectiveness of the biodegradable material for the stabilization of the rib fractures. No nonunion and no infections were found when using SF-PCL plates. The fracture sites collapsed in the untreated control group, leading to obvious chest wall deformity not encountered in the two groups that underwent operative stabilization. Our experimental study shows that the SF/PCL plate has the biocompatibility and mechanical strength suitable for fixation of MRF and is potentially ideal for the treatment of these injuries. Copyright © 2014 Elsevier Inc. All rights reserved.

  11. Multiplicative multifractal modeling and discrimination of human neuronal activity

    Zheng Yi; Gao Jianbo; Sanchez, Justin C.; Principe, Jose C.; Okun, Michael S.

    2005-01-01

    Understanding neuronal firing patterns is one of the most important problems in theoretical neuroscience. It is also very important for clinical neurosurgery. In this Letter, we introduce a computational procedure to examine whether neuronal firing recordings could be characterized by cascade multiplicative multifractals. By analyzing raw recording data as well as generated spike train data from 3 patients collected in two brain areas, the globus pallidus externa (GPe) and the globus pallidus interna (GPi), we show that the neural firings are consistent with a multifractal process over certain time scale range (t 1 ,t 2 ), where t 1 is argued to be not smaller than the mean inter-spike-interval of neuronal firings, while t 2 may be related to the time that neuronal signals propagate in the major neural branching structures pertinent to GPi and GPe. The generalized dimension spectrum D q effectively differentiates the two brain areas, both intra- and inter-patients. For distinguishing between GPe and GPi, it is further shown that the cascade model is more effective than the methods recently examined by Schiff et al. as well as the Fano factor analysis. Therefore, the methodology may be useful in developing computer aided tools to help clinicians perform precision neurosurgery in the operating room

  12. Multiple models guide strategies for agricultural nutrient reductions

    Scavia, Donald; Kalcic, Margaret; Muenich, Rebecca Logsdon; Read, Jennifer; Aloysius, Noel; Bertani, Isabella; Boles, Chelsie; Confesor, Remegio; DePinto, Joseph; Gildow, Marie; Martin, Jay; Redder, Todd; Robertson, Dale M.; Sowa, Scott P.; Wang, Yu-Chen; Yen, Haw

    2017-01-01

    In response to degraded water quality, federal policy makers in the US and Canada called for a 40% reduction in phosphorus (P) loads to Lake Erie, and state and provincial policy makers in the Great Lakes region set a load-reduction target for the year 2025. Here, we configured five separate SWAT (US Department of Agriculture's Soil and Water Assessment Tool) models to assess load reduction strategies for the agriculturally dominated Maumee River watershed, the largest P source contributing to toxic algal blooms in Lake Erie. Although several potential pathways may achieve the target loads, our results show that any successful pathway will require large-scale implementation of multiple practices. For example, one successful pathway involved targeting 50% of row cropland that has the highest P loss in the watershed with a combination of three practices: subsurface application of P fertilizers, planting cereal rye as a winter cover crop, and installing buffer strips. Achieving these levels of implementation will require local, state/provincial, and federal agencies to collaborate with the private sector to set shared implementation goals and to demand innovation and honest assessments of water quality-related programs, policies, and partnerships.

  13. Calcium Intervention Ameliorates Experimental Model of Multiple Sclerosis

    Dariush Haghmorad

    2014-05-01

    Full Text Available Objective: Multiple sclerosis (MS is the most common inflammatory disease of the CNS. Experimental autoimmune encephalomyelitis (EAE is a widely used model for MS. In the present research, our aim was to test the therapeutic efficacy of Calcium (Ca in an experimental model of MS. Methods: In this study the experiment was done on C57BL/6 mice. EAE was induced using 200 μg of the MOG35-55 peptide emulsified in CFA and injected subcutaneously on day 0 over two flank areas. In addition, 250 ng of pertussis toxin was injected on days 0 and 2. In the treatment group, 30 mg/kg Ca was administered intraperitoneally four times at regular 48 hour intervals. The mice were sacrificed 21 days after EAE induction and blood samples were taken from their hearts. The brains of mice were removed for histological analysis and their isolated splenocytes were cultured. Results: Our results showed that treatment with Ca caused a significant reduction in the severity of the EAE. Histological analysis indicated that there was no plaque in brain sections of Ca treated group of mice whereas 4 ± 1 plaques were detected in brain sections of controls. The density of mononuclear infiltration in the CNS of Ca treated mice was lower than in controls. The serum level of Nitric Oxide in the treatment group was lower than in the control group but was not significant. Moreover, the levels of IFN-γ in cell culture supernatant of splenocytes in treated mice were significantly lower than in the control group. Conclusion: The data indicates that Ca intervention can effectively attenuate EAE progression.

  14. A ¤flexible additive multiplicative hazard model

    Martinussen, T.; Scheike, T. H.

    2002-01-01

    Aalen's additive model; Counting process; Cox regression; Hazard model; Proportional excess harzard model; Time-varying effect......Aalen's additive model; Counting process; Cox regression; Hazard model; Proportional excess harzard model; Time-varying effect...

  15. Identification of mutated driver pathways in cancer using a multi-objective optimization model.

    Zheng, Chun-Hou; Yang, Wu; Chong, Yan-Wen; Xia, Jun-Feng

    2016-05-01

    New-generation high-throughput technologies, including next-generation sequencing technology, have been extensively applied to solve biological problems. As a result, large cancer genomics projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium are producing large amount of rich and diverse data in multiple cancer types. The identification of mutated driver genes and driver pathways from these data is a significant challenge. Genome aberrations in cancer cells can be divided into two types: random 'passenger mutation' and functional 'driver mutation'. In this paper, we introduced a Multi-objective Optimization model based on a Genetic Algorithm (MOGA) to solve the maximum weight submatrix problem, which can be employed to identify driver genes and driver pathways promoting cancer proliferation. The maximum weight submatrix problem defined to find mutated driver pathways is based on two specific properties, i.e., high coverage and high exclusivity. The multi-objective optimization model can adjust the trade-off between high coverage and high exclusivity. We proposed an integrative model by combining gene expression data and mutation data to improve the performance of the MOGA algorithm in a biological context. Copyright © 2016 Elsevier Ltd. All rights reserved.

  16. Source term identification in atmospheric modelling via sparse optimization

    Adam, Lukas; Branda, Martin; Hamburger, Thomas

    2015-04-01

    Inverse modelling plays an important role in identifying the amount of harmful substances released into atmosphere during major incidents such as power plant accidents or volcano eruptions. Another possible application of inverse modelling lies in the monitoring the CO2 emission limits where only observations at certain places are available and the task is to estimate the total releases at given locations. This gives rise to minimizing the discrepancy between the observations and the model predictions. There are two standard ways of solving such problems. In the first one, this discrepancy is regularized by adding additional terms. Such terms may include Tikhonov regularization, distance from a priori information or a smoothing term. The resulting, usually quadratic, problem is then solved via standard optimization solvers. The second approach assumes that the error term has a (normal) distribution and makes use of Bayesian modelling to identify the source term. Instead of following the above-mentioned approaches, we utilize techniques from the field of compressive sensing. Such techniques look for a sparsest solution (solution with the smallest number of nonzeros) of a linear system, where a maximal allowed error term may be added to this system. Even though this field is a developed one with many possible solution techniques, most of them do not consider even the simplest constraints which are naturally present in atmospheric modelling. One of such examples is the nonnegativity of release amounts. We believe that the concept of a sparse solution is natural in both problems of identification of the source location and of the time process of the source release. In the first case, it is usually assumed that there are only few release points and the task is to find them. In the second case, the time window is usually much longer than the duration of the actual release. In both cases, the optimal solution should contain a large amount of zeros, giving rise to the

  17. The Answering Process for Multiple-Choice Questions in Collaborative Learning: A Mathematical Learning Model Analysis

    Nakamura, Yasuyuki; Nishi, Shinnosuke; Muramatsu, Yuta; Yasutake, Koichi; Yamakawa, Osamu; Tagawa, Takahiro

    2014-01-01

    In this paper, we introduce a mathematical model for collaborative learning and the answering process for multiple-choice questions. The collaborative learning model is inspired by the Ising spin model and the model for answering multiple-choice questions is based on their difficulty level. An intensive simulation study predicts the possibility of…

  18. New trends in parameter identification for mathematical models

    Leitão, Antonio; Zubelli, Jorge

    2018-01-01

    The Proceedings volume contains 16 contributions to the IMPA conference “New Trends in Parameter Identification for Mathematical Models”, Rio de Janeiro, Oct 30 – Nov 3, 2017, integrating the “Chemnitz Symposium on Inverse Problems on Tour”.  This conference is part of the “Thematic Program on Parameter Identification in Mathematical Models” organized  at IMPA in October and November 2017. One goal is to foster the scientific collaboration between mathematicians and engineers from the Brazialian, European and Asian communities. Main topics are iterative and variational regularization methods in Hilbert and Banach spaces for the stable approximate solution of ill-posed inverse problems, novel methods for parameter identification in partial differential equations, problems of tomography ,  solution of coupled conduction-radiation problems at high temperatures, and the statistical solution of inverse problems with applications in physics.

  19. Assimilation of concentration measurements for retrieving multiple point releases in atmosphere: A least-squares approach to inverse modelling

    Singh, Sarvesh Kumar; Rani, Raj

    2015-10-01

    The study addresses the identification of multiple point sources, emitting the same tracer, from their limited set of merged concentration measurements. The identification, here, refers to the estimation of locations and strengths of a known number of simultaneous point releases. The source-receptor relationship is described in the framework of adjoint modelling by using an analytical Gaussian dispersion model. A least-squares minimization framework, free from an initialization of the release parameters (locations and strengths), is presented to estimate the release parameters. This utilizes the distributed source information observable from the given monitoring design and number of measurements. The technique leads to an exact retrieval of the true release parameters when measurements are noise free and exactly described by the dispersion model. The inversion algorithm is evaluated using the real data from multiple (two, three and four) releases conducted during Fusion Field Trials in September 2007 at Dugway Proving Ground, Utah. The release locations are retrieved, on average, within 25-45 m of the true sources with the distance from retrieved to true source ranging from 0 to 130 m. The release strengths are also estimated within a factor of three to the true release rates. The average deviations in retrieval of source locations are observed relatively large in two release trials in comparison to three and four release trials.

  20. Exclusive description of multiple production on nuclei in the additive quark model. Multiplicity distributions in interactions with heavy nuclei

    Levchenko, B.B.; Nikolaev, N.N.

    1985-01-01

    In the framework of the additive quark model of multiple production on nuclei we calculate the multiplicity distributions of secondary particles and the correlations between secondary particles in πA and pA interactions with heavy nuclei. We show that intranuclear cascades are responsible for up to 50% of the nuclear increase of the multiplicity of fast particles. We analyze the sensitivity of the multiplicities and their correlations to the choice of the quark-hadronization function. We show that with good accuracy the yield of relativistic secondary particles from heavy and intermediate nuclei depends only on the number N/sub p/ of protons knocked out of the nucleus, and not on the mass number of the nucleus (N/sub p/ scaling)

  1. Comparative study between a QCD inspired model and a multiple diffraction model

    Luna, E.G.S.; Martini, A.F.; Menon, M.J.

    2003-01-01

    A comparative study between a QCD Inspired Model (QCDIM) and a Multiple Diffraction Model (MDM) is presented, with focus on the results for pp differential cross section at √s = 52.8 GeV. It is shown that the MDM predictions are in agreement with experimental data, except for the dip region and that the QCDIM describes only the diffraction peak region. Interpretations in terms of the corresponding eikonals are also discussed. (author)

  2. Recursive Subspace Identification of AUV Dynamic Model under General Noise Assumption

    Zheping Yan

    2014-01-01

    Full Text Available A recursive subspace identification algorithm for autonomous underwater vehicles (AUVs is proposed in this paper. Due to the advantages at handling nonlinearities and couplings, the AUV model investigated here is for the first time constructed as a Hammerstein model with nonlinear feedback in the linear part. To better take the environment and sensor noises into consideration, the identification problem is concerned as an errors-in-variables (EIV one which means that the identification procedure is under general noise assumption. In order to make the algorithm recursively, propagator method (PM based subspace approach is extended into EIV framework to form the recursive identification method called PM-EIV algorithm. With several identification experiments carried out by the AUV simulation platform, the proposed algorithm demonstrates its effectiveness and feasibility.

  3. BUILDING MODEL ANALYSIS APPLICATIONS WITH THE JOINT UNIVERSAL PARAMETER IDENTIFICATION AND EVALUATION OF RELIABILITY (JUPITER) API

    The open-source, public domain JUPITER (Joint Universal Parameter IdenTification and Evaluation of Reliability) API (Application Programming Interface) provides conventions and Fortran-90 modules to develop applications (computer programs) for analyzing process models. The input ...

  4. Parametric identification of the Nomoto generalized model using the apparatus of variational calculus

    Agarkov S.A.

    2015-03-01

    Full Text Available A new approach to the identification of parameters of the Nomoto generalized vessel model has been proposed. The apparatus of classical calculus and the method of least squares have been used

  5. Problem solving based learning model with multiple representations to improve student's mental modelling ability on physics

    Haili, Hasnawati; Maknun, Johar; Siahaan, Parsaoran

    2017-08-01

    Physics is a lessons that related to students' daily experience. Therefore, before the students studying in class formally, actually they have already have a visualization and prior knowledge about natural phenomenon and could wide it themselves. The learning process in class should be aimed to detect, process, construct, and use students' mental model. So, students' mental model agree with and builds in the right concept. The previous study held in MAN 1 Muna informs that in learning process the teacher did not pay attention students' mental model. As a consequence, the learning process has not tried to build students' mental modelling ability (MMA). The purpose of this study is to describe the improvement of students' MMA as a effect of problem solving based learning model with multiple representations approach. This study is pre experimental design with one group pre post. It is conducted in XI IPA MAN 1 Muna 2016/2017. Data collection uses problem solving test concept the kinetic theory of gasses and interview to get students' MMA. The result of this study is clarification students' MMA which is categorized in 3 category; High Mental Modelling Ability (H-MMA) for 7Mental Modelling Ability (M-MMA) for 3Mental Modelling Ability (L-MMA) for 0 ≤ x ≤ 3 score. The result shows that problem solving based learning model with multiple representations approach can be an alternative to be applied in improving students' MMA.

  6. Nonlinear System Identification via Basis Functions Based Time Domain Volterra Model

    Yazid Edwar

    2014-07-01

    Full Text Available This paper proposes basis functions based time domain Volterra model for nonlinear system identification. The Volterra kernels are expanded by using complex exponential basis functions and estimated via genetic algorithm (GA. The accuracy and practicability of the proposed method are then assessed experimentally from a scaled 1:100 model of a prototype truss spar platform. Identification results in time and frequency domain are presented and coherent functions are performed to check the quality of the identification results. It is shown that results between experimental data and proposed method are in good agreement.

