Using of "pseudo-second-order model" in adsorption.
Ho, Yuh-Shan
2014-01-01
A research paper's contribution exists not only in its originality and creativity but also in its continuity and development for research that follows. However, the author easily ignores it. Citation error and quotation error occurred very frequently in a scientific paper. Numerous researchers use secondary references without knowing the original idea from authors. Sulaymon et al. (Environ Sci Pollut Res 20:3011-3023, 2013) and Spiridon et al. (Environ Sci Pollut Res 20:6367-6381, 2013) presented wrong pseudo-second-order models in Environmental Science and Pollution Research, vol. 20. This comment pointed the errors of the kinetic models and offered information for citing original idea of pseudo-second-order kinetic expression. In order to stop the proliferation of the mistake, it is suggested to cite the original paper for the kinetic model which provided greater accuracy and more details about the kinetic expression.
Bizerea Spiridon, Otilia; Pitulice, Laura
2014-01-01
This letter is a response to the issues put forth by Dr. Y.S. Ho with regard to the article "Phenol removal from wastewater by adsorption on zeolitic composite" as reported by Bizerea Spiridon et al. (Environ Sci Pollut Res 20:6367-6381, 2013). The response proposes to clarify the error slipped in the typewritten linearized equation of the pseudo-second-kinetic model and the reason for using secondary reference regarding this model.
National Research Council Canada - National Science Library
Zhu, Wen; Liu, Junsheng; Li, Meng
2014-01-01
...., Lagergren pseudo-first order, pseudo-second order, and Elovich models). Adsorption mechanism was evaluated using intraparticle diffusion model, diffusion-chemisorption model, and Boyd equation...
National Research Council Canada - National Science Library
Luo, Xia; Jedlicka, Sabrina; Jellison, Kristen
.... The remobilization of biofilm-associated C. parvum oocysts back into the water column by biofilm sloughing or bulk erosion poses a threat to public health and may be responsible for waterborne outbreaks...
Application of Kinetic Models to the Sorption of Copper(II) on to Peat
National Research Council Canada - National Science Library
Ho, Y.S; McKay, G
2002-01-01
...) concentrations and peat doses was made. The Elovich model and the pseudo-second order model both provided a high degree of correlation with the experimental data for most of the sorption process...
Directory of Open Access Journals (Sweden)
Johnny Espin
2015-06-01
Full Text Available It is known, though not commonly, that one can describe fermions using a second order in derivatives Lagrangian instead of the first order Dirac one. In this description the propagator is scalar, and the complexity is shifted to the vertex, which contains a derivative operator. In this paper we rewrite the Lagrangian of the fermionic sector of the Standard Model in such second order form. The new Lagrangian is extremely compact, and is obtained from the usual first order Lagrangian by integrating out all primed (or dotted 2-component spinors. It thus contains just half of the 2-component spinors that appear in the usual Lagrangian, which suggests a new perspective on unification. We sketch a natural in this framework SU(2×SU(4⊂SO(9 unified theory.
Fernandez, R.; Deveaux, V.
2010-01-01
We provide a formal definition and study the basic properties of partially ordered chains (POC). These systems were proposed to model textures in image processing and to represent independence relations between random variables in statistics (in the later case they are known as Bayesian networks).
Thibes, Ronaldo
2017-02-01
We perform the canonical and path integral quantizations of a lower-order derivatives model describing Podolsky's generalized electrodynamics. The physical content of the model shows an auxiliary massive vector field coupled to the usual electromagnetic field. The equivalence with Podolsky's original model is studied at classical and quantum levels. Concerning the dynamical time evolution, we obtain a theory with two first-class and two second-class constraints in phase space. We calculate explicitly the corresponding Dirac brackets involving both vector fields. We use the Senjanovic procedure to implement the second-class constraints and the Batalin-Fradkin-Vilkovisky path integral quantization scheme to deal with the symmetries generated by the first-class constraints. The physical interpretation of the results turns out to be simpler due to the reduced derivatives order permeating the equations of motion, Dirac brackets and effective action.
Ozacar, Mahmut
2006-09-01
The adsorption of phosphate onto alunite in a batch adsorber has been studied. Four kinetic models including pseudo first- and second-order equation, intraparticle diffusion equation and the Elovich equation were selected to follow the adsorption process. Kinetic parameters, rate constants, equilibrium adsorption capacities and related correlation coefficients, for each kinetic model were calculated and discussed. It was shown that the adsorption of phosphate onto alunite could be described by the pseudo second-order equation. Adsorption of phosphate onto alunite followed the Langmuir isotherm. A model has been used for the design of a two-stage batch adsorber based on pseudo second-order adsorption kinetics. The model has been optimized with respect to operating time in order to minimize total operating time to achieve a specified amount of phosphate removal using a fixed mass of adsorbent. The results of two-stage batch adsorber design studies showed that the required times for specified amounts of phosphate removal significantly decreased. It is particularly suitable for low-cost adsorbents/adsorption systems when minimising operating time is a major operational and design criterion, such as, for highly congested industrial sites in which significant volume of effluent need to be treated in the minimum amount of time.
Figaro, S; Avril, J P; Brouers, F; Ouensanga, A; Gaspard, S
2009-01-30
Adsorption kinetic of molasses wastewaters after anaerobic digestion (MSWD) and melanoidin respectively on activated carbon was studied at different pH. The kinetic parameters could be determined using classical kinetic equations and a recently published fractal kinetic equation. A linear form of this equation can also be used to fit adsorption data. Even with lower correlation coefficients the fractal kinetic equation gives lower normalized standard deviation values than the pseudo-second order model generally used to fit adsorption kinetic data, indicating that the fractal kinetic model is much more accurate for describing the kinetic adsorption data than the pseudo-second order kinetic model.
COMPARATIVE ANALYSIS OF SOME EXISTING KINETIC MODELS ...
African Journals Online (AJOL)
In terms of highest values of R2, first proposed model accounted for 46.7%, Pseudo second-order kinetics model 40% while Elovich, Webber-Morris and second proposed kinetic models accounted for 6.7% respectively of the total results for biosorption of the three heavy metals by five selected microorganisms. But based ...
comparative analysis of some existing kinetic models with proposed ...
African Journals Online (AJOL)
IGNATIUS NWIDI
But based on values of ARE%, first proposed kinetic model accounted for 93.3% while pseudo second-order kinetic model accounted for 6.7% of the results for biosorption of the three heavy metals by the five microbes. Keynotes: Heavy metals, Biosorption, Kinetics Models, Comparative analysis, Average Relative Error. 1.
Determining Reduced Order Models for Optimal Stochastic Reduced Order Models
Energy Technology Data Exchange (ETDEWEB)
Bonney, Matthew S. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Brake, Matthew R.W. [Sandia National Lab. (SNL-CA), Livermore, CA (United States)
2015-08-01
The use of parameterized reduced order models(PROMs) within the stochastic reduced order model (SROM) framework is a logical progression for both methods. In this report, five different parameterized reduced order models are selected and critiqued against the other models along with truth model for the example of the Brake-Reuss beam. The models are: a Taylor series using finite difference, a proper orthogonal decomposition of the the output, a Craig-Bampton representation of the model, a method that uses Hyper-Dual numbers to determine the sensitivities, and a Meta-Model method that uses the Hyper-Dual results and constructs a polynomial curve to better represent the output data. The methods are compared against a parameter sweep and a distribution propagation where the first four statistical moments are used as a comparison. Each method produces very accurate results with the Craig-Bampton reduction having the least accurate results. The models are also compared based on time requirements for the evaluation of each model where the Meta- Model requires the least amount of time for computation by a significant amount. Each of the five models provided accurate results in a reasonable time frame. The determination of which model to use is dependent on the availability of the high-fidelity model and how many evaluations can be performed. Analysis of the output distribution is examined by using a large Monte-Carlo simulation along with a reduced simulation using Latin Hypercube and the stochastic reduced order model sampling technique. Both techniques produced accurate results. The stochastic reduced order modeling technique produced less error when compared to an exhaustive sampling for the majority of methods.
Selection-endogenous ordered probit and dynamic ordered probit models
Alfonso Miranda; Massimiliano Bratti
2009-01-01
In this presentation we define two qualitatitive response models: 1) Selection Endogenous Dummy Ordered Probit model (SED-OP); 2) a Selection Endogenous Dummy Dynamic Selection Ordered Probit model (SED- DOP). The SED-OP model is a three-equation model constituted of an endogenous dummy equation, a selection equation, and a main equation which has an ordinal response form. The main feature of the model is that the endogenous dummy enters both the selection equation and the main equation. The ...
Kinetic modeling of liquid-phase adsorption of phosphate on dolomite.