  7. Identification of Super Phenix steam generator by a simple polynomial model

    Rousseau, I.

    1981-01-01

    This note suggests a method of identification for the steam generator of the Super-Phenix fast neutron power plant for simple polynomial models. This approach is justified in the selection of the adaptive control. The identification algorithms presented will be applied to multivariable input-output behaviours. The results obtained with the representation in self-regressive form and by simple polynomial models will be compared and the effect of perturbations on the output signal will be tested, in order to select a good identification algorithm for multivariable adaptive regulation [fr

  8. Numerical modelling of multiple scattering between two elastical particles

    Bjørnø, Irina; Jensen, Leif Bjørnø

    1998-01-01

    in suspension have been studied extensively since Foldy's formulation of his theory for isotropic scattering by randomly distributed scatterers. However, a number of important problems related to multiple scattering are still far from finding their solutions. A particular, but still unsolved, problem......Multiple acoustical signal interactions with sediment particles in the vicinity of the seabed may significantly change the course of sediment concentration profiles determined by inversion from acoustical backscattering measurements. The scattering properties of high concentrations of sediments...... is the question of proximity thresholds for influence of multiple scattering in terms of particle properties like volume fraction, average distance between particles or other related parameters. A few available experimental data indicate a significance of multiple scattering in suspensions where the concentration...

  9. 231 Using Multiple Regression Analysis in Modelling the Role of ...

    User

    of Internal Revenue, Tourism Bureau and hotel records. The multiple regression .... additional guest facilities such as restaurant, a swimming pool or child care and social function ... and provide good quality service to the public. Conclusion.

  10. Modeling a Single SEP Event from Multiple Vantage Points Using the iPATH Model

    Hu, Junxiang; Li, Gang; Fu, Shuai; Zank, Gary; Ao, Xianzhi

    2018-02-01

    Using the recently extended 2D improved Particle Acceleration and Transport in the Heliosphere (iPATH) model, we model an example gradual solar energetic particle event as observed at multiple locations. Protons and ions that are energized via the diffusive shock acceleration mechanism are followed at a 2D coronal mass ejection-driven shock where the shock geometry varies across the shock front. The subsequent transport of energetic particles, including cross-field diffusion, is modeled by a Monte Carlo code that is based on a stochastic differential equation method. Time intensity profiles and particle spectra at multiple locations and different radial distances, separated in longitudes, are presented. The results shown here are relevant to the upcoming Parker Solar Probe mission.

  11. Hydraulic fracture propagation modeling and data-based fracture identification

    Zhou, Jing

    Successful shale gas and tight oil production is enabled by the engineering innovation of horizontal drilling and hydraulic fracturing. Hydraulically induced fractures will most likely deviate from the bi-wing planar pattern and generate complex fracture networks due to mechanical interactions and reservoir heterogeneity, both of which render the conventional fracture simulators insufficient to characterize the fractured reservoir. Moreover, in reservoirs with ultra-low permeability, the natural fractures are widely distributed, which will result in hydraulic fractures branching and merging at the interface and consequently lead to the creation of more complex fracture networks. Thus, developing a reliable hydraulic fracturing simulator, including both mechanical interaction and fluid flow, is critical in maximizing hydrocarbon recovery and optimizing fracture/well design and completion strategy in multistage horizontal wells. A novel fully coupled reservoir flow and geomechanics model based on the dual-lattice system is developed to simulate multiple nonplanar fractures' propagation in both homogeneous and heterogeneous reservoirs with or without pre-existing natural fractures. Initiation, growth, and coalescence of the microcracks will lead to the generation of macroscopic fractures, which is explicitly mimicked by failure and removal of bonds between particles from the discrete element network. This physics-based modeling approach leads to realistic fracture patterns without using the empirical rock failure and fracture propagation criteria required in conventional continuum methods. Based on this model, a sensitivity study is performed to investigate the effects of perforation spacing, in-situ stress anisotropy, rock properties (Young's modulus, Poisson's ratio, and compressive strength), fluid properties, and natural fracture properties on hydraulic fracture propagation. In addition, since reservoirs are buried thousands of feet below the surface, the

  12. An Additive-Multiplicative Cox-Aalen Regression Model

    Scheike, Thomas H.; Zhang, Mei-Jie

    2002-01-01

    Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects...

  13. Genome-Wide Identification and Expression Analysis of WRKY Transcription Factors under Multiple Stresses in Brassica napus.

    He, Yajun; Mao, Shaoshuai; Gao, Yulong; Zhu, Liying; Wu, Daoming; Cui, Yixin; Li, Jiana; Qian, Wei

    2016-01-01

    WRKY transcription factors play important roles in responses to environmental stress stimuli. Using a genome-wide domain analysis, we identified 287 WRKY genes with 343 WRKY domains in the sequenced genome of Brassica napus, 139 in the A sub-genome and 148 in the C sub-genome. These genes were classified into eight groups based on phylogenetic analysis. In the 343 WRKY domains, a total of 26 members showed divergence in the WRKY domain, and 21 belonged to group I. This finding suggested that WRKY genes in group I are more active and variable compared with genes in other groups. Using genome-wide identification and analysis of the WRKY gene family in Brassica napus, we observed genome duplication, chromosomal/segmental duplications and tandem duplication. All of these duplications contributed to the expansion of the WRKY gene family. The duplicate segments that were detected indicated that genome duplication events occurred in the two diploid progenitors B. rapa and B. olearecea before they combined to form B. napus. Analysis of the public microarray database and EST database for B. napus indicated that 74 WRKY genes were induced or preferentially expressed under stress conditions. According to the public QTL data, we identified 77 WRKY genes in 31 QTL regions related to various stress tolerance. We further evaluated the expression of 26 BnaWRKY genes under multiple stresses by qRT-PCR. Most of the genes were induced by low temperature, salinity and drought stress, indicating that the WRKYs play important roles in B. napus stress responses. Further, three BnaWRKY genes were strongly responsive to the three multiple stresses simultaneously, which suggests that these 3 WRKY may have multi-functional roles in stress tolerance and can potentially be used in breeding new rapeseed cultivars. We also found six tandem repeat pairs exhibiting similar expression profiles under the various stress conditions, and three pairs were mapped in the stress related QTL regions

  14. Genome-Wide Identification and Expression Analysis of WRKY Transcription Factors under Multiple Stresses in Brassica napus.

    Yajun He

    Full Text Available WRKY transcription factors play important roles in responses to environmental stress stimuli. Using a genome-wide domain analysis, we identified 287 WRKY genes with 343 WRKY domains in the sequenced genome of Brassica napus, 139 in the A sub-genome and 148 in the C sub-genome. These genes were classified into eight groups based on phylogenetic analysis. In the 343 WRKY domains, a total of 26 members showed divergence in the WRKY domain, and 21 belonged to group I. This finding suggested that WRKY genes in group I are more active and variable compared with genes in other groups. Using genome-wide identification and analysis of the WRKY gene family in Brassica napus, we observed genome duplication, chromosomal/segmental duplications and tandem duplication. All of these duplications contributed to the expansion of the WRKY gene family. The duplicate segments that were detected indicated that genome duplication events occurred in the two diploid progenitors B. rapa and B. olearecea before they combined to form B. napus. Analysis of the public microarray database and EST database for B. napus indicated that 74 WRKY genes were induced or preferentially expressed under stress conditions. According to the public QTL data, we identified 77 WRKY genes in 31 QTL regions related to various stress tolerance. We further evaluated the expression of 26 BnaWRKY genes under multiple stresses by qRT-PCR. Most of the genes were induced by low temperature, salinity and drought stress, indicating that the WRKYs play important roles in B. napus stress responses. Further, three BnaWRKY genes were strongly responsive to the three multiple stresses simultaneously, which suggests that these 3 WRKY may have multi-functional roles in stress tolerance and can potentially be used in breeding new rapeseed cultivars. We also found six tandem repeat pairs exhibiting similar expression profiles under the various stress conditions, and three pairs were mapped in the stress related

  15. Efficient multiple-trait association and estimation of genetic correlation using the matrix-variate linear mixed model.

    Furlotte, Nicholas A; Eskin, Eleazar

    2015-05-01

    Multiple-trait association mapping, in which multiple traits are used simultaneously in the identification of genetic variants affecting those traits, has recently attracted interest. One class of approaches for this problem builds on classical variance component methodology, utilizing a multitrait version of a linear mixed model. These approaches both increase power and provide insights into the genetic architecture of multiple traits. In particular, it is possible to estimate the genetic correlation, which is a measure of the portion of the total correlation between traits that is due to additive genetic effects. Unfortunately, the practical utility of these methods is limited since they are computationally intractable for large sample sizes. In this article, we introduce a reformulation of the multiple-trait association mapping approach by defining the matrix-variate linear mixed model. Our approach reduces the computational time necessary to perform maximum-likelihood inference in a multiple-trait model by utilizing a data transformation. By utilizing a well-studied human cohort, we show that our approach provides more than a 10-fold speedup, making multiple-trait association feasible in a large population cohort on the genome-wide scale. We take advantage of the efficiency of our approach to analyze gene expression data. By decomposing gene coexpression into a genetic and environmental component, we show that our method provides fundamental insights into the nature of coexpressed genes. An implementation of this method is available at http://genetics.cs.ucla.edu/mvLMM. Copyright © 2015 by the Genetics Society of America.

  16. in silico identification of genetic variants in glucocerebrosidase (GBA gene involved in Gaucher’s disease using multiple software tools.

    Madhumathi eManickam

    2014-05-01

    Full Text Available Gaucher’s disease is an autosomal recessive disorder caused by the deficiency of glucocerebrosidase, a lysosomal enzyme that catalysis the hydrolysis of the glycolipid glucocerebroside to ceramide and glucose. Polymorphisms in GBA gene have been associated with the development of Gaucher disease. We hypothesize that prediction of SNPs using multiple state of the art software tools will help in increasing the confidence in identification of SNPs involved in Gaucher's disease. Enzyme replacement therapy is the only option for GD. Our goal is to use several state of art SNP algorithms to predict/address harmful SNPs using comparative studies. In this study seven different algorithms (SIFT, MutPred, nsSNP Analyzer, PANTHER, PMUT, PROVEAN and SNPs&GO were used to predict the harmful polymorphisms. Among the 7 programs, SIFT found 47 nsSNPs as deleterious, MutPred found 46 nsSNPs as harmful. nsSNP Analyzer program found 43 out of 47 nsSNPs are disease causing SNPs whereas PANTHER found 32 out of 47 as highly deleterious, 22 out of 47 are classified as pathological mutations by PMUT, 44 out of 47 were predicted to be deleterious by PROVEAN server, all 47 shows the disease related mutations by SNPs&GO. Twenty two nsSNPs were commonly predicted by all the seven different algorithms. The common 22 targeted mutations are F251L, C342G, W312C, P415R, R463C, D127V, A309V, G46E, G202E, P391L, Y363C, Y205C, W378C, I402T, S366R, F397S, Y418C, P401L, G195E, W184R, R48W and T43R.

  17. Tools and Models for Integrating Multiple Cellular Networks

    Gerstein, Mark [Yale Univ., New Haven, CT (United States). Gerstein Lab.

    2015-11-06

    In this grant, we have systematically investigated the integrated networks, which are responsible for the coordination of activity between metabolic pathways in prokaryotes. We have developed several computational tools to analyze the topology of the integrated networks consisting of metabolic, regulatory, and physical interaction networks. The tools are all open-source, and they are available to download from Github, and can be incorporated in the Knowledgebase. Here, we summarize our work as follow. Understanding the topology of the integrated networks is the first step toward understanding its dynamics and evolution. For Aim 1 of this grant, we have developed a novel algorithm to determine and measure the hierarchical structure of transcriptional regulatory networks [1]. The hierarchy captures the direction of information flow in the network. The algorithm is generally applicable to regulatory networks in prokaryotes, yeast and higher organisms. Integrated datasets are extremely beneficial in understanding the biology of a system in a compact manner due to the conflation of multiple layers of information. Therefore for Aim 2 of this grant, we have developed several tools and carried out analysis for integrating system-wide genomic information. To make use of the structural data, we have developed DynaSIN for protein-protein interactions networks with various dynamical interfaces [2]. We then examined the association between network topology with phenotypic effects such as gene essentiality. In particular, we have organized E. coli and S. cerevisiae transcriptional regulatory networks into hierarchies. We then correlated gene phenotypic effects by tinkering with different layers to elucidate which layers were more tolerant to perturbations [3]. In the context of evolution, we also developed a workflow to guide the comparison between different types of biological networks across various species using the concept of rewiring [4], and Furthermore, we have developed

  18. Lyssavirus infection: 'low dose, multiple exposure' in the mouse model.

    Banyard, Ashley C; Healy, Derek M; Brookes, Sharon M; Voller, Katja; Hicks, Daniel J; Núñez, Alejandro; Fooks, Anthony R

    2014-03-06

    The European bat lyssaviruses (EBLV-1 and EBLV-2) are zoonotic pathogens present within bat populations across Europe. The maintenance and transmission of lyssaviruses within bat colonies is poorly understood. Cases of repeated isolation of lyssaviruses from bat roosts have raised questions regarding the maintenance and intraspecies transmissibility of these viruses within colonies. Furthermore, the significance of seropositive bats in colonies remains unclear. Due to the protected nature of European bat species, and hence restrictions to working with the natural host for lyssaviruses, this study analysed the outcome following repeat inoculation of low doses of lyssaviruses in a murine model. A standardized dose of virus, EBLV-1, EBLV-2 or a 'street strain' of rabies (RABV), was administered via a peripheral route to attempt to mimic what is hypothesized as natural infection. Each mouse (n=10/virus/group/dilution) received four inoculations, two doses in each footpad over a period of four months, alternating footpad with each inoculation. Mice were tail bled between inoculations to evaluate antibody responses to infection. Mice succumbed to infection after each inoculation with 26.6% of mice developing clinical disease following the initial exposure across all dilutions (RABV, 32.5% (n=13/40); EBLV-1, 35% (n=13/40); EBLV-2, 12.5% (n=5/40)). Interestingly, the lowest dose caused clinical disease in some mice upon first exposure ((RABV, 20% (n=2/10) after first inoculation; RABV, 12.5% (n=1/8) after second inoculation; EBLV-2, 10% (n=1/10) after primary inoculation). Furthermore, five mice developed clinical disease following the second exposure to live virus (RABV, n=1; EBLV-1, n=1; EBLV-2, n=3) although histopathological examination indicated that the primary inoculation was the most probably cause of death due to levels of inflammation and virus antigen distribution observed. All the remaining mice (RABV, n=26; EBLV-1, n=26; EBLV-2, n=29) survived the tertiary and

  19. A comparative proteomics method for multiple samples based on a 18O-reference strategy and a quantitation and identification-decoupled strategy.