Karaca, S; Gürses, A; Ejder, M; Açikyildiz, M
2004-09-15
The adsorption of phosphate from aqueous solution on dolomite was investigated at 20 and 40 degrees C in terms of pseudo-second-order mechanism for chemical adsorption as well as an intraparticle diffusion mechanism process. Adsorption was changed with increased contact time, initial phosphate concentration, temperature, solution pH. A pseudo-second-order model and intraparticle diffusion model have been developed to predict the rate constants of adsorption and equilibrium capacities. The activation energy of adsorption can be evaluated using the pseudo-second-order rate constants. The adsorption of phosphate onto dolomite are an exothermically activated process. A relatively low activation energy and a model highly fitting to intraparticle diffusion suggest that the adsorption of phosphate by dolomite may involve not only physical but also chemisorption. This was likely due to its combined control of chemisorption and intraparticle diffusion. However, for phosphate/dolomite system chemical reaction is important and significant in the rate-controlling step, and for the adsorption of phosphate onto dolomite the pseudo-second-order chemical reaction kinetics provides the best correlation of the experimental data.
Wen Zhu; Junsheng Liu; Meng Li
2014-01-01
A series of zwitterionic hybrid membranes were prepared via the ring opening of 1,3-propanesultone with the amine groups in the chains of TMSPEDA and a subsequent sol-gel process. Their kinetic models for strontium removal were investigated using three two-parameter kinetic equations (i.e., Lagergren pseudo-first order, pseudo-second order, and Elovich models). Adsorption mechanism was evaluated using intraparticle diffusion model, diffusion-chemisorption model, and Boyd equation. It was foun...
Generalized Reduced Order Model Generation Project
National Aeronautics and Space Administration — M4 Engineering proposes to develop a generalized reduced order model generation method. This method will allow for creation of reduced order aeroservoelastic state...
Zhu, Wen; Liu, Junsheng; Li, Meng
2014-01-01
A series of zwitterionic hybrid membranes were prepared via the ring opening of 1,3-propanesultone with the amine groups in the chains of TMSPEDA and a subsequent sol-gel process. Their kinetic models for strontium removal were investigated using three two-parameter kinetic equations (i.e., Lagergren pseudo-first order, pseudo-second order, and Elovich models). Adsorption mechanism was evaluated using intraparticle diffusion model, diffusion-chemisorption model, and Boyd equation. It was found that the adsorption of strontium ions on these zwitterionic hybrid membranes fitted well with the Lagergren pseudo-second order model. Mechanism insights suggested that diffusion-chemisorption was one of the main adsorption mechanisms. Boyd equation exhibited that film-diffusion mechanism might be the control process during the starting period. These findings are very useful in strontium removal from the stimulated radioactive wastewater.
Directory of Open Access Journals (Sweden)
Wen Zhu
2014-01-01
Full Text Available A series of zwitterionic hybrid membranes were prepared via the ring opening of 1,3-propanesultone with the amine groups in the chains of TMSPEDA and a subsequent sol-gel process. Their kinetic models for strontium removal were investigated using three two-parameter kinetic equations (i.e., Lagergren pseudo-first order, pseudo-second order, and Elovich models. Adsorption mechanism was evaluated using intraparticle diffusion model, diffusion-chemisorption model, and Boyd equation. It was found that the adsorption of strontium ions on these zwitterionic hybrid membranes fitted well with the Lagergren pseudo-second order model. Mechanism insights suggested that diffusion-chemisorption was one of the main adsorption mechanisms. Boyd equation exhibited that film-diffusion mechanism might be the control process during the starting period. These findings are very useful in strontium removal from the stimulated radioactive wastewater.
Zhu, Wen; Li, Meng
2014-01-01
A series of zwitterionic hybrid membranes were prepared via the ring opening of 1,3-propanesultone with the amine groups in the chains of TMSPEDA and a subsequent sol-gel process. Their kinetic models for strontium removal were investigated using three two-parameter kinetic equations (i.e., Lagergren pseudo-first order, pseudo-second order, and Elovich models). Adsorption mechanism was evaluated using intraparticle diffusion model, diffusion-chemisorption model, and Boyd equation. It was found that the adsorption of strontium ions on these zwitterionic hybrid membranes fitted well with the Lagergren pseudo-second order model. Mechanism insights suggested that diffusion-chemisorption was one of the main adsorption mechanisms. Boyd equation exhibited that film-diffusion mechanism might be the control process during the starting period. These findings are very useful in strontium removal from the stimulated radioactive wastewater. PMID:25405224
Adsorption kinetics of NO on ordered mesoporous carbon (OMC) and cerium-containing OMC (Ce-OMC)
Energy Technology Data Exchange (ETDEWEB)
Chen, Jinghuan; Cao, Feifei; Chen, Songze; Ni, Mingjiang; Gao, Xiang, E-mail: xgao1@zju.edu.cn; Cen, Kefa
2014-10-30
Graphical abstract: - Highlights: • Ordered mesoporous carbon (OMC) and Ce-OMC were used for NO adsorption. • The NO adsorption capacity of OMC was two times larger than that of activated carbon. • With the addition of cerium both adsorption capacity and adsorption rate increased. • The pseudo-second-order model was the most suitable model for NO adsorption on OMC. • Intraparticle diffusion was the rate controlling step for NO adsorption. - Abstract: Ordered mesoporous carbon (OMC) and cerium-containing OMC (Ce-OMC) were prepared using evaporation-induced self-assembly (EISA) method and used to adsorb NO. N{sub 2} sorption, X-ray diffraction (XRD) and transmission electron microscopy (TEM) were used to confirm their structures. The results showed that the ordered and uniform structures were successfully synthesized and with the introduction of cerium pore properties were not significantly changed. The NO adsorption capacity of OMC was two times larger than that of activated carbon (AC). With the introduction of cerium both the adsorption capacity and the adsorption rate were improved. The effects of residence time and oxygen concentration on NO adsorption were also investigated. Oxygen played an important role in the NO adsorption (especially in the form of chemisorption) and residence time had small influence on the NO adsorption capacity. The NO adsorption kinetics was analyzed using pseudo-first-order, pseudo-second-order, Elovich equation and intraparticle diffusion models. The results indicated that the NO adsorption process can be divided into rapid adsorption period, slow adsorption period, and equilibrium adsorption period. The pseudo-second-order model was the most suitable model for NO adsorption on OMC and Ce-OMC. The rate controlling step was the intraparticle diffusion together with the adsorption reaction.
Fractional Order Models of Industrial Pneumatic Controllers
Directory of Open Access Journals (Sweden)
Abolhassan Razminia
2014-01-01
Full Text Available This paper addresses a new approach for modeling of versatile controllers in industrial automation and process control systems such as pneumatic controllers. Some fractional order dynamical models are developed for pressure and pneumatic systems with bellows-nozzle-flapper configuration. In the light of fractional calculus, a fractional order derivative-derivative (FrDD controller and integral-derivative (FrID are remodeled. Numerical simulations illustrate the application of the obtained theoretical results in simple examples.
Dynamical models of happiness with fractional order
Song, Lei; Xu, Shiyun; Yang, Jianying
2010-03-01
This present study focuses on a dynamical model of happiness described through fractional-order differential equations. By categorizing people of different personality and different impact factor of memory (IFM) with different set of model parameters, it is demonstrated via numerical simulations that such fractional-order models could exhibit various behaviors with and without external circumstance. Moreover, control and synchronization problems of this model are discussed, which correspond to the control of emotion as well as emotion synchronization in real life. This study is an endeavor to combine the psychological knowledge with control problems and system theories, and some implications for psychotherapy as well as hints of a personal approach to life are both proposed.
Steganalysis Using Partially Ordered Markov Models
Davidson, Jennifer; Jalan, Jaikishan
The field of steganalysis has blossomed prolifically in the past few years, providing the community with a number of very good blind steganalyzers. Features for blind steganalysis are generated in many different ways, typically using statistical measures. This paper presents a new image modeling technique for steganalysis that uses as features the conditional probabilities described by a stochastic model called a partially ordered Markov model (POMM). The POMM allows concise modeling of pixel dependencies among quantized discrete cosine transform coefficients. We develop a steganalyzer based on support vector machines that distinguishes between cover and stego JPEG images using 98 POMM features. We show that the proposed steganalyzer outperforms two comparative Markov-based steganalyzers [25,6] and outperforms a third steganalyzer [23] on half of the tested classes, by testing our approach with many different image databases on five embedding algorithms, with a total of 20,000 images.
Orbital-only models: ordering and excitations
van den Brink, Jeroen
2004-12-01
We consider orbital-only models in Mott insulators, where the orbital orbital interactions are either due to Jahn Teller distortions or due to the Kugel Khomskii superexchange. This leads to highly anisotropic and frustrated orbital Hamiltonians. For two-fold degenerate eg systems, both types of orbital interactions lead to the same form of the Hamiltonian—the 120° model. In both cases, the predicted symmetry of the orbital ordering is the same, although different from the one observed experimentally. The orbital operators that appear in the two kinds of orbital-only Hamiltonians are different. In the case of superexchange, the orbital degrees of freedom are represented by quantum pseudo-spin 1/2 operators. But when the interactions are Jahn Teller mediated and the coupling with the lattice is strong, the orbital operators are essentially classical pseudospins. Thus as a function of the relative coupling strengths, a quantum-to-classical crossover is expected. For three-fold degenerate t2g orbitals, the Jahn Teller coupling gives rise to a particular type of orbital compass models. We point out that fluctuations—whether due to quantum effects or finite temperature—are of prime importance for ordering in the 120° and orbital compass models. The fluctuations generally generate a gap in the orbital excitation spectrum. These orbital excitations—orbitons—are hybrid excitations that carry both a lattice Jahn Teller and a magnetic Kugel Khomskii character.