    Wang, Hongbin; Zhang, Yongqian; Gui, Shuqi; Zhang, Yong; Lu, Fuping; Deng, Yulin

    2017-08-15

    Comparisons across large numbers of samples are frequently necessary in quantitative proteomics. Many quantitative methods used in proteomics are based on stable isotope labeling, but most of these are only useful for comparing two samples. For up to eight samples, the iTRAQ labeling technique can be used. For greater numbers of samples, the label-free method has been used, but this method was criticized for low reproducibility and accuracy. An ingenious strategy has been introduced, comparing each sample against a 18 O-labeled reference sample that was created by pooling equal amounts of all samples. However, it is necessary to use proportion-known protein mixtures to investigate and evaluate this new strategy. Another problem for comparative proteomics of multiple samples is the poor coincidence and reproducibility in protein identification results across samples. In present study, a method combining 18 O-reference strategy and a quantitation and identification-decoupled strategy was investigated with proportion-known protein mixtures. The results obviously demonstrated that the 18 O-reference strategy had greater accuracy and reliability than other previously used comparison methods based on transferring comparison or label-free strategies. By the decoupling strategy, the quantification data acquired by LC-MS and the identification data acquired by LC-MS/MS are matched and correlated to identify differential expressed proteins, according to retention time and accurate mass. This strategy made protein identification possible for all samples using a single pooled sample, and therefore gave a good reproducibility in protein identification across multiple samples, and allowed for optimizing peptide identification separately so as to identify more proteins. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. Digital Modulation Identification Model Using Wavelet Transform and Statistical Parameters

    P. Prakasam

    2008-01-01

    Full Text Available A generalized modulation identification scheme is developed and presented. With the help of this scheme, the automatic modulation classification and recognition of wireless communication signals with a priori unknown parameters are possible effectively. The special features of the procedure are the possibility to adapt it dynamically to nearly all modulation types, and the capability to identify. The developed scheme based on wavelet transform and statistical parameters has been used to identify M-ary PSK, M-ary QAM, GMSK, and M-ary FSK modulations. The simulated results show that the correct modulation identification is possible to a lower bound of 5 dB. The identification percentage has been analyzed based on the confusion matrix. When SNR is above 5 dB, the probability of detection of the proposed system is more than 0.968. The performance of the proposed scheme has been compared with existing methods and found it will identify all digital modulation schemes with low SNR.

  1. Identification of biased sectors in emission data using a combination of chemical transport model and receptor model

    Uranishi, Katsushige; Ikemori, Fumikazu; Nakatsubo, Ryohei; Shimadera, Hikari; Kondo, Akira; Kikutani, Yuki; Asano, Katsuyoshi; Sugata, Seiji

    2017-10-01

    This study presented a comparison approach with multiple source apportionment methods to identify which sectors of emission data have large biases. The source apportionment methods for the comparison approach included both receptor and chemical transport models, which are widely used to quantify the impacts of emission sources on fine particulate matter of less than 2.5 μm in diameter (PM2.5). We used daily chemical component concentration data in the year 2013, including data for water-soluble ions, elements, and carbonaceous species of PM2.5 at 11 sites in the Kinki-Tokai district in Japan in order to apply the Positive Matrix Factorization (PMF) model for the source apportionment. Seven PMF factors of PM2.5 were identified with the temporal and spatial variation patterns and also retained features of the sites. These factors comprised two types of secondary sulfate, road transportation, heavy oil combustion by ships, biomass burning, secondary nitrate, and soil and industrial dust, accounting for 46%, 17%, 7%, 14%, 13%, and 3% of the PM2.5, respectively. The multiple-site data enabled a comprehensive identification of the PM2.5 sources. For the same period, source contributions were estimated by air quality simulations using the Community Multiscale Air Quality model (CMAQ) with the brute-force method (BFM) for four source categories. Both models provided consistent results for the following three of the four source categories: secondary sulfates, road transportation, and heavy oil combustion sources. For these three target categories, the models' agreement was supported by the small differences and high correlations between the CMAQ/BFM- and PMF-estimated source contributions to the concentrations of PM2.5, SO42-, and EC. In contrast, contributions of the biomass burning sources apportioned by CMAQ/BFM were much lower than and little correlated with those captured by the PMF model, indicating large uncertainties in the biomass burning emissions used in the

  2. Rapid installation of numerical models in multiple parent codes

    Brannon, R.M.; Wong, M.K.

    1996-10-01

    A set of``model interface guidelines``, called MIG, is offered as a means to more rapidly install numerical models (such as stress-strain laws) into any parent code (hydrocode, finite element code, etc.) without having to modify the model subroutines. The model developer (who creates the model package in compliance with the guidelines) specifies the model`s input and storage requirements in a standardized way. For portability, database management (such as saving user inputs and field variables) is handled by the parent code. To date, NUG has proved viable in beta installations of several diverse models in vectorized and parallel codes written in different computer languages. A NUG-compliant model can be installed in different codes without modifying the model`s subroutines. By maintaining one model for many codes, MIG facilitates code-to-code comparisons and reduces duplication of effort potentially reducing the cost of installing and sharing models.

  3. Stochastic modeling of pitting corrosion: A new model for initiation and growth of multiple corrosion pits

    Valor, A.; Caleyo, F.; Alfonso, L.; Rivas, D.; Hallen, J.M.

    2007-01-01

    In this work, a new stochastic model capable of simulating pitting corrosion is developed and validated. Pitting corrosion is modeled as the combination of two stochastic processes: pit initiation and pit growth. Pit generation is modeled as a nonhomogeneous Poisson process, in which induction time for pit initiation is simulated as the realization of a Weibull process. In this way, the exponential and Weibull distributions can be considered as the possible distributions for pit initiation time. Pit growth is simulated using a nonhomogeneous Markov process. Extreme value statistics is used to find the distribution of maximum pit depths resulting from the combination of the initiation and growth processes for multiple pits. The proposed model is validated using several published experiments on pitting corrosion. It is capable of reproducing the experimental observations with higher quality than the stochastic models available in the literature for pitting corrosion

  4. Stochastic modeling of pitting corrosion: A new model for initiation and growth of multiple corrosion pits

    Valor, A. [Facultad de Fisica, Universidad de La Habana, San Lazaro y L, Vedado, 10400 Havana (Cuba); Caleyo, F. [Departamento de Ingenieria, Metalurgica, IPN-ESIQIE, UPALM Edif. 7, Zacatenco, Mexico DF 07738 (Mexico)]. E-mail: fcaleyo@gmail.com; Alfonso, L. [Departamento de Ingenieria, Metalurgica, IPN-ESIQIE, UPALM Edif. 7, Zacatenco, Mexico DF 07738 (Mexico); Rivas, D. [Departamento de Ingenieria, Metalurgica, IPN-ESIQIE, UPALM Edif. 7, Zacatenco, Mexico DF 07738 (Mexico); Hallen, J.M. [Departamento de Ingenieria, Metalurgica, IPN-ESIQIE, UPALM Edif. 7, Zacatenco, Mexico DF 07738 (Mexico)

    2007-02-15

    In this work, a new stochastic model capable of simulating pitting corrosion is developed and validated. Pitting corrosion is modeled as the combination of two stochastic processes: pit initiation and pit growth. Pit generation is modeled as a nonhomogeneous Poisson process, in which induction time for pit initiation is simulated as the realization of a Weibull process. In this way, the exponential and Weibull distributions can be considered as the possible distributions for pit initiation time. Pit growth is simulated using a nonhomogeneous Markov process. Extreme value statistics is used to find the distribution of maximum pit depths resulting from the combination of the initiation and growth processes for multiple pits. The proposed model is validated using several published experiments on pitting corrosion. It is capable of reproducing the experimental observations with higher quality than the stochastic models available in the literature for pitting corrosion.

  5. Recent developments in identification of kinetic and transport models from experimental data. Contributed Paper IT-08

    Bhatt, Nirav P.

    2014-01-01

    In this presentation, we will discuss recent developments in area of identification of kinetic and transport models from experimental data, and their importance in spent fuel reprocessing. The traditional kinetic modelling approaches, differentiation and integral methods, will be presented to set the stage. Then, two frameworks of identifying kinetic and transport models will be presented in details. These frameworks can be classified as follows: (i) simultaneous or global model identification (SMI), and (ii) incremental model identification (IMI). In the SMI framework, as name indicates, rate expressions of all reactions are integrated to predict concentrations that are fitted to measured values via a least-squares problem simultaneously. Alternatively, the identification task can be split into a sequence of sub-problems such as the identification of stoichiometry and rate expressions. For each subproblem, the number of model candidates can be kept small. In addition, the information available at a given step can be used to refine the model in subsequent steps. Further, the advantages and disadvantages of these frameworks will be presented

  6. Race, Ethnicity and Ancestry in Unrelated Transplant Matching for the National Marrow Donor Program: A Comparison of Multiple Forms of Self-Identification with Genetics

    Hollenbach, Jill A.; Saperstein, Aliya; Albrecht, Mark; Vierra-Green, Cynthia; Parham, Peter; Norman, Paul J.; Maiers, Martin

    2015-01-01

    We conducted a nationwide study comparing self-identification to genetic ancestry classifications in a large cohort (n = 1752) from the National Marrow Donor Program. We sought to determine how various measures of self-identification intersect with genetic ancestry, with the aim of improving matching algorithms for unrelated bone marrow transplant. Multiple dimensions of self-identification, including race/ethnicity and geographic ancestry were compared to classifications based on ancestry informative markers (AIMs), and the human leukocyte antigen (HLA) genes, which are required for transplant matching. Nearly 20% of responses were inconsistent between reporting race/ethnicity versus geographic ancestry. Despite strong concordance between AIMs and HLA, no measure of self-identification shows complete correspondence with genetic ancestry. In certain cases geographic ancestry reporting matches genetic ancestry not reflected in race/ethnicity identification, but in other cases geographic ancestries show little correspondence to genetic measures, with important differences by gender. However, when respondents assign ancestry to grandparents, we observe sub-groups of individuals with well- defined genetic ancestries, including important differences in HLA frequencies, with implications for transplant matching. While we advocate for tailored questioning to improve accuracy of ancestry ascertainment, collection of donor grandparents’ information will improve the chances of finding matches for many patients, particularly for mixed-ancestry individuals. PMID:26287376

  7. Race, Ethnicity and Ancestry in Unrelated Transplant Matching for the National Marrow Donor Program: A Comparison of Multiple Forms of Self-Identification with Genetics.

    Jill A Hollenbach

    Full Text Available We conducted a nationwide study comparing self-identification to genetic ancestry classifications in a large cohort (n = 1752 from the National Marrow Donor Program. We sought to determine how various measures of self-identification intersect with genetic ancestry, with the aim of improving matching algorithms for unrelated bone marrow transplant. Multiple dimensions of self-identification, including race/ethnicity and geographic ancestry were compared to classifications based on ancestry informative markers (AIMs, and the human leukocyte antigen (HLA genes, which are required for transplant matching. Nearly 20% of responses were inconsistent between reporting race/ethnicity versus geographic ancestry. Despite strong concordance between AIMs and HLA, no measure of self-identification shows complete correspondence with genetic ancestry. In certain cases geographic ancestry reporting matches genetic ancestry not reflected in race/ethnicity identification, but in other cases geographic ancestries show little correspondence to genetic measures, with important differences by gender. However, when respondents assign ancestry to grandparents, we observe sub-groups of individuals with well- defined genetic ancestries, including important differences in HLA frequencies, with implications for transplant matching. While we advocate for tailored questioning to improve accuracy of ancestry ascertainment, collection of donor grandparents' information will improve the chances of finding matches for many patients, particularly for mixed-ancestry individuals.

  8. Time domain system identification of longitudinal dynamics of single rotor model helicopter using sidpac

    Khaizer, A.N.; Hussain, I.

    2015-01-01

    This paper presents a time-domain approach for identification of longitudinal dynamics of single rotor model helicopter. A frequency sweep excitation input signal is applied for hover flying mode widely used for space state linearized model. A fully automated programmed flight test method provides high quality flight data for system identification using the computer controlled flight simulator X-plane. The flight test data were recorded, analyzed and reduced using the SIDPAC (System Identification Programs for Air Craft) toolbox for MATLAB, resulting in an aerodynamic model of single rotor helicopter. Finally, the identified model of single rotor helicopter is validated on Raptor 30-class model helicopter at hover showing the reliability of proposed approach. (author)

  9. Data-Driven Photovoltaic System Modeling Based on Nonlinear System Identification

    Ayedh Alqahtani

    2016-01-01

    Full Text Available Solar photovoltaic (PV energy sources are rapidly gaining potential growth and popularity compared to conventional fossil fuel sources. As the merging of PV systems with existing power sources increases, reliable and accurate PV system identification is essential, to address the highly nonlinear change in PV system dynamic and operational characteristics. This paper deals with the identification of a PV system characteristic with a switch-mode power converter. Measured input-output data are collected from a real PV panel to be used for the identification. The data are divided into estimation and validation sets. The identification methodology is discussed. A Hammerstein-Wiener model is identified and selected due to its suitability to best capture the PV system dynamics, and results and discussion are provided to demonstrate the accuracy of the selected model structure.

  10. An Additive-Multiplicative Restricted Mean Residual Life Model

    Mansourvar, Zahra; Martinussen, Torben; Scheike, Thomas H.

    2016-01-01

    mean residual life model to study the association between the restricted mean residual life function and potential regression covariates in the presence of right censoring. This model extends the proportional mean residual life model using an additive model as its covariate dependent baseline....... For the suggested model, some covariate effects are allowed to be time-varying. To estimate the model parameters, martingale estimating equations are developed, and the large sample properties of the resulting estimators are established. In addition, to assess the adequacy of the model, we investigate a goodness...

  11. Extending positive CLASS results across multiple instructors and multiple classes of Modeling Instruction

    Brewe, Eric; Traxler, Adrienne; de la Garza, Jorge; Kramer, Laird H.

    2013-12-01

    We report on a multiyear study of student attitudes measured with the Colorado Learning Attitudes about Science Survey in calculus-based introductory physics taught with the Modeling Instruction curriculum. We find that five of six instructors and eight of nine sections using Modeling Instruction showed significantly improved attitudes from pre- to postcourse. Cohen’s d effect sizes range from 0.08 to 0.95 for individual instructors. The average effect was d=0.45, with a 95% confidence interval of (0.26-0.64). These results build on previously published results showing positive shifts in attitudes from Modeling Instruction classes. We interpret these data in light of other published positive attitudinal shifts and explore mechanistic explanations for similarities and differences with other published positive shifts.

  12. Extending positive CLASS results across multiple instructors and multiple classes of Modeling Instruction

    Eric Brewe

    2013-10-01

    Full Text Available We report on a multiyear study of student attitudes measured with the Colorado Learning Attitudes about Science Survey in calculus-based introductory physics taught with the Modeling Instruction curriculum. We find that five of six instructors and eight of nine sections using Modeling Instruction showed significantly improved attitudes from pre- to postcourse. Cohen’s d effect sizes range from 0.08 to 0.95 for individual instructors. The average effect was d=0.45, with a 95% confidence interval of (0.26–0.64. These results build on previously published results showing positive shifts in attitudes from Modeling Instruction classes. We interpret these data in light of other published positive attitudinal shifts and explore mechanistic explanations for similarities and differences with other published positive shifts.

  13. Identification of Multiple Cryptococcal Fungicidal Drug Targets by Combined Gene Dosing and Drug Affinity Responsive Target Stability Screening

    Yoon-Dong Park

    2016-08-01

    Full Text Available Cryptococcus neoformans is a pathogenic fungus that is responsible for up to half a million cases of meningitis globally, especially in immunocompromised individuals. Common fungistatic drugs, such as fluconazole, are less toxic for patients but have low efficacy for initial therapy of the disease. Effective therapy against the disease is provided by the fungicidal drug amphotericin B; however, due to its high toxicity and the difficulty in administering its intravenous formulation, it is imperative to find new therapies targeting the fungus. The antiparasitic drug bithionol has been recently identified as having potent fungicidal activity. In this study, we used a combined gene dosing and drug affinity responsive target stability (GD-DARTS screen as well as protein modeling to identify a common drug binding site of bithionol within multiple NAD-dependent dehydrogenase drug targets. This combination genetic and proteomic method thus provides a powerful method for identifying novel fungicidal drug targets for further development.

  14. Interstitial integrals in the multiple-scattering model

    Swanson, J.R.; Dill, D.

    1982-01-01

    We present an efficient method for the evaluation of integrals involving multiple-scattering wave functions over the interstitial region. Transformation of the multicenter interstitial wave functions to a single center representation followed by a geometric projection reduces the integrals to products of analytic angular integrals and numerical radial integrals. The projection function, which has the value 1 in the interstitial region and 0 elsewhere, has a closed-form partial-wave expansion. The method is tested by comparing its results with exact normalization and dipole integrals; the differences are 2% at worst and typically less than 1%. By providing an efficient means of calculating Coulomb integrals, the method allows treatment of electron correlations using a multiple scattering basis set

  15. Talent identification and development programmes in sport : current models and future directions.

    Vaeyens, Roel; Lenoir, Matthieu; Williams, A Mark; Philippaerts, Renaat M

    2008-01-01

    Many children strive to attain excellence in sport. However, although talent identification and development programmes have gained popularity in recent decades, there remains a lack of consensus in relation to how talent should be defined or identified and there is no uniformly accepted theoretical framework to guide current practice. The success rates of talent identification and development programmes have rarely been assessed and the validity of the models applied remains highly debated. This article provides an overview of current knowledge in this area with special focus on problems associated with the identification of gifted adolescents. There is a growing agreement that traditional cross-sectional talent identification models are likely to exclude many, especially late maturing, 'promising' children from development programmes due to the dynamic and multidimensional nature of sport talent. A conceptual framework that acknowledges both genetic and environmental influences and considers the dynamic and multidimensional nature of sport talent is presented. The relevance of this model is highlighted and recommendations for future work provided. It is advocated that talent identification and development programmes should be dynamic and interconnected taking into consideration maturity status and the potential to develop rather than to exclude children at an early age. Finally, more representative real-world tasks should be developed and employed in a multidimensional design to increase the efficacy of talent identification and development programmes.

  16. A new spatial multiple discrete-continuous modeling approach to land use change analysis.

    2013-09-01

    This report formulates a multiple discrete-continuous probit (MDCP) land-use model within a : spatially explicit economic structural framework for land-use change decisions. The spatial : MDCP model is capable of predicting both the type and intensit...

  17. Steam consumption minimization model in a multiple evaporation effect in a sugar plant

    Villada, Fernando; Valencia, Jaime A; Moreno, German; Murillo, J. Joaquin

    1992-01-01

    In this work, a mathematical model to minimize the steam consumption in a multiple effect evaporation system is shown. The model is based in the dynamic programming technique and the results are tested in a Colombian sugar mill

  18. Multiple machine learning based descriptive and predictive workflow for the identification of potential PTP1B inhibitors.

    Chandra, Sharat; Pandey, Jyotsana; Tamrakar, Akhilesh Kumar; Siddiqi, Mohammad Imran

    2017-01-01

    In insulin and leptin signaling pathway, Protein-Tyrosine Phosphatase 1B (PTP1B) plays a crucial controlling role as a negative regulator, which makes it an attractive therapeutic target for both Type-2 Diabetes (T2D) and obesity. In this work, we have generated classification models by using the inhibition data set of known PTP1B inhibitors to identify new inhibitors of PTP1B utilizing multiple machine learning techniques like naïve Bayesian, random forest, support vector machine and k-nearest neighbors, along with structural fingerprints and selected molecular descriptors. Several models from each algorithm have been constructed and optimized, with the different combination of molecular descriptors and structural fingerprints. For the training and test sets, most of the predictive models showed more than 90% of overall prediction accuracies. The best model was obtained with support vector machine approach and has Matthews Correlation Coefficient of 0.82 for the external test set, which was further employed for the virtual screening of Maybridge small compound database. Five compounds were subsequently selected for experimental assay. Out of these two compounds were found to inhibit PTP1B with significant inhibitory activity in in-vitro inhibition assay. The structural fragments which are important for PTP1B inhibition were identified by naïve Bayesian method and can be further exploited to design new molecules around the identified scaffolds. The descriptive and predictive modeling strategy applied in this study is capable of identifying PTP1B inhibitors from the large compound libraries. Copyright © 2016 Elsevier Inc. All rights reserved.

  19. Modelling of Biometric Identification System with Given Parameters Using Colored Petri Nets

    Petrosyan, G.; Ter-Vardanyan, L.; Gaboutchian, A.

    2017-05-01

    Biometric identification systems use given parameters and function on the basis of Colored Petri Nets as a modelling language developed for systems in which communication, synchronization and distributed resources play an important role. Colored Petri Nets combine the strengths of Classical Petri Nets with the power of a high-level programming language. Coloured Petri Nets have both, formal intuitive and graphical presentations. Graphical CPN model consists of a set of interacting modules which include a network of places, transitions and arcs. Mathematical representation has a well-defined syntax and semantics, as well as defines system behavioural properties. One of the best known features used in biometric is the human finger print pattern. During the last decade other human features have become of interest, such as iris-based or face recognition. The objective of this paper is to introduce the fundamental concepts of Petri Nets in relation to tooth shape analysis. Biometric identification systems functioning has two phases: data enrollment phase and identification phase. During the data enrollment phase images of teeth are added to database. This record contains enrollment data as a noisy version of the biometrical data corresponding to the individual. During the identification phase an unknown individual is observed again and is compared to the enrollment data in the database and then system estimates the individual. The purpose of modeling biometric identification system by means of Petri Nets is to reveal the following aspects of the functioning model: the efficiency of the model, behavior of the model, mistakes and accidents in the model, feasibility of the model simplification or substitution of its separate components for more effective components without interfering system functioning. The results of biometric identification system modeling and evaluating are presented and discussed.

  20. New Hybrid Variational Recovery Model for Blurred Images with Multiplicative Noise

    Dong, Yiqiu; Zeng, Tieyong

    2013-01-01

    A new hybrid variational model for recovering blurred images in the presence of multiplicative noise is proposed. Inspired by previous work on multiplicative noise removal, an I-divergence technique is used to build a strictly convex model under a condition that ensures the uniqueness...

  1. Pursuing the method of multiple working hypotheses for hydrological modeling

    Clark, M.P.; Kavetski, D.; Fenicia, F.

    2011-01-01

    Ambiguities in the representation of environmental processes have manifested themselves in a plethora of hydrological models, differing in almost every aspect of their conceptualization and implementation. The current overabundance of models is symptomatic of an insufficient scientific understanding

  2. Multiple Models of Reality and How to Use Them

    Jamroga, W.J.; Blockeel, H.; Denecker, M.

    2002-01-01

    A virtual agent may obviously benefit from having an up-to-date model of her environment of activity. The model may include actual users' profiles, a dynamic environment characteristic or some assumptions being accepted by default. However, the agent doesn't have to stick to one model only, she can

  3. The Impact of School Climate and School Identification on Academic Achievement: Multilevel Modeling with Student and Teacher Data.

    Maxwell, Sophie; Reynolds, Katherine J; Lee, Eunro; Subasic, Emina; Bromhead, David

    2017-01-01

    School climate is a leading factor in explaining student learning and achievement. Less work has explored the impact of both staff and student perceptions of school climate raising interesting questions about whether staff school climate experiences can add "value" to students' achievement. In the current research, multiple sources were integrated into a multilevel model, including staff self-reports, student self-reports, objective school records of academic achievement, and socio-economic demographics. Achievement was assessed using a national literacy and numeracy tests ( N = 760 staff and 2,257 students from 17 secondary schools). In addition, guided by the "social identity approach," school identification is investigated as a possible psychological mechanism to explain the relationship between school climate and achievement. In line with predictions, results show that students' perceptions of school climate significantly explain writing and numeracy achievement and this effect is mediated by students' psychological identification with the school. Furthermore, staff perceptions of school climate explain students' achievement on numeracy, writing and reading tests (while accounting for students' responses). However, staff's school identification did not play a significant role. Implications of these findings for organizational, social, and educational research are discussed.

  4. The Impact of School Climate and School Identification on Academic Achievement: Multilevel Modeling with Student and Teacher Data

    Sophie Maxwell

    2017-12-01

    Full Text Available School climate is a leading factor in explaining student learning and achievement. Less work has explored the impact of both staff and student perceptions of school climate raising interesting questions about whether staff school climate experiences can add “value” to students' achievement. In the current research, multiple sources were integrated into a multilevel model, including staff self-reports, student self-reports, objective school records of academic achievement, and socio-economic demographics. Achievement was assessed using a national literacy and numeracy tests (N = 760 staff and 2,257 students from 17 secondary schools. In addition, guided by the “social identity approach,” school identification is investigated as a possible psychological mechanism to explain the relationship between school climate and achievement. In line with predictions, results show that students' perceptions of school climate significantly explain writing and numeracy achievement and this effect is mediated by students' psychological identification with the school. Furthermore, staff perceptions of school climate explain students' achievement on numeracy, writing and reading tests (while accounting for students' responses. However, staff's school identification did not play a significant role. Implications of these findings for organizational, social, and educational research are discussed.

  5. On Early Conflict Identification by Requirements Modeling of Energy System Control Structures

    Heussen, Kai; Gehrke, Oliver; Niemann, Hans Henrik

    2015-01-01

    issues early. For requirements formulation of control structures, cyber and physical aspects need to be jointly represented to express interdependencies, check for consistency and discover potentially conflicting requirements. Early identification of potential conflicts may prevent larger problems...... at later design stages. However, languages employed for requirements modeling today do not offer the expressiveness necessary to represent control purposes in relation to domain level interactions and therefore miss several types of interdependencies. This paper introduces the idea of control structure...... modeling for early requirements checking using a suitable modeling language, and illustrates how this approach enables the identification of several classes of controller conflict....

  6. Identification techniques for phenomenological models of hysteresis based on the conjugate gradient method

    Andrei, Petru; Oniciuc, Liviu; Stancu, Alexandru; Stoleriu, Laurentiu

    2007-01-01

    An identification technique for the parameters of phenomenological models of hysteresis is presented. The basic idea of our technique is to set up a system of equations for the parameters of the model as a function of known quantities on the major or minor hysteresis loops (e.g. coercive force, susceptibilities at various points, remanence), or other magnetization curves. This system of equations can be either over or underspecified and is solved by using the conjugate gradient method. Numerical results related to the identification of parameters in the Energetic, Jiles-Atherton, and Preisach models are presented

  7. A Review of the Modelling of Thermally Interacting Multiple Boreholes

    Seama Koohi-Fayegh

    2013-06-01

    Full Text Available Much attention is now focused on utilizing ground heat pumps for heating and cooling buildings, as well as water heating, refrigeration and other thermal tasks. Modeling such systems is important for understanding, designing and optimizing their performance and characteristics. Several heat transfer models exist for ground heat exchangers. In this review article, challenges of modelling heat transfer in vertical heat exchangers are described, some analytical and numerical models are reviewed and compared, recent related developments are described and the importance of modelling these systems is discussed from a variety of aspects, such as sustainability of geothermal systems or their potential impacts on the ecosystems nearby.

  8. Identification of the Skirt Piled Gullfaks C Gravity Platform using ARMAV Models

    Kirkegaard, Poul Henning; Andersen, P.; Brincker, Rune

    This paper presents the results from the system identification of the Gullfaks C gravity offshore platform excited by natural loads. The paper describes how modal parameters and mode shapes can be estimated by use of ARMAV models. The results estimated by an ARMAV model are compared with results...

  9. Identification of the Skirt Piled Gullfaks C Gravity Platform using ARMAV Models

    Kirkegaard, Poul Henning; Andersen, P.; Brincker, Rune

    1996-01-01

    This paper presents the results from the system identification of the Gullfaks C gravity offshore platform excited by natural loads. The paper describes how modal parameters and mode shapes can be estimated by use of ARMAV models. The results estimated by an ARMAV model are compared with results...

  10. A Brand Loyalty Model Utilizing Team Identification and Customer Satisfaction in the Licensed Sports Product Industry

    Lee, Soonhwan; Shin, Hongbum; Park, Jung-Jun; Kwon, Oh-Ryun

    2010-01-01

    The purpose of this study was to investigate the relationship among the attitudinal brand loyalty variables (i.e., cognitive, affective, and conative components), team identification, and customer satisfaction by developing a structural equation model, based on Oliver's (1997) attitudinal brand loyalty model. The results of this study confirmed…

  11. System Identification for Nonlinear FOPDT Model with Input-Dependent Dead-Time

    Sun, Zhen; Yang, Zhenyu

    2011-01-01

    An on-line iterative method of system identification for a kind of nonlinear FOPDT system is proposed in the paper. The considered nonlinear FOPDT model is an extension of the standard FOPDT model by means that its dead time depends on the input signal and the other parameters are time dependent....

  12. Multiple phase transitions in the generalized Curie-Weiss model

    Eisele, T.; Ellis, R.S.

    1988-01-01

    The generalized Curie-Weiss model is an extension of the classical Curie-Weiss model in which the quadratic interaction function of the mean spin value is replaced by a more general interaction function. It is shown that the generalized Curie-Weiss model can have a sequence of phase transitions at different critical temperatures. Both first-order and second-order phase transitions can occur, and explicit criteria for the two types are given. Three examples of generalized Curie-Weiss models are worked out in detail, including one example with infinitely many phase transitions. A number of results are derived using large-deviation techniques

  13. Towards Integration of CAx Systems and a Multiple-View Product Modeller in Mechanical Design

    H. Song

    2005-01-01

    Full Text Available This paper deals with the development of an integration framework and its implementation for the connexion of CAx systems and multiple-view product modelling. The integration framework is presented regarding its conceptual level and the implementation level is described currently with the connexion of a functional modeller, a multiple-view product modeller, an optimisation module and a CAD system. The integration between the multiple-view product modeller and CATIA V5 based on the STEP standard is described in detail. Finally, the presented works are discussed and future research developments are suggested. 