Second-order continuum traffic flow model
Wagner, C.; Hoffmann, C.; Sollacher, R.; Wagenhuber, J.; Schürmann, B.
1996-11-01
A second-order traffic flow model is derived from microscopic equations and is compared to existing models. In order to build in different driver characteristics on the microscopic level, we exploit the idea of an additional phase-space variable, called the desired velocity originally introduced by Paveri-Fontana [Trans. Res. 9, 225 (1975)]. By taking the moments of Paveri-Fontana's Boltzmann-like ansatz, a hierachy of evolution equations is found. This hierarchy is closed by neglecting cumulants of third and higher order in the cumulant expansion of the distribution function, thus leading to Euler-like traffic equations. As a consequence of the desired velocity, we find dynamical quantities, which are the mean desired velocity, the variance of the desired velocity, and the covariance of actual and desired velocity. Through these quantities an alternative explanation for the onset of traffic clusters can be given, i.e., a spatial variation of the variance of the desired velocity can cause the formation of a traffic jam. Furthermore, by taking into account the finite car length, Paveri-Fontana's equation is generalized to the high-density regime eventually producing corrections to the macroscopic equations. The relevance of the present dynamic quantities is demonstrated by numerical simulations.
Are Quantum Models for Order Effects Quantum?
Moreira, Catarina; Wichert, Andreas
2017-12-01
The application of principles of Quantum Mechanics in areas outside of physics has been getting increasing attention in the scientific community in an emergent disciplined called Quantum Cognition. These principles have been applied to explain paradoxical situations that cannot be easily explained through classical theory. In quantum probability, events are characterised by a superposition state, which is represented by a state vector in a N-dimensional vector space. The probability of an event is given by the squared magnitude of the projection of this superposition state into the desired subspace. This geometric approach is very useful to explain paradoxical findings that involve order effects, but do we really need quantum principles for models that only involve projections? This work has two main goals. First, it is still not clear in the literature if a quantum projection model has any advantage towards a classical projection. We compared both models and concluded that the Quantum Projection model achieves the same results as its classical counterpart, because the quantum interference effects play no role in the computation of the probabilities. Second, it intends to propose an alternative relativistic interpretation for rotation parameters that are involved in both classical and quantum models. In the end, instead of interpreting these parameters as a similarity measure between questions, we propose that they emerge due to the lack of knowledge concerned with a personal basis state and also due to uncertainties towards the state of world and towards the context of the questions.
Reduced-order modelling numerical homogenization.
Abdulle, A; Bai, Y
2014-08-06
A general framework to combine numerical homogenization and reduced-order modelling techniques for partial differential equations (PDEs) with multiple scales is described. Numerical homogenization methods are usually efficient to approximate the effective solution of PDEs with multiple scales. However, classical numerical homogenization techniques require the numerical solution of a large number of so-called microproblems to approximate the effective data at selected grid points of the computational domain. Such computations become particularly expensive for high-dimensional, time-dependent or nonlinear problems. In this paper, we explain how numerical homogenization method can benefit from reduced-order modelling techniques that allow one to identify offline and online computational procedures. The effective data are only computed accurately at a carefully selected number of grid points (offline stage) appropriately 'interpolated' in the online stage resulting in an online cost comparable to that of a single-scale solver. The methodology is presented for a class of PDEs with multiple scales, including elliptic, parabolic, wave and nonlinear problems. Numerical examples, including wave propagation in inhomogeneous media and solute transport in unsaturated porous media, illustrate the proposed method. © 2014 The Author(s) Published by the Royal Society. All rights reserved.
Adsorption kinetics of NO on ordered mesoporous carbon (OMC) and cerium-containing OMC (Ce-OMC)
Chen, Jinghuan; Cao, Feifei; Chen, Songze; Ni, Mingjiang; Gao, Xiang; Cen, Kefa
2014-10-01
Ordered mesoporous carbon (OMC) and cerium-containing OMC (Ce-OMC) were prepared using evaporation-induced self-assembly (EISA) method and used to adsorb NO. N2 sorption, X-ray diffraction (XRD) and transmission electron microscopy (TEM) were used to confirm their structures. The results showed that the ordered and uniform structures were successfully synthesized and with the introduction of cerium pore properties were not significantly changed. The NO adsorption capacity of OMC was two times larger than that of activated carbon (AC). With the introduction of cerium both the adsorption capacity and the adsorption rate were improved. The effects of residence time and oxygen concentration on NO adsorption were also investigated. Oxygen played an important role in the NO adsorption (especially in the form of chemisorption) and residence time had small influence on the NO adsorption capacity. The NO adsorption kinetics was analyzed using pseudo-first-order, pseudo-second-order, Elovich equation and intraparticle diffusion models. The results indicated that the NO adsorption process can be divided into rapid adsorption period, slow adsorption period, and equilibrium adsorption period. The pseudo-second-order model was the most suitable model for NO adsorption on OMC and Ce-OMC. The rate controlling step was the intraparticle diffusion together with the adsorption reaction.
National Research Council Canada - National Science Library
Notaros, Branislav M; Ilic, Milan M; Djordjevic, Miroslav
2004-01-01
...), method of moments (MoM), and physical optics (PO). The simulations combine higher order geometrical modeling and higher order field/current modeling, which is referred to as double-higher-order modeling...
The Optimal Economic Order: the simplest model
J. Tinbergen (Jan)
1992-01-01
textabstractIn the last five years humanity has become faced with the problem of the optimal socioeconomic order more clearly than ever. After the confrontation of capitalism and socialism, which was the core of the Marxist thesis, the fact transpired that capitalism was not the optimal order. It
Order and disorder in product innovation models
Pina e Cunha, Miguel; Gomes, Jorge F.S.; Gomes, J.F.
2003-01-01
This article argues that the conceptual development of product innovation models goes hand in hand with paradigmatic changes in the field of organization science. Remarkable similarities in the change of organizational perspectives and product innovation models are noticeable. To illustrate how
Model selection criteria : how to evaluate order restrictions
Kuiper, R.M.
2012-01-01
Researchers often have ideas about the ordering of model parameters. They frequently have one or more theories about the ordering of the group means, in analysis of variance (ANOVA) models, or about the ordering of coefficients corresponding to the predictors, in regression models.A researcher might
Partial Orders and Fully Abstract Models for Concurrency
DEFF Research Database (Denmark)
Engberg, Uffe Henrik
1990-01-01
In this thesis sets of labelled partial orders are employed as fundamental mathematical entities for modelling nondeterministic and concurrent processes thereby obtaining so-called noninterleaving semantics. Based on different closures of sets of labelled partial orders, simple algebraic languages...
Chen, Jonathan H; Goldstein, Mary K; Asch, Steven M; Mackey, Lester; Altman, Russ B
2017-05-01
Build probabilistic topic model representations of hospital admissions processes and compare the ability of such models to predict clinical order patterns as compared to preconstructed order sets. The authors evaluated the first 24 hours of structured electronic health record data for > 10 K inpatients. Drawing an analogy between structured items (e.g., clinical orders) to words in a text document, the authors performed latent Dirichlet allocation probabilistic topic modeling. These topic models use initial clinical information to predict clinical orders for a separate validation set of > 4 K patients. The authors evaluated these topic model-based predictions vs existing human-authored order sets by area under the receiver operating characteristic curve, precision, and recall for subsequent clinical orders. Existing order sets predict clinical orders used within 24 hours with area under the receiver operating characteristic curve 0.81, precision 16%, and recall 35%. This can be improved to 0.90, 24%, and 47% ( P topic models to summarize clinical data into up to 32 topics. Many of these latent topics yield natural clinical interpretations (e.g., "critical care," "pneumonia," "neurologic evaluation"). Existing order sets tend to provide nonspecific, process-oriented aid, with usability limitations impairing more precise, patient-focused support. Algorithmic summarization has the potential to breach this usability barrier by automatically inferring patient context, but with potential tradeoffs in interpretability. Probabilistic topic modeling provides an automated approach to detect thematic trends in patient care and generate decision support content. A potential use case finds related clinical orders for decision support.
Fractional-order in a macroeconomic dynamic model
David, S. A.; Quintino, D. D.; Soliani, J.
2013-10-01
In this paper, we applied the Riemann-Liouville approach in order to realize the numerical simulations to a set of equations that represent a fractional-order macroeconomic dynamic model. It is a generalization of a dynamic model recently reported in the literature. The aforementioned equations have been simulated for several cases involving integer and non-integer order analysis, with some different values to fractional order. The time histories and the phase diagrams have been plotted to visualize the effect of fractional order approach. The new contribution of this work arises from the fact that the macroeconomic dynamic model proposed here involves the public sector deficit equation, which renders the model more realistic and complete when compared with the ones encountered in the literature. The results reveal that the fractional-order macroeconomic model can exhibit a real reasonable behavior to macroeconomics systems and might offer greater insights towards the understanding of these complex dynamic systems.