  14. Identification of ecosystem parameters by SDE-modelling

    Stochastic differential equations (SDEs) for ecosystem modelling have attracted increasing attention during recent years. The modelling has mostly been through simulation experiments in order to analyse how system noise propagates through the ordinary differential equation formulation of ecosystem...... models. Estimation of parameters in SDEs is, however, possible by combining Kalman filter techniques and likelihood estimation. By modelling parameters as random walks it is possible to identify linear as well as non-linear interactions between ecosystem components. By formulating a simple linear SDE...

  15. Experimental Grey Box Model Identification of an Active Gas Bearing

    Theisen, Lukas Roy Svane; Pierart Vásquez, Fabián Gonzalo; Niemann, Hans Henrik

    2014-01-01

    in a dynamic model of an active gas bearing and subsequent control loop design. A grey box model is determined based on experiments where piezo actuated valves are used to perturb the journal and hence excite the rotor-bearing system. Such modelling from actuator to output is shown to effciently support...

  16. Multiple Model Adaptive Control Using Dual Youla-Kucera Factorisation

    Bendtsen, Jan Dimon; Trangbæk, Klaus

    2012-01-01

    We propose a multi-model adaptive control scheme for uncertain linear plants based on the concept of model unfalsification. The approach relies on examining the ability of a pre-computed set of plant-controller candidates and choosing the one that is best able to reproduce observed in- and output...

  17. Multiple operating models for data linkage: A privacy positive

    Katrina Irvine

    2017-04-01

    Our data linkage centre will implement new operating models with cascading levels of data handling on behalf of custodians. Sharing or publication of empirical evidence on timeframes, efficiency and quality can provide useful inputs in the design of new operating models and assist with the development of stakeholder and public confidence.

  18. Learning Data Set Influence on Identification Accuracy of Gas Turbine Neural Network Model

    Kuznetsov, A. V.; Makaryants, G. M.

    2018-01-01

    There are many gas turbine engine identification researches via dynamic neural network models. It should minimize errors between model and real object during identification process. Questions about training data set processing of neural networks are usually missed. This article presents a study about influence of data set type on gas turbine neural network model accuracy. The identification object is thermodynamic model of micro gas turbine engine. The thermodynamic model input signal is the fuel consumption and output signal is the engine rotor rotation frequency. Four types input signals was used for creating training and testing data sets of dynamic neural network models - step, fast, slow and mixed. Four dynamic neural networks were created based on these types of training data sets. Each neural network was tested via four types test data sets. In the result 16 transition processes from four neural networks and four test data sets from analogous solving results of thermodynamic model were compared. The errors comparison was made between all neural network errors in each test data set. In the comparison result it was shown error value ranges of each test data set. It is shown that error values ranges is small therefore the influence of data set types on identification accuracy is low.

  19. Parameter identification of a BWR nuclear power plant model for use in optimal control

    Volf, K.

    1976-02-01

    The problem being considered is the modeling of a nuclear power plant for the development of an optimal control system of the plant. Current system identification concepts, combining input/output information with a-priori structural information are employed. Two of the known parameter identification methods i.e., a least squares method and a maximum likelihood technique, are studied as ways of parameter identification from measurement data. A low order state variable stochastic model of a BWR nuclear power plant is presented as an application of this approach. The model consists of a deterministic and a noise part. The deterministic part is formed by simplified modeling of the major plant dynamic phenomena. The moise part models the effects of input random disturbances to the deterministic part and additive measurement noise. Most of the model parameters are assumed to be initially unknown. They are identified using measurement data records. A detailed high order digital computer simulation is used to simulate plant dynamic behaviour since it is not conceivable for experimentation of this kind to be performed on the real nuclear power plant. The identification task consists in adapting the performance of the simple model to the data acquired from this plant simulation ensuring the applicability of the techniques to measurement data acquired directly from the plant. (orig.) [de

  20. Relative efficiency of joint-model and full-conditional-specification multiple imputation when conditional models are compatible: The general location model.

    Seaman, Shaun R; Hughes, Rachael A

    2018-06-01

    Estimating the parameters of a regression model of interest is complicated by missing data on the variables in that model. Multiple imputation is commonly used to handle these missing data. Joint model multiple imputation and full-conditional specification multiple imputation are known to yield imputed data with the same asymptotic distribution when the conditional models of full-conditional specification are compatible with that joint model. We show that this asymptotic equivalence of imputation distributions does not imply that joint model multiple imputation and full-conditional specification multiple imputation will also yield asymptotically equally efficient inference about the parameters of the model of interest, nor that they will be equally robust to misspecification of the joint model. When the conditional models used by full-conditional specification multiple imputation are linear, logistic and multinomial regressions, these are compatible with a restricted general location joint model. We show that multiple imputation using the restricted general location joint model can be substantially more asymptotically efficient than full-conditional specification multiple imputation, but this typically requires very strong associations between variables. When associations are weaker, the efficiency gain is small. Moreover, full-conditional specification multiple imputation is shown to be potentially much more robust than joint model multiple imputation using the restricted general location model to mispecification of that model when there is substantial missingness in the outcome variable.

  1. Knowledge representation to support reasoning based on multiple models

    Gillam, April; Seidel, Jorge P.; Parker, Alice C.

    1990-01-01

    Model Based Reasoning is a powerful tool used to design and analyze systems, which are often composed of numerous interactive, interrelated subsystems. Models of the subsystems are written independently and may be used together while they are still under development. Thus the models are not static. They evolve as information becomes obsolete, as improved artifact descriptions are developed, and as system capabilities change. Researchers are using three methods to support knowledge/data base growth, to track the model evolution, and to handle knowledge from diverse domains. First, the representation methodology is based on having pools, or types, of knowledge from which each model is constructed. In addition information is explicit. This includes the interactions between components, the description of the artifact structure, and the constraints and limitations of the models. The third principle we have followed is the separation of the data and knowledge from the inferencing and equation solving mechanisms. This methodology is used in two distinct knowledge-based systems: one for the design of space systems and another for the synthesis of VLSI circuits. It has facilitated the growth and evolution of our models, made accountability of results explicit, and provided credibility for the user community. These capabilities have been implemented and are being used in actual design projects.

  2. Multiplicative quiver varieties and generalised Ruijsenaars-Schneider models

    Chalykh, Oleg; Fairon, Maxime

    2017-11-01

    We study some classical integrable systems naturally associated with multiplicative quiver varieties for the (extended) cyclic quiver with m vertices. The phase space of our integrable systems is obtained by quasi-Hamiltonian reduction from the space of representations of the quiver. Three families of Poisson-commuting functions are constructed and written explicitly in suitable Darboux coordinates. The case m = 1 corresponds to the tadpole quiver and the Ruijsenaars-Schneider system and its variants, while for m > 1 we obtain new integrable systems that generalise the Ruijsenaars-Schneider system. These systems and their quantum versions also appeared recently in the context of supersymmetric gauge theory and cyclotomic DAHAs (Braverman et al. [32,34,35] and Kodera and Nakajima [36]), as well as in the context of the Macdonald theory (Chalykh and Etingof, 2013).

  3. Parameter Identification of Ship Maneuvering Models Using Recursive Least Square Method Based on Support Vector Machines

    Man Zhu

    2017-03-01

    Full Text Available Determination of ship maneuvering models is a tough task of ship maneuverability prediction. Among several prime approaches of estimating ship maneuvering models, system identification combined with the full-scale or free- running model test is preferred. In this contribution, real-time system identification programs using recursive identification method, such as the recursive least square method (RLS, are exerted for on-line identification of ship maneuvering models. However, this method seriously depends on the objects of study and initial values of identified parameters. To overcome this, an intelligent technology, i.e., support vector machines (SVM, is firstly used to estimate initial values of the identified parameters with finite samples. As real measured motion data of the Mariner class ship always involve noise from sensors and external disturbances, the zigzag simulation test data include a substantial quantity of Gaussian white noise. Wavelet method and empirical mode decomposition (EMD are used to filter the data corrupted by noise, respectively. The choice of the sample number for SVM to decide initial values of identified parameters is extensively discussed and analyzed. With de-noised motion data as input-output training samples, parameters of ship maneuvering models are estimated using RLS and SVM-RLS, respectively. The comparison between identification results and true values of parameters demonstrates that both the identified ship maneuvering models from RLS and SVM-RLS have reasonable agreements with simulated motions of the ship, and the increment of the sample for SVM positively affects the identification results. Furthermore, SVM-RLS using data de-noised by EMD shows the highest accuracy and best convergence.

  4. The impacts of multiple stressors to model ecological structures

    Landis, W.G.; Kelly, S.A.; Markiewicz, A.J.; Matthews, R.A.; Matthews, G.B.

    1995-01-01

    The basis of the community conditioning hypothesis is that ecological structures are the result of their unique etiology. Systems that have been exposed to a variety of stressors should reflect this history. The authors how conducted a series of microcosm experiments that can compare the effects of multiple stressors upon community dynamics. The microcosm protocols are derived from the Standardized Aquatic Microcosm (SAM) and have Lemma and additional protozoan species. Two multiple stressor experiments have been conducted. In an extended length SAM (ELSAM), two of four treatments were dosed with the turbine fuel JP-8 one week into the experiment. Two treatments were later exposed to the heat stress, one that had received jet fuel and one that had not. Similarly, an ELSAM was conducted with the second stressor being the further addition of JP-8 replacing the heat shock. Biological, physical and chemical data were analyzed with multivariate techniques including nonmetric clustering and association analysis. Space-time worms and phase diagrams were also employed to ascertain the dynamic relationships of variables identified as important by the multivariate techniques. The experiments do not result in a simple additive linear response to the additional stressor. Examination of the relative population dynamics reveal alterations in trajectories that suggest treatment related effects. As in previous single stressor experiments, recovery does not occur even after extended experimental periods. The authors are now attempting to measure the resulting trajectories, changes in similarity vectors and overall dynamics. However, community conditioning does appear to be an important framework in understanding systems with a heterogeneous array of stressors

  5. Physics-Based Identification, Modeling and Risk Management for Aeroelastic Flutter and Limit-Cycle Oscillations (LCO), Phase I

    National Aeronautics and Space Administration — The proposed research program will develop a physics-based identification, modeling and risk management infrastructure for aeroelastic transonic flutter and...

  6. A heart model with multiple chambers for myocardial scintigraphy

    Pretschner, D.P.; Hundeshagen, H.

    1980-01-01

    A heart model is portrayed which consists from individual segments to be filled with activity. The mechanics allow to vary the position in order to generate different positions for evaluation of the scintigraphic systems performance. (orig.) [de

  7. Multiple Model Particle Filtering For Multi-Target Tracking

    Hero, Alfred; Kreucher, Chris; Kastella, Keith

    2004-01-01

    .... The details of this method have been presented elsewhere 1. One feature of real targets is that they are poorly described by a single kinematic model Target behavior may change dramatically i.e...

  8. Computational Modeling of Human Multiple-Task Performance

    Kieras, David E; Meyer, David

    2005-01-01

    This is the final report for a project that was a continuation of an earlier, long-term project on the development and validation of the EPIC cognitive architecture for modeling human cognition and performance...

  9. A Bose-Einstein model of particle multiplicity distributions

    Mekjian, A.Z. [Department of Physics and Astronomy, Rutgers University, Piscataway, NJ 08854 (United States) and California Institute of Technology, Kellogg Radiation Lab., Pasadena, CA 91106 (United States) and MTA KFKI RMKI, 114 PO Box 49, H-1525 Budapest (Hungary)]. E-mail: amekjian@physics.rutgers.edu; Csoergoe, T. [MTA KFKI RMKI, 114 PO Box 49, H-1525 Budapest (Hungary); Hegyi, S. [MTA KFKI RMKI, 114 PO Box 49, H-1525 Budapest (Hungary)

    2007-03-01

    A model of particle production is developed based on a parallel with a theory of Bose-Einstein condensation and similarities with other critical phenomena such as critical opalescence. The role of a power law critical exponent {tau} and Levy index {alpha} are studied. Various features of this model are developed and compared with other commonly used models of particle production which are shown to differ by having different values for {tau}, {alpha}. While void scaling is a feature of this model, hierarchical structure is not a general property of it. The value of the exponent {tau}=2 is a transition point associated with void and hierarchical scaling features. An exponent {gamma} is introduced to describe enhanced fluctuations near a critical point. Experimentally determined properties of the void scaling function can be used to determine {tau}.

  10. A Bose-Einstein model of particle multiplicity distributions

    Mekjian, A.Z.; Csoergoe, T.; Hegyi, S.

    2007-01-01

    A model of particle production is developed based on a parallel with a theory of Bose-Einstein condensation and similarities with other critical phenomena such as critical opalescence. The role of a power law critical exponent τ and Levy index α are studied. Various features of this model are developed and compared with other commonly used models of particle production which are shown to differ by having different values for τ, α. While void scaling is a feature of this model, hierarchical structure is not a general property of it. The value of the exponent τ=2 is a transition point associated with void and hierarchical scaling features. An exponent γ is introduced to describe enhanced fluctuations near a critical point. Experimentally determined properties of the void scaling function can be used to determine τ

  11. A Bose Einstein model of particle multiplicity distributions

    Mekjian, A. Z.; Csörgö, T.; Hegyi, S.

    2007-03-01

    A model of particle production is developed based on a parallel with a theory of Bose-Einstein condensation and similarities with other critical phenomena such as critical opalescence. The role of a power law critical exponent τ and Levy index α are studied. Various features of this model are developed and compared with other commonly used models of particle production which are shown to differ by having different values for τ, α. While void scaling is a feature of this model, hierarchical structure is not a general property of it. The value of the exponent τ=2 is a transition point associated with void and hierarchical scaling features. An exponent γ is introduced to describe enhanced fluctuations near a critical point. Experimentally determined properties of the void scaling function can be used to determine τ.

  12. Using Closed-Set Speaker Identification Score Confidence to Enhance Audio-Based Collaborative Filtering for Multiple Users

    Shepstone, Sven Ewan; Tan, Zheng-Hua; Kristoffersen, Miklas Strøm

    2018-01-01

    In this paper, we utilize a closed-set speaker-identification approach to convey the ratings needed for collaborative filtering-based recommendation. Instead of explicitly providing a rating for a given program, users use a speech interface to dictate the desired rating after watching a movie. Due...... to the inaccuracies that may be imposed by a state-of-the-art speaker identification system, it is possible to mistake a user for another user in the household, especially when the users exhibit similar or identical age and gender demographics. This leads to the undesirable effect of injecting unwanted ratings...... into the collaborative rating matrix, and when the users have different tastes, can result in the recommendation of undesirable items. We therefore propose a simple confidence-based heuristic that utilizes the log-likelihood scores from the speaker identification front-end. The algorithm limits the degree to which...

  13. Discrete modeling of multiple discontinuities in rock mass using XFEM

    Das, Kamal C.; Ausas, Roberto Federico; Carol, Ignacio; Rodrigues, Eduardo; Sandeep, Sandra; Vargas, P. E.; Gonzalez, Nubia Aurora; Segura, Josep María; Lakshmikantha, Ramasesha Mookanahallipatna; Mello,, U.

    2017-01-01

    Modeling of discontinuities (fractures and fault surfaces) is of major importance to assess the geomechanical behavior of oil and gas reservoirs, especially for tight and unconventional reservoirs. Numerical analysis of discrete discontinuities traditionally has been studied using interface element concepts, however more recently there are attempts to use extended finite element method (XFEM). The development of an XFEM tool for geo-mechanical fractures/faults modeling has significant industr...

  14. Identification of Biokinetic Models Using the Concept of Extents.

    Mašić, Alma; Srinivasan, Sriniketh; Billeter, Julien; Bonvin, Dominique; Villez, Kris

    2017-07-05

    The development of a wide array of process technologies to enable the shift from conventional biological wastewater treatment processes to resource recovery systems is matched by an increasing demand for predictive capabilities. Mathematical models are excellent tools to meet this demand. However, obtaining reliable and fit-for-purpose models remains a cumbersome task due to the inherent complexity of biological wastewater treatment processes. In this work, we present a first study in the context of environmental biotechnology that adopts and explores the use of extents as a way to simplify and streamline the dynamic process modeling task. In addition, the extent-based modeling strategy is enhanced by optimal accounting for nonlinear algebraic equilibria and nonlinear measurement equations. Finally, a thorough discussion of our results explains the benefits of extent-based modeling and its potential to turn environmental process modeling into a highly automated task.

  15. Modeling, Identification, Estimation, and Simulation of Urban Traffic Flow in Jakarta and Bandung

    Herman Y. Sutarto

    2015-06-01

    Full Text Available This paper presents an overview of urban traffic flow from the perspective of system theory and stochastic control. The topics of modeling, identification, estimation and simulation techniques are evaluated and validated using actual traffic flow data from the city of Jakarta and Bandung, Indonesia, and synthetic data generated from traffic micro-simulator VISSIM. The results on particle filter (PF based state estimation and Expectation-Maximization (EM based parameter estimation (identification confirm the proposed model gives satisfactory results that capture the variation of urban traffic flow. The combination of the technique and the simulator platform assembles possibility to develop a real-time traffic light controller.  

  16. Exploring the Use of Multiple Analogical Models when Teaching and Learning Chemical Equilibrium

    Harrison, Allan G.; De Jong, Onno

    2005-01-01

    This study describes the multiple analogical models used to introduce and teach Grade 12 chemical equilibrium. We examine the teacher's reasons for using models, explain each model's development during the lessons, and analyze the understandings students derived from the models. A case study approach was used and the data were drawn from the…

  17. Application of an improved model for the identification of material parameters

    Frederiksen, Per S.

    1997-01-01

    Elastic material constants of thick plates can be identified by combining a range of measured natural frequencies with an accurate numerical model for the theoretical predictions. To deal with thick plates, a model that takes transverse shear effects into account is necessary. Since modeling errors...... affect the estimates in a systematic way, an accurate numerical model is of primary importance. Compared to a model used previously, an improved more accurate plate model is studied here for the purpose of identification. This new advanced model is used to assess the systematic errors...

  18. A neural network model for non invasive subsurface stratigraphic identification

    Sullivan, John M. Jr.; Ludwig, Reinhold; Lai Qiang

    2000-01-01

    Ground-Penetrating Radar (GRP) is a powerful tool to examine the stratigraphy below ground surface for remote sensing. Increasingly GPR has also found applications in microwave NDE as an interrogation tool to assess dielectric layers. Unfortunately, GPR data is characterized by a high degree of uncertainty and natural physical ambiguity. Robust decomposition routines are sparse for this application. We have developed a hierarchical set of neural network modules which split the task of layer profiling into consecutive stages. Successful GPR profiling of the subsurface stratigraphy is of key importance for many remote sensing applications including microwave NDE. Neural network modules were designed to accomplish the two main processing goals of recognizing the 'subsurface pattern' followed by the identification of the depths of the subsurface layers like permafrost, groundwater table, and bedrock. We used an adaptive transform technique to transform raw GPR data into a small feature vector containing the most representative and discriminative features of the signal. This information formed the input for the neural network processing units. This strategy reduced the number of required training samples for the neural network by orders of magnitude. The entire processing system was trained using the adaptive transformed feature vector inputs and tested with real measured GPR data. The successful results of this system establishes the feasibility the feasibility of delineating subsurface layering nondestructively

  19. Towards simplification of hydrologic modeling: Identification of dominant processes

    Markstrom, Steven; Hay, Lauren E.; Clark, Martyn P.

    2016-01-01

    The Precipitation–Runoff Modeling System (PRMS), a distributed-parameter hydrologic model, has been applied to the conterminous US (CONUS). Parameter sensitivity analysis was used to identify: (1) the sensitive input parameters and (2) particular model output variables that could be associated with the dominant hydrologic process(es). Sensitivity values of 35 PRMS calibration parameters were computed using the Fourier amplitude sensitivity test procedure on 110 000 independent hydrologically based spatial modeling units covering the CONUS and then summarized to process (snowmelt, surface runoff, infiltration, soil moisture, evapotranspiration, interflow, baseflow, and runoff) and model performance statistic (mean, coefficient of variation, and autoregressive lag 1). Identified parameters and processes provide insight into model performance at the location of each unit and allow the modeler to identify the most dominant process on the basis of which processes are associated with the most sensitive parameters. The results of this study indicate that: (1) the choice of performance statistic and output variables has a strong influence on parameter sensitivity, (2) the apparent model complexity to the modeler can be reduced by focusing on those processes that are associated with sensitive parameters and disregarding those that are not, (3) different processes require different numbers of parameters for simulation, and (4) some sensitive parameters influence only one hydrologic process, while others may influence many

  20. Identification of drought in Dhalai river watershed using MCDM and ANN models

    Aher, Sainath; Shinde, Sambhaji; Guha, Shantamoy; Majumder, Mrinmoy

    2017-03-01

    An innovative approach for drought identification is developed using Multi-Criteria Decision Making (MCDM) and Artificial Neural Network (ANN) models from surveyed drought parameter data around the Dhalai river watershed in Tripura hinterlands, India. Total eight drought parameters, i.e., precipitation, soil moisture, evapotranspiration, vegetation canopy, cropping pattern, temperature, cultivated land, and groundwater level were obtained from expert, literature and cultivator survey. Then, the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) were used for weighting of parameters and Drought Index Identification (DII). Field data of weighted parameters in the meso scale Dhalai River watershed were collected and used to train the ANN model. The developed ANN model was used in the same watershed for identification of drought. Results indicate that the Limited-Memory Quasi-Newton algorithm was better than the commonly used training method. Results obtained from the ANN model shows the drought index developed from the study area ranges from 0.32 to 0.72. Overall analysis revealed that, with appropriate training, the ANN model can be used in the areas where the model is calibrated, or other areas where the range of input parameters is similar to the calibrated region for drought identification.

  1. Identification of cracks in thick beams with a cracked beam element model

    Hou, Chuanchuan; Lu, Yong

    2016-12-01

    The effect of a crack on the vibration of a beam is a classical problem, and various models have been proposed, ranging from the basic stiffness reduction method to the more sophisticated model involving formulation based on the additional flexibility due to a crack. However, in the damage identification or finite element model updating applications, it is still common practice to employ a simple stiffness reduction factor to represent a crack in the identification process, whereas the use of a more realistic crack model is rather limited. In this paper, the issues with the simple stiffness reduction method, particularly concerning thick beams, are highlighted along with a review of several other crack models. A robust finite element model updating procedure is then presented for the detection of cracks in beams. The description of the crack parameters is based on the cracked beam flexibility formulated by means of the fracture mechanics, and it takes into consideration of shear deformation and coupling between translational and longitudinal vibrations, and thus is particularly suitable for thick beams. The identification procedure employs a global searching technique using Genetic Algorithms, and there is no restriction on the location, severity and the number of cracks to be identified. The procedure is verified to yield satisfactory identification for practically any configurations of cracks in a beam.

  2. Accelerating parameter identification of proton exchange membrane fuel cell model with ranking-based differential evolution

    Gong, Wenyin; Cai, Zhihua

    2013-01-01

    Parameter identification of PEM (proton exchange membrane) fuel cell model is a very active area of research. Generally, it can be treated as a numerical optimization problem with complex nonlinear and multi-variable features. DE (differential evolution), which has been successfully used in various fields, is a simple yet efficient evolutionary algorithm for global numerical optimization. In this paper, with the objective of accelerating the process of parameter identification of PEM fuel cell models and reducing the necessary computational efforts, we firstly present a generic and simple ranking-based mutation operator for the DE algorithm. Then, the ranking-based mutation operator is incorporated into five highly-competitive DE variants to solve the PEM fuel cell model parameter identification problems. The main contributions of this work are the proposed ranking-based DE variants and their application to the parameter identification problems of PEM fuel cell models. Experiments have been conducted by using both the simulated voltage–current data and the data obtained from the literature to validate the performance of our approach. The results indicate that the ranking-based DE methods provide better results with respect to the solution quality, the convergence rate, and the success rate compared with their corresponding original DE methods. In addition, the voltage–current characteristics obtained by our approach are in good agreement with the original voltage–current curves in all cases. - Highlights: • A simple and generic ranking-based mutation operator is presented in this paper. • Several DE (differential evolution) variants are used to solve the parameter identification of PEMFC (proton exchange membrane fuel cells) model. • Results show that our method accelerates the process of parameter identification. • The V–I characteristics are in very good agreement with experimental data

  3. Simple model for multiple-choice collective decision making.

    Lee, Ching Hua; Lucas, Andrew

    2014-11-01

    We describe a simple model of heterogeneous, interacting agents making decisions between n≥2 discrete choices. For a special class of interactions, our model is the mean field description of random field Potts-like models and is effectively solved by finding the extrema of the average energy E per agent. In these cases, by studying the propagation of decision changes via avalanches, we argue that macroscopic dynamics is well captured by a gradient flow along E. We focus on the permutation symmetric case, where all n choices are (on average) the same, and spontaneous symmetry breaking (SSB) arises purely from cooperative social interactions. As examples, we show that bimodal heterogeneity naturally provides a mechanism for the spontaneous formation of hierarchies between decisions and that SSB is a preferred instability to discontinuous phase transitions between two symmetric points. Beyond the mean field limit, exponentially many stable equilibria emerge when we place this model on a graph of finite mean degree. We conclude with speculation on decision making with persistent collective oscillations. Throughout the paper, we emphasize analogies between methods of solution to our model and common intuition from diverse areas of physics, including statistical physics and electromagnetism.

  4. Chlorpyrifos PBPK/PD model for multiple routes of exposure.

    Poet, Torka S; Timchalk, Charles; Hotchkiss, Jon A; Bartels, Michael J

    2014-10-01

    1. Chlorpyrifos (CPF) is an important pesticide used to control crop insects. Human Exposures to CPF will occur primarily through oral exposure to residues on foods. A physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model has been developed that describes the relationship between oral, dermal and inhalation doses of CPF and key events in the pathway for cholinergic effects. The model was built on a prior oral model that addressed age-related changes in metabolism and physiology. This multi-route model was developed in rats and humans to validate all scenarios in a parallelogram design. 2. Critical biological effects from CPF exposure require metabolic activation to CPF oxon, and small amounts of metabolism in tissues will potentially have a great effect on pharmacokinetics and pharmacodynamic outcomes. Metabolism (bioactivation and detoxification) was therefore added in diaphragm, brain, lung and skin compartments. Pharmacokinetic data are available for controlled human exposures via the oral and dermal routes and from oral and inhalation studies in rats. The validated model was then used to determine relative dermal versus inhalation uptake from human volunteers exposed to CPF in an indoor scenario.

  5. A measurement model of multiple intelligence profiles of management graduates

    Krishnan, Heamalatha; Awang, Siti Rahmah

    2017-05-01

    In this study, developing a fit measurement model and identifying the best fitting items to represent Howard Gardner's nine intelligences namely, musical intelligence, bodily-kinaesthetic intelligence, mathematical/logical intelligence, visual/spatial intelligence, verbal/linguistic intelligence, interpersonal intelligence, intrapersonal intelligence, naturalist intelligence and spiritual intelligence are the main interest in order to enhance the opportunities of the management graduates for employability. In order to develop a fit measurement model, Structural Equation Modeling (SEM) was applied. A psychometric test which is the Ability Test in Employment (ATIEm) was used as the instrument to measure the existence of nine types of intelligence of 137 University Teknikal Malaysia Melaka (UTeM) management graduates for job placement purposes. The initial measurement model contains nine unobserved variables and each unobserved variable is measured by ten observed variables. Finally, the modified measurement model deemed to improve the Normed chi-square (NC) = 1.331; Incremental Fit Index (IFI) = 0.940 and Root Mean Square of Approximation (RMSEA) = 0.049 was developed. The findings showed that the UTeM management graduates possessed all nine intelligences either high or low. Musical intelligence, mathematical/logical intelligence, naturalist intelligence and spiritual intelligence contributed highest loadings on certain items. However, most of the intelligences such as bodily kinaesthetic intelligence, visual/spatial intelligence, verbal/linguistic intelligence interpersonal intelligence and intrapersonal intelligence possessed by UTeM management graduates are just at the borderline.