Proposed higher order continuum-based models for an elastic ...
African Journals Online (AJOL)
The resulting differential equations are similar in form and order to a high-order model developed earlier by Reissner based on a number of simplifying assumptions, but with different coefficients dependant on Poisson ratio. With the help of appropriately selected mechanical models, it has been shown that all of the new ...
On the Economic Order Quantity Model With Transportation Costs
S.I. Birbil (Ilker); K. Bulbul; J.B.G. Frenk (Hans); H.M. Mulder (Martyn)
2009-01-01
textabstractWe consider an economic order quantity type model with unit out-of-pocket holding costs, unit opportunity costs of holding, fixed ordering costs and general transportation costs. For these models, we analyze the associated optimization problem and derive an easy procedure for determining
Model-order reduction of biochemical reaction networks
Rao, Shodhan; Schaft, Arjan van der; Eunen, Karen van; Bakker, Barbara M.; Jayawardhana, Bayu
2013-01-01
In this paper we propose a model-order reduction method for chemical reaction networks governed by general enzyme kinetics, including the mass-action and Michaelis-Menten kinetics. The model-order reduction method is based on the Kron reduction of the weighted Laplacian matrix which describes the
A comparison of zero-order, first-order, and monod biotransformation models
Bekins, B.A.; Warren, E.; Godsy, E.M.
1998-01-01
Under some conditions, a first-order kinetic model is a poor representation of biodegradation in contaminated aquifers. Although it is well known that the assumption of first-order kinetics is valid only when substrate concentration, S, is much less than the half-saturation constant, K(s), this assumption is often made without verification of this condition. We present a formal error analysis showing that the relative error in the first-order approximation is S/K(S) and in the zero-order approximation the error is K(s)/S. We then examine the problems that arise when the first-order approximation is used outside the range for which it is valid. A series of numerical simulations comparing results of first- and zero-order rate approximations to Monod kinetics for a real data set illustrates that if concentrations observed in the field are higher than K(s), it may better to model degradation using a zero-order rate expression. Compared with Monod kinetics, extrapolation of a first-order rate to lower concentrations under-predicts the biotransformation potential, while extrapolation to higher concentrations may grossly over-predict the transformation rate. A summary of solubilities and Monod parameters for aerobic benzene, toluene, and xylene (BTX) degradation shows that the a priori assumption of first-order degradation kinetics at sites contaminated with these compounds is not valid. In particular, out of six published values of KS for toluene, only one is greater than 2 mg/L, indicating that when toluene is present in concentrations greater than about a part per million, the assumption of first-order kinetics may be invalid. Finally, we apply an existing analytical solution for steady-state one-dimensional advective transport with Monod degradation kinetics to a field data set.A formal error analysis is presented showing that the relative error in the first-order approximation is S/KS and in the zero-order approximation the error is KS/S where S is the substrate
Semi-nonparametric estimation of extended ordered probit models
Mark Stewart
2002-01-01
This paper presents a semi-nonparametric estimator for a series of generalized models that nest the ordered probit model and thereby relax the distributional assumption in that model. It describes a new Stata command for fitting such models and presents an illustration of the approach. Copyright 2004 by StataCorp LP.
Pfarr, Christian; Schmid, Andreas; Schneider, Udo
2010-01-01
Estimation procedures for ordered categories usually assume that the estimated coefficients of independent variables do not vary between the categories (parallel-lines assumption). This view neglects possible heterogeneous effects of some explaining factors. This paper describes the use of an autofit option for identifying variables that meet the parallel-lines assumption when estimating a random effects generalized ordered probit model. We combine the test procedure developed by Richard Will...
Model order reduction techniques with applications in finite element analysis
Qu, Zu-Qing
2004-01-01
Despite the continued rapid advance in computing speed and memory the increase in the complexity of models used by engineers persists in outpacing them. Even where there is access to the latest hardware, simulations are often extremely computationally intensive and time-consuming when full-blown models are under consideration. The need to reduce the computational cost involved when dealing with high-order/many-degree-of-freedom models can be offset by adroit computation. In this light, model-reduction methods have become a major goal of simulation and modeling research. Model reduction can also ameliorate problems in the correlation of widely used finite-element analyses and test analysis models produced by excessive system complexity. Model Order Reduction Techniques explains and compares such methods focusing mainly on recent work in dynamic condensation techniques: - Compares the effectiveness of static, exact, dynamic, SEREP and iterative-dynamic condensation techniques in producing valid reduced-order mo...
Modeling Human Behaviour with Higher Order Logic: Insider Threats
DEFF Research Database (Denmark)
Boender, Jaap; Ivanova, Marieta Georgieva; Kammuller, Florian
2014-01-01
to and implemented in Higher Order Logic. We validate this working hypothesis by revisiting Weber’s understanding explanation. We focus on constructive realism in the context of logical explanation. We review Higher Order Logic (HOL) as a foundation for computer science and summarize its use of theories relating...... it to the sociological process of logical explanation. As a case study on modeling human behaviour, we present the modeling and analysis of insider threats as a Higher Order Logic theory in Isabelle/HOL. We show how each of the three step process of sociological explanation can be seen in our modeling of insider’s state...
Building Higher-Order Markov Chain Models with EXCEL
Ching, Wai-Ki; Fung, Eric S.; Ng, Michael K.
2004-01-01
Categorical data sequences occur in many applications such as forecasting, data mining and bioinformatics. In this note, we present higher-order Markov chain models for modelling categorical data sequences with an efficient algorithm for solving the model parameters. The algorithm can be implemented easily in a Microsoft EXCEL worksheet. We give a…
Phase-field-crystal model for ordered crystals.
Alster, Eli; Elder, K R; Hoyt, Jeffrey J; Voorhees, Peter W
2017-02-01
We describe a general method to model multicomponent ordered crystals using the phase-field-crystal (PFC) formalism. As a test case, a generic B2 compound is investigated. We are able to produce a line of either first-order or second-order order-disorder phase transitions, features that have not been incorporated in existing PFC approaches. Further, it is found that the only elastic constant for B2 that depends on ordering is C_{11}. This B2 model is then used to study antiphase boundaries (APBs). The APBs are shown to reproduce classical mean-field results. Dynamical simulations of ordering across small-angle grain boundaries predict that dislocation cores pin the evolution of APBs.
Multi-Criteria Model for Determining Order Size
Directory of Open Access Journals (Sweden)
Katarzyna Jakowska-Suwalska
2013-01-01
Full Text Available A multi-criteria model for determining the order size for materials used in production has been presented. It was assumed that the consumption rate of each material is a random variable with a known probability distribution. Using such a model, in which the purchase cost of materials ordered is limited, three criteria were considered: order size, probability of a lack of materials in the production process, and deviations in the order size from the consumption rate in past periods. Based on an example, it has been shown how to use the model to determine the order sizes for polyurethane adhesive and wood in a hard-coal mine. (original abstract
Atmospheric Turbulence Modeling for Aerospace Vehicles: Fractional Order Fit
Kopasakis, George (Inventor)
2015-01-01
An improved model for simulating atmospheric disturbances is disclosed. A scale Kolmogorov spectral may be scaled to convert the Kolmogorov spectral into a finite energy von Karman spectral and a fractional order pole-zero transfer function (TF) may be derived from the von Karman spectral. Fractional order atmospheric turbulence may be approximated with an integer order pole-zero TF fit, and the approximation may be stored in memory.
The second-order decomposition model of nonlinear irregular waves
DEFF Research Database (Denmark)
Yang, Zhi Wen; Bingham, Harry B.; Li, Jin Xuan
2013-01-01
into the first- and the second-order super-harmonic as well as the second-order sub-harmonic components by transferring them into an identical Fourier frequency-space and using a Newton-Raphson iteration method. In order to evaluate the present model, a variety of monochromatic waves and the second......A new method to decompose the nonlinear irregular waves is proposed. The second-order potential flow theory is employed to construct the relation of the second-order items solution by deriving the transfer function between the first- and the second-order components. Target waves are decomposed......-order nonlinear irregular waves over a broad range of frequencies have been analyzed, and the effects on wave nonlinearity are analyzed. The experimental results show that the present method is reasonably effective for the wave decomposition....
A bivariate cumulative probit regression model for ordered categorical data.
Kim, K
1995-06-30
This paper proposes a latent variable regression model for bivariate ordered categorical data and develops the necessary numerical procedure for parameter estimation. The proposed model is an extension of the standard bivariate probit model for dichotomous data to ordered categorical data with more than two categories for each margin. In addition, the proposed model allows for different covariates for the margins, which is characteristic of data from typical ophthalmological studies. It utilizes the stochastic ordering implicit in the data and the correlation coefficient of the bivariate normal distribution in expressing intra-subject dependency. Illustration of the proposed model uses data from the Wisconsin Epidemiologic Study of Diabetic Retinopathy for identifying risk factors for diabetic retinopathy among younger-onset diabetics. The proposed regression model also applies to other clinical or epidemiological studies that involve paired organs.