  6. Bouc–Wen hysteresis model identification using Modified Firefly Algorithm

    Zaman, Mohammad Asif; Sikder, Urmita

    2015-01-01

    The parameters of Bouc–Wen hysteresis model are identified using a Modified Firefly Algorithm. The proposed algorithm uses dynamic process control parameters to improve its performance. The algorithm is used to find the model parameter values that results in the least amount of error between a set of given data points and points obtained from the Bouc–Wen model. The performance of the algorithm is compared with the performance of conventional Firefly Algorithm, Genetic Algorithm and Differential Evolution algorithm in terms of convergence rate and accuracy. Compared to the other three optimization algorithms, the proposed algorithm is found to have good convergence rate with high degree of accuracy in identifying Bouc–Wen model parameters. Finally, the proposed method is used to find the Bouc–Wen model parameters from experimental data. The obtained model is found to be in good agreement with measured data. - Highlights: • We describe a new method to find the Bouc–Wen hysteresis model parameters. • We propose a Modified Firefly Algorithm. • We compare our method with existing methods to find that the proposed method performs better. • We use our model to fit experimental results. Good agreement is found

  7. Towards simplification of hydrologic modeling: identification of dominant processes

    S. L. Markstrom

    2016-11-01

    Full Text Available parameter hydrologic model, has been applied to the conterminous US (CONUS. Parameter sensitivity analysis was used to identify: (1 the sensitive input parameters and (2 particular model output variables that could be associated with the dominant hydrologic process(es. Sensitivity values of 35 PRMS calibration parameters were computed using the Fourier amplitude sensitivity test procedure on 110 000 independent hydrologically based spatial modeling units covering the CONUS and then summarized to process (snowmelt, surface runoff, infiltration, soil moisture, evapotranspiration, interflow, baseflow, and runoff and model performance statistic (mean, coefficient of variation, and autoregressive lag 1. Identified parameters and processes provide insight into model performance at the location of each unit and allow the modeler to identify the most dominant process on the basis of which processes are associated with the most sensitive parameters. The results of this study indicate that: (1 the choice of performance statistic and output variables has a strong influence on parameter sensitivity, (2 the apparent model complexity to the modeler can be reduced by focusing on those processes that are associated with sensitive parameters and disregarding those that are not, (3 different processes require different numbers of parameters for simulation, and (4 some sensitive parameters influence only one hydrologic process, while others may influence many.

  8. Identification of human operator performance models utilizing time series analysis

    Holden, F. M.; Shinners, S. M.

    1973-01-01

    The results of an effort performed by Sperry Systems Management Division for AMRL in applying time series analysis as a tool for modeling the human operator are presented. This technique is utilized for determining the variation of the human transfer function under various levels of stress. The human operator's model is determined based on actual input and output data from a tracking experiment.

  9. Bouc–Wen hysteresis model identification using Modified Firefly Algorithm

    Zaman, Mohammad Asif, E-mail: zaman@stanford.edu [Department of Electrical Engineering, Stanford University (United States); Sikder, Urmita [Department of Electrical Engineering and Computer Sciences, University of California, Berkeley (United States)

    2015-12-01

    The parameters of Bouc–Wen hysteresis model are identified using a Modified Firefly Algorithm. The proposed algorithm uses dynamic process control parameters to improve its performance. The algorithm is used to find the model parameter values that results in the least amount of error between a set of given data points and points obtained from the Bouc–Wen model. The performance of the algorithm is compared with the performance of conventional Firefly Algorithm, Genetic Algorithm and Differential Evolution algorithm in terms of convergence rate and accuracy. Compared to the other three optimization algorithms, the proposed algorithm is found to have good convergence rate with high degree of accuracy in identifying Bouc–Wen model parameters. Finally, the proposed method is used to find the Bouc–Wen model parameters from experimental data. The obtained model is found to be in good agreement with measured data. - Highlights: • We describe a new method to find the Bouc–Wen hysteresis model parameters. • We propose a Modified Firefly Algorithm. • We compare our method with existing methods to find that the proposed method performs better. • We use our model to fit experimental results. Good agreement is found.

  10. Identification of novel adhesins of M. tuberculosis H37Rv using integrated approach of multiple computational algorithms and experimental analysis.

    Sanjiv Kumar

    Full Text Available Pathogenic bacteria interacting with eukaryotic host express adhesins on their surface. These adhesins aid in bacterial attachment to the host cell receptors during colonization. A few adhesins such as Heparin binding hemagglutinin adhesin (HBHA, Apa, Malate Synthase of M. tuberculosis have been identified using specific experimental interaction models based on the biological knowledge of the pathogen. In the present work, we carried out computational screening for adhesins of M. tuberculosis. We used an integrated computational approach using SPAAN for predicting adhesins, PSORTb, SubLoc and LocTree for extracellular localization, and BLAST for verifying non-similarity to human proteins. These steps are among the first of reverse vaccinology. Multiple claims and attacks from different algorithms were processed through argumentative approach. Additional filtration criteria included selection for proteins with low molecular weights and absence of literature reports. We examined binding potential of the selected proteins using an image based ELISA. The protein Rv2599 (membrane protein binds to human fibronectin, laminin and collagen. Rv3717 (N-acetylmuramoyl-L-alanine amidase and Rv0309 (L,D-transpeptidase bind to fibronectin and laminin. We report Rv2599 (membrane protein, Rv0309 and Rv3717 as novel adhesins of M. tuberculosis H37Rv. Our results expand the number of known adhesins of M. tuberculosis and suggest their regulated expression in different stages.

  11. Analysis of Offshore Knuckle Boom Crane - Part One: Modeling and Parameter Identification

    Morten K. Bak

    2013-10-01

    Full Text Available This paper presents an extensive model of a knuckle boom crane used for pipe handling on offshore drilling rigs. The mechanical system is modeled as a multi-body system and includes the structural flexibility and damping. The motion control system model includes the main components of the crane's electro-hydraulic actuation system. For this a novel black-box model for counterbalance valves is presented, which uses two different pressure ratios to compute the flow through the valve. Experimental data and parameter identification, based on both numerical optimization and manual tuning, are used to verify the crane model. The demonstrated modeling and parameter identification techniques target the system engineer and takes into account the limited access to component data normally encountered by engineers working with design of hydraulic systems.

  12. Chaotic System Identification Based on a Fuzzy Wiener Model with Particle Swarm Optimization

    Yong, Li; Ying-Gan, Tang

    2010-01-01

    A fuzzy Wiener model is proposed to identify chaotic systems. The proposed fuzzy Wiener model consists of two parts, one is a linear dynamic subsystem and the other is a static nonlinear part, which is represented by the Takagi–Sugeno fuzzy model. Identification of chaotic systems is converted to find optimal parameters of the fuzzy Wiener model by minimizing the state error between the original chaotic system and the fuzzy Wiener model. Particle swarm optimization algorithm, a global optimizer, is used to search the optimal parameter of the fuzzy Wiener model. The proposed method can identify the parameters of the linear part and nonlinear part simultaneously. Numerical simulations for Henón and Lozi chaotic system identification show the effectiveness of the proposed method

  13. Camera-Model Identification Using Markovian Transition Probability Matrix

    Xu, Guanshuo; Gao, Shang; Shi, Yun Qing; Hu, Ruimin; Su, Wei

    Detecting the (brands and) models of digital cameras from given digital images has become a popular research topic in the field of digital forensics. As most of images are JPEG compressed before they are output from cameras, we propose to use an effective image statistical model to characterize the difference JPEG 2-D arrays of Y and Cb components from the JPEG images taken by various camera models. Specifically, the transition probability matrices derived from four different directional Markov processes applied to the image difference JPEG 2-D arrays are used to identify statistical difference caused by image formation pipelines inside different camera models. All elements of the transition probability matrices, after a thresholding technique, are directly used as features for classification purpose. Multi-class support vector machines (SVM) are used as the classification tool. The effectiveness of our proposed statistical model is demonstrated by large-scale experimental results.

  14. Multiple bifurcations and periodic 'bubbling' in a delay population model

    Peng Mingshu

    2005-01-01

    In this paper, the flip bifurcation and periodic doubling bifurcations of a discrete population model without delay influence is firstly studied and the phenomenon of Feigenbaum's cascade of periodic doublings is also observed. Secondly, we explored the Neimark-Sacker bifurcation in the delay population model (two-dimension discrete dynamical systems) and the unique stable closed invariant curve which bifurcates from the nontrivial fixed point. Finally, a computer-assisted study for the delay population model is also delved into. Our computer simulation shows that the introduction of delay effect in a nonlinear difference equation derived from the logistic map leads to much richer dynamic behavior, such as stable node → stable focus → an lower-dimensional closed invariant curve (quasi-periodic solution, limit cycle) or/and stable periodic solutions → chaotic attractor by cascading bubbles (the combination of potential period doubling and reverse period-doubling) and the sudden change between two different attractors, etc

  15. Semantics of Temporal Models with Multiple Temporal Dimensions

    Kraft, Peter; Sørensen, Jens Otto

    ending up with lexical data models. In particular we look upon the representations by sets of normalised tables, by sets of 1NF tables and by sets of N1NF/nested tables. At each translation step we focus on how the temporal semantic is consistently maintained. In this way we recognise the requirements...... for representation of temporal properties in different models and the correspondence between the models. The results rely on the assumptions that the temporal dimensions are interdependent and ordered. Thus for example the valid periods of existences of a property in a mini world are dependent on the transaction...... periods in which the corresponding recordings are valid. This is not the normal way of looking at temporal dimensions and we give arguments supporting our assumption....

  16. Modeling of plates with multiple anisotropic layers and residual stress

    Engholm, Mathias; Pedersen, Thomas; Thomsen, Erik Vilain

    2016-01-01

    Usually the analytical approach for modeling of plates uses the single layer plate equation to obtain the deflection and does not take anisotropy and residual stress into account. Based on the stress–strain relation of each layer and balancing stress resultants and bending moments, a general...... multilayered anisotropic plate equation is developed for plates with an arbitrary number of layers. The exact deflection profile is calculated for a circular clamped plate of anisotropic materials with residual bi-axial stress.From the deflection shape the critical stress for buckling is calculated......, and an excellent agreement between the two models is seen with a relative difference of less than 2% for all calculations. The model was also used to extract the cell capacitance, the parasitic capacitance and the residual stress of a pressure sensor composed of a multilayered plate of silicon and silicon oxide...

  17. Dynamic information architecture system (DIAS) : multiple model simulation management

    Simunich, K. L.; Sydelko, P.; Dolph, J.; Christiansen, J.

    2002-01-01

    Dynamic Information Architecture System (DIAS) is a flexible, extensible, object-based framework for developing and maintaining complex multidisciplinary simulations of a wide variety of application contexts. The modeling domain of a specific DIAS-based simulation is determined by (1) software Entity (domain-specific) objects that represent the real-world entities that comprise the problem space (atmosphere, watershed, human), and (2) simulation models and other data processing applications that express the dynamic behaviors of the domain entities. In DIAS, models communicate only with Entity objects, never with each other. Each Entity object has a number of Parameter and Aspect (of behavior) objects associated with it. The Parameter objects contain the state properties of the Entity object. The Aspect objects represent the behaviors of the Entity object and how it interacts with other objects. DIAS extends the ''Object'' paradigm by abstraction of the object's dynamic behaviors, separating the ''WHAT'' from the ''HOW.'' DIAS object class definitions contain an abstract description of the various aspects of the object's behavior (the WHAT), but no implementation details (the HOW). Separate DIAS models/applications carry the implementation of object behaviors (the HOW). Any model deemed appropriate, including existing legacy-type models written in other languages, can drive entity object behavior. The DIAS design promotes plug-and-play of alternative models, with minimal recoding of existing applications. The DIAS Context Builder object builds a constructs or scenario for the simulation, based on developer specification and user inputs. Because DIAS is a discrete event simulation system, there is a Simulation Manager object with which all events are processed. Any class that registers to receive events must implement an event handler (method) to process the event during execution. Event handlers can schedule other events; create or remove Entities from the

  18. Multiple sclerosis care: an integrated disease-management model.

    Burks, J

    1998-04-01

    A disease-management model must be integrated, comprehensive, individual patient focused and outcome driven. In addition to high quality care, the successful model must reduce variations in care and costs. MS specialists need to be intimately involved in the long-term care of MS patients, while not neglecting primary care issues. A nurse care manager is the "glue" between the managed care company, health care providers and the patient/family. Disease management focuses on education and prevention, and can be cost effective as well as patient specific. To implement a successful program, managed care companies and health care providers must work together.

  19. Dynamic information architecture system (DIAS) : multiple model simulation management.

    Simunich, K. L.; Sydelko, P.; Dolph, J.; Christiansen, J.

    2002-05-13

    Dynamic Information Architecture System (DIAS) is a flexible, extensible, object-based framework for developing and maintaining complex multidisciplinary simulations of a wide variety of application contexts. The modeling domain of a specific DIAS-based simulation is determined by (1) software Entity (domain-specific) objects that represent the real-world entities that comprise the problem space (atmosphere, watershed, human), and (2) simulation models and other data processing applications that express the dynamic behaviors of the domain entities. In DIAS, models communicate only with Entity objects, never with each other. Each Entity object has a number of Parameter and Aspect (of behavior) objects associated with it. The Parameter objects contain the state properties of the Entity object. The Aspect objects represent the behaviors of the Entity object and how it interacts with other objects. DIAS extends the ''Object'' paradigm by abstraction of the object's dynamic behaviors, separating the ''WHAT'' from the ''HOW.'' DIAS object class definitions contain an abstract description of the various aspects of the object's behavior (the WHAT), but no implementation details (the HOW). Separate DIAS models/applications carry the implementation of object behaviors (the HOW). Any model deemed appropriate, including existing legacy-type models written in other languages, can drive entity object behavior. The DIAS design promotes plug-and-play of alternative models, with minimal recoding of existing applications. The DIAS Context Builder object builds a constructs or scenario for the simulation, based on developer specification and user inputs. Because DIAS is a discrete event simulation system, there is a Simulation Manager object with which all events are processed. Any class that registers to receive events must implement an event handler (method) to process the event during execution. Event handlers

  20. Innovative supply chain optimization models with multiple uncertainty factors

    Choi, Tsan Ming; Govindan, Kannan; Li, Xiang

    2017-01-01

    Uncertainty is an inherent factor that affects all dimensions of supply chain activities. In today’s business environment, initiatives to deal with one specific type of uncertainty might not be effective since other types of uncertainty factors and disruptions may be present. These factors relate...... to supply chain competition and coordination. Thus, to achieve a more efficient and effective supply chain requires the deployment of innovative optimization models and novel methods. This preface provides a concise review of critical research issues regarding innovative supply chain optimization models...

  1. Latent Clustering Models for Outlier Identification in Telecom Data

    Ye Ouyang

    2016-01-01

    Full Text Available Collected telecom data traffic has boomed in recent years, due to the development of 4G mobile devices and other similar high-speed machines. The ability to quickly identify unexpected traffic data in this stream is critical for mobile carriers, as it can be caused by either fraudulent intrusion or technical problems. Clustering models can help to identify issues by showing patterns in network data, which can quickly catch anomalies and highlight previously unseen outliers. In this article, we develop and compare clustering models for telecom data, focusing on those that include time-stamp information management. Two main models are introduced, solved in detail, and analyzed: Gaussian Probabilistic Latent Semantic Analysis (GPLSA and time-dependent Gaussian Mixture Models (time-GMM. These models are then compared with other different clustering models, such as Gaussian model and GMM (which do not contain time-stamp information. We perform computation on both sample and telecom traffic data to show that the efficiency and robustness of GPLSA make it the superior method to detect outliers and provide results automatically with low tuning parameters or expertise requirement.