A variable-order fractal derivative model for anomalous diffusion
Directory of Open Access Journals (Sweden)
Liu Xiaoting
2017-01-01
Full Text Available This paper pays attention to develop a variable-order fractal derivative model for anomalous diffusion. Previous investigations have indicated that the medium structure, fractal dimension or porosity may change with time or space during solute transport processes, results in time or spatial dependent anomalous diffusion phenomena. Hereby, this study makes an attempt to introduce a variable-order fractal derivative diffusion model, in which the index of fractal derivative depends on temporal moment or spatial position, to characterize the above mentioned anomalous diffusion (or transport processes. Compared with other models, the main advantages in description and the physical explanation of new model are explored by numerical simulation. Further discussions on the dissimilitude such as computational efficiency, diffusion behavior and heavy tail phenomena of the new model and variable-order fractional derivative model are also offered.
Higher order comoments of multifactor models and asset allocation
Boudt, K.M.R.; Lu, W.; Peeters, B.
2015-01-01
Accurate estimates of the higher order comoments are needed in asset allocation. We derive explicit formulas for the higher order comoments under the assumption that stock returns are generated by a multifactor model and show that this assumption leads to a substantial reduction in the number of
Planar ordering in the plaquette-only gonihedric Ising model
Directory of Open Access Journals (Sweden)
Marco Mueller
2015-05-01
Full Text Available In this paper we conduct a careful multicanonical simulation of the isotropic 3d plaquette (“gonihedric” Ising model and confirm that a planar, fuki-nuke type order characterises the low-temperature phase of the model. From consideration of the anisotropic limit of the model we define a class of order parameters which can distinguish the low- and high-temperature phases in both the anisotropic and isotropic cases. We also verify the recently voiced suspicion that the order parameter like behaviour of the standard magnetic susceptibility χm seen in previous Metropolis simulations was an artefact of the algorithm failing to explore the phase space of the macroscopically degenerate low-temperature phase. χm is therefore not a suitable order parameter for the model.
Planar ordering in the plaquette-only gonihedric Ising model
Mueller, Marco; Janke, Wolfhard; Johnston, Desmond A.
2015-05-01
In this paper we conduct a careful multicanonical simulation of the isotropic 3d plaquette ("gonihedric") Ising model and confirm that a planar, fuki-nuke type order characterises the low-temperature phase of the model. From consideration of the anisotropic limit of the model we define a class of order parameters which can distinguish the low- and high-temperature phases in both the anisotropic and isotropic cases. We also verify the recently voiced suspicion that the order parameter like behaviour of the standard magnetic susceptibility χm seen in previous Metropolis simulations was an artefact of the algorithm failing to explore the phase space of the macroscopically degenerate low-temperature phase. χm is therefore not a suitable order parameter for the model.
Front dynamics in fractional-order epidemic models.
Hanert, Emmanuel; Schumacher, Eva; Deleersnijder, Eric
2011-06-21
A number of recent studies suggest that human and animal mobility patterns exhibit scale-free, Lévy-flight dynamics. However, current reaction-diffusion epidemics models do not account for the superdiffusive spread of modern epidemics due to Lévy flights. We have developed a SIR model to simulate the spatial spread of a hypothetical epidemic driven by long-range displacements in the infective and susceptible populations. The model has been obtained by replacing the second-order diffusion operator by a fractional-order operator. Theoretical developments and numerical simulations show that fractional-order diffusion leads to an exponential acceleration of the epidemic's front and a power-law decay of the front's leading tail. Our results indicate the potential of fractional-order reaction-diffusion models to represent modern epidemics. Copyright © 2011 Elsevier Ltd. All rights reserved.
Multivariable frequency weighted model order reduction for control synthesis
Schmidt, David K.
1989-01-01
Quantitative criteria are presented for model simplification, or order reduction, such that the reduced order model may be used to synthesize and evaluate a control law, and the stability robustness obtained using the reduced order model will be preserved when controlling the full-order system. The error introduced due to model simplification is treated as modeling uncertainty, and some of the results from multivariate robustness theory are brought to bear on the model simplification problem. A numerical procedure developed previously is shown to lead to results that meet the necessary criteria. The procedure is applied to reduce the model of a flexible aircraft. Also, the importance of the control law itself, in meeting the modeling criteria, is underscored. An example is included that demonstrates that an apparently robust control law actually amplifies modest modeling errors in the critical frequency region, and leads to undesirable results. The cause of this problem is associated with the canceling of lightly damped transmission zeroes in the plant. An attempt is made to expand on some of the earlier results and to further clarify the theoretical basis behind the proposed methodology.
Directory of Open Access Journals (Sweden)
Khalifa Riahi
2017-01-01
Full Text Available The removal of phosphates from aqueous solutions by adsorption onto date palm fibers (DPF has been studied in batch mode. The aim of this study was to understand the mechanisms that govern phosphate sorption and find an appropriate model for the kinetics of removal. In order to investigate the mechanism of sorption and potential rate controlling steps, pseudo first-order, pseudo second-order, intra-particle diffusion and the Elovich equations have been used to test experimental data. Kinetic analysis of the four models has been carried out for initial phosphate concentration in the range of 30–110 mg/L. The rate constants for the four models have been determined and the correlation coefficients have been calculated in order to assess which model provides the best fit predicted data with experimental results. Seven statistical functions were used to estimate the error deviations between experimental and theoretically predicted kinetic adsorption values, including the average relative error deviation (ARED, Marquardt’s percent standard error deviation (MPSED, the hybrid fractional error function (HYBRID, the sum of the squares of the errors (SSE and three alternative statistical functions, including the Chi-square test, the F-test and Student’s T-test. The results showed that, both Elovich equation and pseudo second-order equation provide the best fit to experimental data for different initial phosphate concentrations.
The Complexity of Model Checking Higher-Order Fixpoint Logic
DEFF Research Database (Denmark)
Axelsson, Roland; Lange, Martin; Somla, Rafal
2007-01-01
provides complexity results for its model checking problem. In particular we consider its fragments HFLk,m which are formed using types of bounded order k and arity m only. We establish k-ExpTime-completeness for model checking each HFLk,m fragment. For the upper bound we reduce the problem to the problem...
Next-to-leading order corrections to the valon model
Indian Academy of Sciences (India)
A seminumerical solution to the valon model at next-to-leading order (NLO) in the Laguerre polynomials is presented. We used the valon model to generate the structure of proton with respect to the Laguerre polynomials method. The results are compared with H1 data and other parametrizations.
POD-DEIM model order reduction for strain softening viscoplasticity
Ghavamian, F.; Tiso, P.; Simone, A.
2017-01-01
We demonstrate a Model Order Reduction technique for a system of nonlinear equations arising from the Finite Element Method (FEM) discretization of the three-dimensional quasistatic equilibrium equation equipped with a Perzyna viscoplasticity constitutive model. The procedure employs the Proper
Control of fluid flows using multivariate spline reduced order models
Tol, H.J.; de Visser, C.C.; Kotsonis, M.
2016-01-01
This paper presents a study on control of fluid flows using multivariate spline reduced order models. A new approach is presented for model reduction of the incompressible Navier-Stokes equations using multivariate splines defined on triangulations. State space descriptions are derived that can be
Data-Driven Model Order Reduction for Bayesian Inverse Problems
Cui, Tiangang
2014-01-06
One of the major challenges in using MCMC for the solution of inverse problems is the repeated evaluation of computationally expensive numerical models. We develop a data-driven projection- based model order reduction technique to reduce the computational cost of numerical PDE evaluations in this context.
First-order transitions for some generalized XY models
Enter, Aernout C.D. van; Romano, Silvano; Zagrebnov, Valentin A.
2006-01-01
In this letter we demonstrate the occurrence of first-order transitions in temperature for some recently introduced generalized XY models, and also point out the connection between them and annealed site-diluted (lattice-gas) continuous-spin models.
Testing static tradeoff theiry against pecking order models of capital ...
African Journals Online (AJOL)
We test two models with the purpose of finding the best empirical explanation for corporate financing choice of a cross section of 27 Nigerian quoted companies. The models were developed to represent the Static tradeoff Theory and the Pecking order Theory of capital structure with a view to make comparison between ...
Reduced-Order Modeling: New Approaches for Computational Physics
Beran, Philip S.; Silva, Walter A.
2001-01-01
In this paper, we review the development of new reduced-order modeling techniques and discuss their applicability to various problems in computational physics. Emphasis is given to methods ba'sed on Volterra series representations and the proper orthogonal decomposition. Results are reported for different nonlinear systems to provide clear examples of the construction and use of reduced-order models, particularly in the multi-disciplinary field of computational aeroelasticity. Unsteady aerodynamic and aeroelastic behaviors of two- dimensional and three-dimensional geometries are described. Large increases in computational efficiency are obtained through the use of reduced-order models, thereby justifying the initial computational expense of constructing these models and inotivatim,- their use for multi-disciplinary design analysis.
A Fractional Order Recovery SIR Model from a Stochastic Process.
Angstmann, C N; Henry, B I; McGann, A V
2016-03-01
Over the past several decades, there has been a proliferation of epidemiological models with ordinary derivatives replaced by fractional derivatives in an ad hoc manner. These models may be mathematically interesting, but their relevance is uncertain. Here we develop an SIR model for an epidemic, including vital dynamics, from an underlying stochastic process. We show how fractional differential operators arise naturally in these models whenever the recovery time from the disease is power-law distributed. This can provide a model for a chronic disease process where individuals who are infected for a long time are unlikely to recover. The fractional order recovery model is shown to be consistent with the Kermack-McKendrick age-structured SIR model, and it reduces to the Hethcote-Tudor integral equation SIR model. The derivation from a stochastic process is extended to discrete time, providing a stable numerical method for solving the model equations. We have carried out simulations of the fractional order recovery model showing convergence to equilibrium states. The number of infecteds in the endemic equilibrium state increases as the fractional order of the derivative tends to zero.
Directory of Open Access Journals (Sweden)
Carolina Martínez-Sánchez
2013-01-01
Full Text Available This work presents the use of potentiometric measurements for kinetic studies of biosorption of Cd2+ ions from aqueous solutions on Eichhornia crassipes roots. The open circuit potential of the Cd/Cd2+ electrode of the first kind was measured during the bioadsorption process. The amount of Cd2+ ions accumulated was determined in real time. The data were fit to different models, with the pseudo-second-order model proving to be the best in describing the data. The advantages and limitations of the methodology proposed relative to the traditional method are discussed.
Mathematical modelling of fractional order circuit elements and bioimpedance applications
Moreles, Miguel Angel; Lainez, Rafael
2017-05-01
In this work a classical derivation of fractional order circuits models is presented. Generalised constitutive equations in terms of fractional Riemann-Liouville derivatives are introduced in the Maxwell's equations for each circuit element. Next the Kirchhoff voltage law is applied in a RCL circuit configuration. It is shown that from basic properties of Fractional Calculus, a fractional differential equation model with Caputo derivatives is obtained. Thus standard initial conditions apply. Finally, models for bioimpedance are revisited.
Estimation in second order dependency model for multivariate binary data
Energy Technology Data Exchange (ETDEWEB)
Ip, E.H.S.
1995-04-01
This paper proposes a normal model for multivariate binary data. The normal model is an extension to the bivariate normal model for 2{times}2 contingency table proposed by Pearson. A stochastic algorithm using Gibbs sampler is developed to estimate the parameters for high dimensional binary data. The method is compared to the second order dependency Bahadur-Lazarsfeld representation of binary density. Two examples, one from psychological testing and one from medical science, are used to substantiate the above ideas.
Abnormal Waves Modelled as Second-order Conditional Waves
DEFF Research Database (Denmark)
Jensen, Jørgen Juncher
2005-01-01
The paper presents results for the expected second order short-crested wave conditional of a given wave crest at a specific point in time and space. The analysis is based on the second order Sharma and Dean shallow water wave theory. Numerical results showing the importance of the spectral density......, the water depth and the directional spreading on the conditional mean wave profile are presented. Application of conditional waves to model and explain abnormal waves, e.g. the well-known New Year Wave measured at the Draupner platform January 1st 1995, is discussed. Whereas the wave profile can be modelled...... quite well by the second order conditional wave including directional spreading and finite water depth the probability to encounter such a wave is still, however, extremely rare. The use of the second order conditional wave as initial condition to a fully non-linear three-dimensional analysis...
Second-order TGV model for Poisson noise image restoration.
Li, Hou-Biao; Wang, Jun-Yan; Dou, Hong-Xia
2016-01-01
Restoring Poissonian noise images have drawn a lot of attention in recent years. There are many regularization methods to solve this problem and one of the most famous methods is the total variation model. In this paper, by adding a quadratic regularization on TGV regularization part, a new image restoration model is proposed based on second-order total generalized variation regularization. Then the split Bregman iteration algorithm was used to solve this new model. The experimental results show that the proposed model and algorithm can deal with Poisson image restoration problem well. What's more, the restoration model performance is significantly improved both in visual effect and objective evaluation indexes.
AN OVERVIEW OF REDUCED ORDER MODELING TECHNIQUES FOR SAFETY APPLICATIONS
Energy Technology Data Exchange (ETDEWEB)
Mandelli, D.; Alfonsi, A.; Talbot, P.; Wang, C.; Maljovec, D.; Smith, C.; Rabiti, C.; Cogliati, J.
2016-10-01
The RISMC project is developing new advanced simulation-based tools to perform Computational Risk Analysis (CRA) for the existing fleet of U.S. nuclear power plants (NPPs). These tools numerically model not only the thermal-hydraulic behavior of the reactors primary and secondary systems, but also external event temporal evolution and component/system ageing. Thus, this is not only a multi-physics problem being addressed, but also a multi-scale problem (both spatial, µm-mm-m, and temporal, seconds-hours-years). As part of the RISMC CRA approach, a large amount of computationally-expensive simulation runs may be required. An important aspect is that even though computational power is growing, the overall computational cost of a RISMC analysis using brute-force methods may be not viable for certain cases. A solution that is being evaluated to assist the computational issue is the use of reduced order modeling techniques. During the FY2015, we investigated and applied reduced order modeling techniques to decrease the RISMC analysis computational cost by decreasing the number of simulation runs; for this analysis improvement we used surrogate models instead of the actual simulation codes. This article focuses on the use of reduced order modeling techniques that can be applied to RISMC analyses in order to generate, analyze, and visualize data. In particular, we focus on surrogate models that approximate the simulation results but in a much faster time (microseconds instead of hours/days).
Research on Modeling of Hydropneumatic Suspension Based on Fractional Order
Directory of Open Access Journals (Sweden)
Junwei Zhang
2015-01-01
Full Text Available With such excellent performance as nonlinear stiffness, adjustable vehicle height, and good vibration resistance, hydropneumatic suspension (HS has been more and more applied to heavy vehicle and engineering vehicle. Traditional modeling methods are still confined to simple models without taking many factors into consideration. A hydropneumatic suspension model based on fractional order (HSM-FO is built with the advantage of fractional order (FO in viscoelastic material modeling considering the mechanics property of multiphase medium of HS. Then, the detailed calculation method is proposed based on Oustaloup filtering approximation algorithm. The HSM-FO is implemented in Matlab/Simulink, and the results of comparison among the simulation curve of fractional order, integral order, and the curve of real experiment prove the feasibility and validity of HSM-FO. The damping force property of the suspension system under different fractional orders is also studied. In the end of this paper, several conclusions concerning HSM-FO are drawn according to analysis of simulation.
Fundamental Frequency and Model Order Estimation Using Spatial Filtering
DEFF Research Database (Denmark)
Karimian-Azari, Sam; Jensen, Jesper Rindom; Christensen, Mads Græsbøll
2014-01-01
parameters. In this paper, we present an estimation procedure for harmonic-structured signals in situations with strong interference using spatial filtering, or beamforming. We jointly estimate the fundamental frequency and the constrained model order through the output of the beamformers. Besides that, we...... extend this procedure to account for inharmonicity using unconstrained model order estimation. The simulations show that beamforming improves the performance of the joint estimates of fundamental frequency and the number of harmonics in low signal to interference (SIR) levels, and an experiment...
A reduced order model for nonlinear vibroacoustic problems
Directory of Open Access Journals (Sweden)
Ouisse Morvan
2012-07-01
Full Text Available This work is related to geometrical nonlinearities applied to thin plates coupled with fluid-filled domain. Model reduction is performed to reduce the computation time. Reduced order model (ROM is issued from the uncoupled linear problem and enriched with residues to describe the nonlinear behavior and coupling effects. To show the efficiency of the proposed method, numerical simulations in the case of an elastic plate closing an acoustic cavity are presented.
Numerical Analysis of Fractional Order Epidemic Model of Childhood Diseases
Directory of Open Access Journals (Sweden)
Fazal Haq
2017-01-01
Full Text Available The fractional order Susceptible-Infected-Recovered (SIR epidemic model of childhood disease is considered. Laplace–Adomian Decomposition Method is used to compute an approximate solution of the system of nonlinear fractional differential equations. We obtain the solutions of fractional differential equations in the form of infinite series. The series solution of the proposed model converges rapidly to its exact value. The obtained results are compared with the classical case.
Morphologically accurate reduced order modeling of spiking neurons.
Kellems, Anthony R; Chaturantabut, Saifon; Sorensen, Danny C; Cox, Steven J
2010-06-01
Accurately simulating neurons with realistic morphological structure and synaptic inputs requires the solution of large systems of nonlinear ordinary differential equations. We apply model reduction techniques to recover the complete nonlinear voltage dynamics of a neuron using a system of much lower dimension. Using a proper orthogonal decomposition, we build a reduced-order system from salient snapshots of the full system output, thus reducing the number of state variables. A discrete empirical interpolation method is then used to reduce the complexity of the nonlinear term to be proportional to the number of reduced variables. Together these two techniques allow for up to two orders of magnitude dimension reduction without sacrificing the spatially-distributed input structure, with an associated order of magnitude speed-up in simulation time. We demonstrate that both nonlinear spiking behavior and subthreshold response of realistic cells are accurately captured by these low-dimensional models.
Reduced-order models of the coagulation cascade
Hansen, Kirk B.; Shadden, Shawn C.