  2. Doctoral Dissertation Supervision: Identification and Evaluation of Models

    Ngozi Agu

    2014-01-01

    Full Text Available Doctoral research supervision is one of the major avenues for sustaining students’ satisfaction with the programme, preparing students to be independent researchers and effectively initiating students into the academic community. This work reports doctoral students’ evaluation of their various supervision models, their satisfaction with these supervision models, and development of research-related skills. The study used a descriptive research design and was guided by three research questions and two hypotheses. A sample of 310 Ph.D. candidates drawn from a federal university in Eastern part of Nigeria was used for this study. The data generated through the questionnaire was analyzed using descriptive statistics and t-tests. Results show that face-to-face interactive model was not only the most frequently used, but also the most widely adopted in doctoral thesis supervision while ICT-based models were rarely used. Students supervised under face-to-face interactive model reported being more satisfied with dissertation supervision than those operating under face-to-face noninteractive model. However, students supervised under these two models did not differ significantly in their perceived development in research-related skills.

  3. Nonparametric Identification and Estimation of Finite Mixture Models of Dynamic Discrete Choices

    Hiroyuki Kasahara; Katsumi Shimotsu

    2006-01-01

    In dynamic discrete choice analysis, controlling for unobserved heterogeneity is an important issue, and finite mixture models provide flexible ways to account for unobserved heterogeneity. This paper studies nonparametric identifiability of type probabilities and type-specific component distributions in finite mixture models of dynamic discrete choices. We derive sufficient conditions for nonparametric identification for various finite mixture models of dynamic discrete choices used in appli...

  4. Modeling tissue contamination to improve molecular identification of the primary tumor site of metastases

    Vincent, Martin; Perell, Katharina; Nielsen, Finn Cilius

    2014-01-01

    with any predictor model. The usability of the model is illustrated on primary tumor site identification of liver biopsies, specifically, on a human dataset consisting of microRNA expression measurements of primary tumor samples, benign liver samples and liver metastases. For a predictor trained on primary...... tumor and benign liver samples, the contamination model decreased the test error on biopsies from liver metastases from 77 to 45%. A further reduction to 34% was obtained by including biopsies in the training data....

  5. LEARNING VECTOR QUANTIZATION FOR ADAPTED GAUSSIAN MIXTURE MODELS IN AUTOMATIC SPEAKER IDENTIFICATION

    IMEN TRABELSI

    2017-05-01

    Full Text Available Speaker Identification (SI aims at automatically identifying an individual by extracting and processing information from his/her voice. Speaker voice is a robust a biometric modality that has a strong impact in several application areas. In this study, a new combination learning scheme has been proposed based on Gaussian mixture model-universal background model (GMM-UBM and Learning vector quantization (LVQ for automatic text-independent speaker identification. Features vectors, constituted by the Mel Frequency Cepstral Coefficients (MFCC extracted from the speech signal are used to train the New England subset of the TIMIT database. The best results obtained (90% for gender- independent speaker identification, 97 % for male speakers and 93% for female speakers for test data using 36 MFCC features.

  6. Network formation under heterogeneous costs: The multiple group model

    Kamphorst, J.J.A.; van der Laan, G.

    2007-01-01

    It is widely recognized that the shape of networks influences both individual and aggregate behavior. This raises the question which types of networks are likely to arise. In this paper we investigate a model of network formation, where players are divided into groups and the costs of a link between

  7. Framework for Modelling Multiple Input Complex Aggregations for Interactive Installations

    Padfield, Nicolas; Andreasen, Troels

    2012-01-01

    on fuzzy logic and provides a method for variably balancing interaction and user input with the intention of the artist or director. An experimental design is presented, demonstrating an intuitive interface for parametric modelling of a complex aggregation function. The aggregation function unifies...

  8. Multiple Linear Regression Model for Estimating the Price of a ...

    Ghana Mining Journal ... In the modeling, the Ordinary Least Squares (OLS) normality assumption which could introduce errors in the statistical analyses was dealt with by log transformation of the data, ensuring the data is normally ... The resultant MLRM is: Ŷi MLRM = (X'X)-1X'Y(xi') where X is the sample data matrix.

  9. A multiple-compartment model for biokinetics studies in plants

    Garcia, Fermin; Pietrobron, Flavio; Fonseca, Agnes M.F.; Mol, Anderson W.; Rodriguez, Oscar; Guzman, Fernando

    2001-01-01

    In the present work is used the system of linear equations based in the general Assimakopoulos's GMCM model , for the development of a new method that will determine the flow's parameters and transfer coefficients in plants. The need of mathematical models to quantify the penetration of a trace substance in animals and plants, has often been stressed in the literature. Usually, in radiological environment studies, it is used the mean value of contaminant concentrations on whole or edible part plant body, without taking in account vegetable physiology regularities. In this work concepts and mathematical formulation of a Vegetable Multi-compartment Model (VMCM), taking into account the plant's physiology regularities is presented. The model based in general ideas of the GMCM , and statistical Square Minimum Method STATFLUX is proposed to use in inverse sense: the experimental time dependence of concentration in each compartment, should be input, and the parameters should be determined from this data in a statistical approach. The case of Uranium metabolism is discussed. (author)

  10. Modeling of Optimal Power Generation using Multiple Kites

    Williams, P.; Lansdorp, B.; Ockels, W.J.

    2008-01-01

    Kite systems have the potential to revolutionize energy generation. Large scale systems are envisioned that can fly autonomously in “power generation” cycles which drive a ground-based generator. In order for such systems to produce power efficiently, good models of the system are required. This

  11. Metamodel-based inverse method for parameter identification: elastic-plastic damage model

    Huang, Changwu; El Hami, Abdelkhalak; Radi, Bouchaïb

    2017-04-01

    This article proposed a metamodel-based inverse method for material parameter identification and applies it to elastic-plastic damage model parameter identification. An elastic-plastic damage model is presented and implemented in numerical simulation. The metamodel-based inverse method is proposed in order to overcome the disadvantage in computational cost of the inverse method. In the metamodel-based inverse method, a Kriging metamodel is constructed based on the experimental design in order to model the relationship between material parameters and the objective function values in the inverse problem, and then the optimization procedure is executed by the use of a metamodel. The applications of the presented material model and proposed parameter identification method in the standard A 2017-T4 tensile test prove that the presented elastic-plastic damage model is adequate to describe the material's mechanical behaviour and that the proposed metamodel-based inverse method not only enhances the efficiency of parameter identification but also gives reliable results.

  12. Research on potential user identification model for electric energy substitution

    Xia, Huaijian; Chen, Meiling; Lin, Haiying; Yang, Shuo; Miao, Bo; Zhu, Xinzhi

    2018-01-01

    The implementation of energy substitution plays an important role in promoting the development of energy conservation and emission reduction in china. Energy service management platform of alternative energy users based on the data in the enterprise production value, product output, coal and other energy consumption as a potential evaluation index, using principal component analysis model to simplify the formation of characteristic index, comprehensive index contains the original variables, and using fuzzy clustering model for the same industry user’s flexible classification. The comprehensive index number and user clustering classification based on constructed particle optimization neural network classification model based on the user, user can replace electric potential prediction. The results of an example show that the model can effectively predict the potential of users’ energy potential.

  13. The Model Identification Test: A Limited Verbal Science Test

    McIntyre, P. J.

    1972-01-01

    Describes the production of a test with a low verbal load for use with elementary school science students. Animated films were used to present appropriate and inappropriate models of the behavior of particles of matter. (AL)

  14. Bayesian Modeling for Identification and Estimation of the Learning Effects of Pointing Tasks

    Kyo, Koki

    Recently, in the field of human-computer interaction, a model containing the systematic factor and human factor has been proposed to evaluate the performance of the input devices of a computer. This is called the SH-model. In this paper, in order to extend the range of application of the SH-model, we propose some new models based on the Box-Cox transformation and apply a Bayesian modeling method for identification and estimation of the learning effects of pointing tasks. We consider the parameters describing the learning effect as random variables and introduce smoothness priors for them. Illustrative results show that the newly-proposed models work well.

  15. On the thermoluminescent interactive multiple-trap system (IMTS) model: is it a simple model?

    Gil T, M. I.; Perez C, L.; Cruz Z, E.; Furetta, C.; Roman L, J.

    2016-10-01

    In the thermally stimulated luminescence phenomenon, named thermoluminescence (Tl), the electrons and holes generated by the radiation-matter interaction can be trapped by the metastable levels in the band gap of the solid. Following, the electron can be thermally releases into the conduction band and a radiatively recombination with hole close to the recombination center occurred and the glow curve is emitted. However, the complex mechanism of trapping and thermally releases occurred in the band gap of solid. Some models, such as; first, second and general-order kinetics, have been well established to explain the behaviour of the glow curves and their defects recombination mechanism. In this work, expressions for and Interactive Multiple-Trap System model (IMTS) was obtained assuming: a set of discrete electron traps (active traps At), another set of thermally disconnected trap (TDT) and a recombination center (Rc) too. A numerical analysis based on the Levenberg-Marquardt method in conjunction with an implicit Rosenbrock method was taken into account to simulate the glow curve. The numerical method was tested through synthetic Tl glow curves for a wide range of trap parameters. The activation energy and kinetics order were determined using values from the General Order Kinetics (GOK) model as entry data to IMTS model. This model was tested using the experimental glow curves obtained from Ce or Eu-doped MgF 2 (LiF) polycrystals samples. Results shown that the IMTS model can predict more accurately the behavior of the Tl glow curves that those obtained by the GOK modified by Rasheedy and by the Mixed Order Kinetics model. (Author)

  16. On the thermoluminescent interactive multiple-trap system (IMTS) model: is it a simple model?

    Gil T, M. I.; Perez C, L. [UNAM, Facultad de Quimica, Ciudad Universitaria, 04510 Ciudad de Mexico (Mexico); Cruz Z, E.; Furetta, C.; Roman L, J., E-mail: ecruz@nucleares.unam.mx [UNAM, Instituto de Ciencias Nucleares, Ciudad Universitaria, 04510 Ciudad de Mexico (Mexico)

    2016-10-15

    In the thermally stimulated luminescence phenomenon, named thermoluminescence (Tl), the electrons and holes generated by the radiation-matter interaction can be trapped by the metastable levels in the band gap of the solid. Following, the electron can be thermally releases into the conduction band and a radiatively recombination with hole close to the recombination center occurred and the glow curve is emitted. However, the complex mechanism of trapping and thermally releases occurred in the band gap of solid. Some models, such as; first, second and general-order kinetics, have been well established to explain the behaviour of the glow curves and their defects recombination mechanism. In this work, expressions for and Interactive Multiple-Trap System model (IMTS) was obtained assuming: a set of discrete electron traps (active traps At), another set of thermally disconnected trap (TDT) and a recombination center (Rc) too. A numerical analysis based on the Levenberg-Marquardt method in conjunction with an implicit Rosenbrock method was taken into account to simulate the glow curve. The numerical method was tested through synthetic Tl glow curves for a wide range of trap parameters. The activation energy and kinetics order were determined using values from the General Order Kinetics (GOK) model as entry data to IMTS model. This model was tested using the experimental glow curves obtained from Ce or Eu-doped MgF{sub 2}(LiF) polycrystals samples. Results shown that the IMTS model can predict more accurately the behavior of the Tl glow curves that those obtained by the GOK modified by Rasheedy and by the Mixed Order Kinetics model. (Author)

  17. MODELING OF TARGETED DRUG DELIVERY PART II. MULTIPLE DRUG ADMINISTRATION

    A. V. Zaborovskiy

    2017-01-01

    Full Text Available In oncology practice, despite significant advances in early cancer detection, surgery, radiotherapy, laser therapy, targeted therapy, etc., chemotherapy is unlikely to lose its relevance in the near future. In this context, the development of new antitumor agents is one of the most important problems of cancer research. In spite of the importance of searching for new compounds with antitumor activity, the possibilities of the “old” agents have not been fully exhausted. Targeted delivery of antitumor agents can give them a “second life”. When developing new targeted drugs and their further introduction into clinical practice, the change in their pharmacodynamics and pharmacokinetics plays a special role. The paper describes a pharmacokinetic model of the targeted drug delivery. The conditions under which it is meaningful to search for a delivery vehicle for the active substance were described. Primary screening of antitumor agents was undertaken to modify them for the targeted delivery based on underlying assumptions of the model.

  18. Modeling of CMUTs with Multiple Anisotropic Layers and Residual Stress

    Engholm, Mathias; Thomsen, Erik Vilain

    2014-01-01

    Usually the analytical approach for modeling CMUTs uses the single layer plate equation to obtain the deflection and does not take anisotropy and residual stress into account. A highly accurate model is developed for analytical characterization of CMUTs taking an arbitrary number of layers...... and residual stress into account. Based on the stress-strain relation of each layer and balancing stress resultants and bending moments, a general multilayered anisotropic plate equation is developed for plates with an arbitrary number of layers. The exact deflection profile is calculated for a circular...... clamped plate of anisotropic materials with residual bi-axial stress. From the deflection shape the critical stress for buckling is calculated and by using the Rayleigh-Ritz method the natural frequency is estimated....

  19. Standard model fermion hierarchies with multiple Higgs doublets

    Solaguren-Beascoa Negre, Ana

    2016-01-01

    The hierarchies between the Standard Model (SM) fermion masses and mixing angles and the origin of neutrino masses are two of the biggest mysteries in particle physics. We extend the SM with new Higgs doublets to solve these issues. The lightest fermion masses and the mixing angles are generated through radiative effects, correctly reproducing the hierarchy pattern. Neutrino masses are generated in the see-saw mechanism.

  20. Optical model with multiple band couplings using soft rotator structure

    Martyanov, Dmitry; Soukhovitskii, Efrem; Capote, Roberto; Quesada, Jose Manuel; Chiba, Satoshi

    2017-09-01

    A new dispersive coupled-channel optical model (DCCOM) is derived that describes nucleon scattering on 238U and 232Th targets using a soft-rotator-model (SRM) description of the collective levels of the target nucleus. SRM Hamiltonian parameters are adjusted to the observed collective levels of the target nucleus. SRM nuclear wave functions (mixed in K quantum number) have been used to calculate coupling matrix elements of the generalized optical model. Five rotational bands are coupled: the ground-state band, β-, γ-, non-axial- bands, and a negative parity band. Such coupling scheme includes almost all levels below 1.2 MeV of excitation energy of targets. The "effective" deformations that define inter-band couplings are derived from SRM Hamiltonian parameters. Conservation of nuclear volume is enforced by introducing a monopolar deformed potential leading to additional couplings between rotational bands. The present DCCOM describes the total cross section differences between 238U and 232Th targets within experimental uncertainty from 50 keV up to 200 MeV of neutron incident energy. SRM couplings and volume conservation allow a precise calculation of the compound-nucleus (CN) formation cross sections, which is significantly different from the one calculated with rigid-rotor potentials with any number of coupled levels.