2015-11-01
Previous models of flow-mediated thrombogenesis have generally included the transport and reaction of dozens of biochemical species involved in the coagulation cascade. Researchers have shown, however, that thrombin generation curves can be accurately reproduced by a significantly smaller system of reactions. These reduced-order models are based on the system of ordinary differential equations representative of a well-mixed system, however, not the system of advection-diffusion-reaction equations required to model the flow-mediated case. Additionally, they focus solely on reproducing the thrombin generation curve, although accurate representation of certain intermediate species may be required to model additional aspects of clot formation, e.g. interactions with activated and non-activated platelets. In this work, we develop a method to reduce the order of a coagulation model through optimization techniques. The results of this reduced-order model are then compared to those of the full system in several representative cardiovascular flows. This work was supported by NSF grant 1354541, the NSF GRFP, and NIH grant HL108272.
Atmospheric Turbulence Modeling for Aero Vehicles: Fractional Order Fits
Kopasakis, George
2015-01-01
Atmospheric turbulence models are necessary for the design of both inlet/engine and flight controls, as well as for studying coupling between the propulsion and the vehicle structural dynamics for supersonic vehicles. Models based on the Kolmogorov spectrum have been previously utilized to model atmospheric turbulence. In this paper, a more accurate model is developed in its representative fractional order form, typical of atmospheric disturbances. This is accomplished by first scaling the Kolmogorov spectral to convert them into finite energy von Karman forms and then by deriving an explicit fractional circuit-filter type analog for this model. This circuit model is utilized to develop a generalized formulation in frequency domain to approximate the fractional order with the products of first order transfer functions, which enables accurate time domain simulations. The objective of this work is as follows. Given the parameters describing the conditions of atmospheric disturbances, and utilizing the derived formulations, directly compute the transfer function poles and zeros describing these disturbances for acoustic velocity, temperature, pressure, and density. Time domain simulations of representative atmospheric turbulence can then be developed by utilizing these computed transfer functions together with the disturbance frequencies of interest.
Time Ordering in Frontal Lobe Patients: A Stochastic Model Approach
Magherini, Anna; Saetti, Maria Cristina; Berta, Emilia; Botti, Claudio; Faglioni, Pietro
2005-01-01
Frontal lobe patients reproduced a sequence of capital letters or abstract shapes. Immediate and delayed reproduction trials allowed the analysis of short- and long-term memory for time order by means of suitable Markov chain stochastic models. Patients were as proficient as healthy subjects on the immediate reproduction trial, thus showing spared…
Higher-Order Item Response Models for Hierarchical Latent Traits
Huang, Hung-Yu; Wang, Wen-Chung; Chen, Po-Hsi; Su, Chi-Ming
2013-01-01
Many latent traits in the human sciences have a hierarchical structure. This study aimed to develop a new class of higher order item response theory models for hierarchical latent traits that are flexible in accommodating both dichotomous and polytomous items, to estimate both item and person parameters jointly, to allow users to specify…
An Almost Integration-free Approach to Ordered Response Models
van Praag, B.M.S.; Ferrer-i-Carbonell, A.
2006-01-01
'In this paper we propose an alternative approach to the estimation of ordered response models. We show that the Probit-method may be replaced by a simple OLS-approach, called P(robit)OLS, without any loss of efficiency. This method can be generalized to the analysis of panel data. For large-scale
Bilinear reduced order approximate model of parabolic distributed solar collectors
Elmetennani, Shahrazed
2015-07-01
This paper proposes a novel, low dimensional and accurate approximate model for the distributed parabolic solar collector, by means of a modified gaussian interpolation along the spatial domain. The proposed reduced model, taking the form of a low dimensional bilinear state representation, enables the reproduction of the heat transfer dynamics along the collector tube for system analysis. Moreover, presented as a reduced order bilinear state space model, the well established control theory for this class of systems can be applied. The approximation efficiency has been proven by several simulation tests, which have been performed considering parameters of the Acurex field with real external working conditions. Model accuracy has been evaluated by comparison to the analytical solution of the hyperbolic distributed model and its semi discretized approximation highlighting the benefits of using the proposed numerical scheme. Furthermore, model sensitivity to the different parameters of the gaussian interpolation has been studied.
Reduced order modeling of fluid/structure interaction.
Energy Technology Data Exchange (ETDEWEB)
Barone, Matthew Franklin; Kalashnikova, Irina; Segalman, Daniel Joseph; Brake, Matthew Robert
2009-11-01
This report describes work performed from October 2007 through September 2009 under the Sandia Laboratory Directed Research and Development project titled 'Reduced Order Modeling of Fluid/Structure Interaction.' This project addresses fundamental aspects of techniques for construction of predictive Reduced Order Models (ROMs). A ROM is defined as a model, derived from a sequence of high-fidelity simulations, that preserves the essential physics and predictive capability of the original simulations but at a much lower computational cost. Techniques are developed for construction of provably stable linear Galerkin projection ROMs for compressible fluid flow, including a method for enforcing boundary conditions that preserves numerical stability. A convergence proof and error estimates are given for this class of ROM, and the method is demonstrated on a series of model problems. A reduced order method, based on the method of quadratic components, for solving the von Karman nonlinear plate equations is developed and tested. This method is applied to the problem of nonlinear limit cycle oscillations encountered when the plate interacts with an adjacent supersonic flow. A stability-preserving method for coupling the linear fluid ROM with the structural dynamics model for the elastic plate is constructed and tested. Methods for constructing efficient ROMs for nonlinear fluid equations are developed and tested on a one-dimensional convection-diffusion-reaction equation. These methods are combined with a symmetrization approach to construct a ROM technique for application to the compressible Navier-Stokes equations.
Ordering kinetics in model systems with inhibited interfacial adsorption
DEFF Research Database (Denmark)
Willart, J.-F.; Mouritsen, Ole G.; Naudts, J.
1992-01-01
neighboring domains. This condition can be either hard, as modeled by a singularity in the domain-boundary potential, or soft, as modeled by a version of the Blume-Capel model. The results show that the effect of the steric hindrance, be it hard or soft, is only manifested in the amplitude, A......, of the algebraic growth law, R(t)∼Atn, whereas the growth exponent, n, remains close to the value n=1/2 predicted by the classical Lifshitz-Allen-Cahn growth law for systems with nonconserved order parameter. At very low temperatures there is, however, an effective crossover to a much slower algebraic growth...
Accelerating transient simulation of linear reduced order models.
Energy Technology Data Exchange (ETDEWEB)
Thornquist, Heidi K.; Mei, Ting; Keiter, Eric Richard; Bond, Brad
2011-10-01
Model order reduction (MOR) techniques have been used to facilitate the analysis of dynamical systems for many years. Although existing model reduction techniques are capable of providing huge speedups in the frequency domain analysis (i.e. AC response) of linear systems, such speedups are often not obtained when performing transient analysis on the systems, particularly when coupled with other circuit components. Reduced system size, which is the ostensible goal of MOR methods, is often insufficient to improve transient simulation speed on realistic circuit problems. It can be shown that making the correct reduced order model (ROM) implementation choices is crucial to the practical application of MOR methods. In this report we investigate methods for accelerating the simulation of circuits containing ROM blocks using the circuit simulator Xyce.
Modeling and analysis of fractional order DC-DC converter.
Radwan, Ahmed G; Emira, Ahmed A; AbdelAty, Amr M; Azar, Ahmed Taher
2017-07-11
Due to the non-idealities of commercial inductors, the demand for a better model that accurately describe their dynamic response is elevated. So, the fractional order models of Buck, Boost and Buck-Boost DC-DC converters are presented in this paper. The detailed analysis is made for the two most common modes of converter operation: Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM). Closed form time domain expressions are derived for inductor currents, voltage gain, average current, conduction time and power efficiency where the effect of the fractional order inductor is found to be strongly present. For example, the peak inductor current at steady state increases with decreasing the inductor order. Advanced Design Systems (ADS) circuit simulations are used to verify the derived formulas, where the fractional order inductor is simulated using Valsa Constant Phase Element (CPE) approximation and Generalized Impedance Converter (GIC). Different simulation results are introduced with good matching to the theoretical formulas for the three DC-DC converter topologies under different fractional orders. A comprehensive comparison with the recently published literature is presented to show the advantages and disadvantages of each approach. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Non-linear reduced order models for steady aerodynamics
DEFF Research Database (Denmark)
Zimmermann, Ralf; Goertz, Stefan
2010-01-01
transformation for obtaining problem-adapted global basis modes is introduced. Model order reduction is achieved by parameter space sampling, reduced solution space representation via global POD and restriction of a CFD flow solver to the reduced POD subspace. Solving the governing equations of fluid dynamics...... is replaced by solving a non-linear least-squares optimization problem. Methods for obtaining feasible starting solutions for the optimization procedure are discussed. The method is demonstrated by computing reduced-order solutions to the compressible Euler equations for the NACA 0012 airfoil based on two...
Reduced order modeling in topology optimization of vibroacoustic problems
DEFF Research Database (Denmark)
Creixell Mediante, Ester; Jensen, Jakob Søndergaard; Brunskog, Jonas
2017-01-01
There is an interest in introducing topology optimization techniques in the design process of structural-acoustic systems. In topology optimization, the design space must be finely meshed in order to obtain an accurate design, which results in large numbers of degrees of freedom when designing...... complex 3D parts. The optimization process can therefore become highly time consuming due to the need to solve a large system of equations at each iteration. Projection-based parametric Model Order Reduction (pMOR) methods have successfully been applied for reducing the computational cost of material...
Anisotropic Third-Order Regularization for Sparse Digital Elevation Models
Lellmann, Jan
2013-01-01
We consider the problem of interpolating a surface based on sparse data such as individual points or level lines. We derive interpolators satisfying a list of desirable properties with an emphasis on preserving the geometry and characteristic features of the contours while ensuring smoothness across level lines. We propose an anisotropic third-order model and an efficient method to adaptively estimate both the surface and the anisotropy. Our experiments show that the approach outperforms AMLE and higher-order total variation methods qualitatively and quantitatively on real-world digital elevation data. © 2013 Springer-Verlag.
Empirical Reduced-Order Modeling for Boundary Feedback Flow Control
Directory of Open Access Journals (Sweden)
Seddik M. Djouadi
2008-01-01
Full Text Available This paper deals with the practical and theoretical implications of model reduction for aerodynamic flow-based control problems. Various aspects of model reduction are discussed that apply to partial differential equation- (PDE- based models in general. Specifically, the proper orthogonal decomposition (POD of a high dimension system as well as frequency domain identification methods are discussed for initial model construction. Projections on the POD basis give a nonlinear Galerkin model. Then, a model reduction method based on empirical balanced truncation is developed and applied to the Galerkin model. The rationale for doing so is that linear subspace approximations to exact submanifolds associated with nonlinear controllability and observability require only standard matrix manipulations utilizing simulation/experimental data. The proposed method uses a chirp signal as input to produce the output in the eigensystem realization algorithm (ERA. This method estimates the system's Markov parameters that accurately reproduce the output. Balanced truncation is used to show that model reduction is still effective on ERA produced approximated systems. The method is applied to a prototype convective flow on obstacle geometry. An H∞ feedback flow controller is designed based on the reduced model to achieve tracking and then applied to the full-order model with excellent performance.
Advanced Fluid Reduced Order Models for Compressible Flow.
Energy Technology Data Exchange (ETDEWEB)
Tezaur, Irina Kalashnikova [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Fike, Jeffrey A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Carlberg, Kevin Thomas [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Barone, Matthew F. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Maddix, Danielle [Stanford Univ., CA (United States); Mussoni, Erin E. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Balajewicz, Maciej [Univ. of Illinois, Urbana-Champaign, IL (United States)
2017-09-01
This report summarizes fiscal year (FY) 2017 progress towards developing and implementing within the SPARC in-house finite volume flow solver advanced fluid reduced order models (ROMs) for compressible captive-carriage flow problems of interest to Sandia National Laboratories for the design and qualification of nuclear weapons components. The proposed projection-based model order reduction (MOR) approach, known as the Proper Orthogonal Decomposition (POD)/Least- Squares Petrov-Galerkin (LSPG) method, can substantially reduce the CPU-time requirement for these simulations, thereby enabling advanced analyses such as uncertainty quantification and de- sign optimization. Following a description of the project objectives and FY17 targets, we overview briefly the POD/LSPG approach to model reduction implemented within SPARC . We then study the viability of these ROMs for long-time predictive simulations in the context of a two-dimensional viscous laminar cavity problem, and describe some FY17 enhancements to the proposed model reduction methodology that led to ROMs with improved predictive capabilities. Also described in this report are some FY17 efforts pursued in parallel to the primary objective of determining whether the ROMs in SPARC are viable for the targeted application. These include the implemen- tation and verification of some higher-order finite volume discretization methods within SPARC (towards using the code to study the viability of ROMs on three-dimensional cavity problems) and a novel structure-preserving constrained POD/LSPG formulation that can improve the accuracy of projection-based reduced order models. We conclude the report by summarizing the key takeaways from our FY17 findings, and providing some perspectives for future work.
Model Order Reduction Algorithm for Estimating the Absorption Spectrum
Energy Technology Data Exchange (ETDEWEB)
Van Beeumen, Roel [Computational; Williams-Young, David B. [Department; Kasper, Joseph M. [Department; Yang, Chao [Computational; Ng, Esmond G. [Computational; Li, Xiaosong [Department
2017-09-20
The ab initio description of the spectral interior of the absorption spectrum poses both a theoretical and computational challenge for modern electronic structure theory. Due to the often spectrally dense character of this domain in the quantum propagator’s eigenspectrum for medium-to-large sized systems, traditional approaches based on the partial diagonalization of the propagator often encounter oscillatory and stagnating convergence. Electronic structure methods which solve the molecular response problem through the solution of spectrally shifted linear systems, such as the complex polarization propagator, offer an alternative approach which is agnostic to the underlying spectral density or domain location. This generality comes at a seemingly high computational cost associated with solving a large linear system for each spectral shift in some discretization of the spectral domain of interest. In this work, we present a novel, adaptive solution to this high computational overhead based on model order reduction techniques via interpolation. Model order reduction reduces the computational complexity of mathematical models and is ubiquitous in the simulation of dynamical systems and control theory. The efficiency and effectiveness of the proposed algorithm in the ab initio prediction of X-ray absorption spectra is demonstrated using a test set of challenging water clusters which are spectrally dense in the neighborhood of the oxygen K-edge. On the basis of a single, user defined tolerance we automatically determine the order of the reduced models and approximate the absorption spectrum up to the given tolerance. We also illustrate that, for the systems studied, the automatically determined model order increases logarithmically with the problem dimension, compared to a linear increase of the number of eigenvalues within the energy window. Furthermore, we observed that the computational cost of the proposed algorithm only scales quadratically with respect to the
MODELING THE SELF-ASSEMBLY OF ORDERED NANOPOROUS MATERIALS
Energy Technology Data Exchange (ETDEWEB)
Monson, Peter [University of Massachusetts; Auerbach, Scott [University of Massachusetts
2017-11-13
This report describes progress on a collaborative project on the multiscale modeling of the assembly processes in the synthesis of nanoporous materials. Such materials are of enormous importance in modern technology with application in the chemical process industries, biomedicine and biotechnology as well as microelectronics. The project focuses on two important classes of materials: i) microporous crystalline materials, such as zeolites, and ii) ordered mesoporous materials. In the first case the pores are part of the crystalline structure, while in the second the structures are amorphous on the atomistic length scale but where surfactant templating gives rise to order on the length scale of 2 - 20 nm. We have developed a modeling framework that encompasses both these kinds of materials. Our models focus on the assembly of corner sharing silica tetrahedra in the presence of structure directing agents. We emphasize a balance between sufficient realism in the models and computational tractibility given the complex many-body phenomena. We use both on-lattice and off-lattice models and the primary computational tools are Monte Carlo simulations with sampling techniques and ensembles appropriate to specific situations. Our modeling approach is the first to capture silica polymerization, nanopore crystallization, and mesopore formation through computer-simulated self assembly.
Directory of Open Access Journals (Sweden)
Nwankwere Emeka Thompson
2015-02-01
Full Text Available Abstract This study explores the feasibility of using local nanoclays as starting materials for sorbents with potential to treat crude oil polluted aquatic environment. The nanoclays have been converted into environmentally friendly and hydrophobic sorbents by a hydrothermal method under mild conditions using Hexadecyltrimetylammonium bromide HDTMAB as intercalant. Batch sorption studies were studied for oil concentration 0.5-5.0 g100ml and contact time 1-30 mins. An attempt to describe the crude oil sorptive behaviour of the organoclays b applying popular adsorption models were discussed and the experimental methods adopted for the determination and estimation of the sorption coefficients have also been described. The Langmuir the Freundlich and the Dubinin-Radushkevich adsorption models were applied to experimental equilibrium data. Also the kinetic properties of the sorption procedure were evaluated using the pseudo-second-order Elovich and the intraparticle diffusion of Weber and Morris kinetics models. It was discovered that the sorption process best fitted the Langmuir and the Pseudo-second-order rate models. It was concluded that the organoclays have a good affinity for the crude oil the sorption process was mostly by monolayer coverage the manner of sorption by chemisorption and that diffusion was not only the rate-controlling step.
Model predictive control based on reduced order models applied to belt conveyor system.
Chen, Wei; Li, Xin
2016-11-01
In the paper, a model predictive controller based on reduced order model is proposed to control belt conveyor system, which is an electro-mechanics complex system with long visco-elastic body. Firstly, in order to design low-degree controller, the balanced truncation method is used for belt conveyor model reduction. Secondly, MPC algorithm based on reduced order model for belt conveyor system is presented. Because of the error bound between the full-order model and reduced order model, two Kalman state estimators are applied in the control scheme to achieve better system performance. Finally, the simulation experiments are shown that balanced truncation method can significantly reduce the model order with high-accuracy and model predictive control based on reduced-model performs well in controlling the belt conveyor system. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
HOKF: High Order Kalman Filter for Epilepsy Forecasting Modeling.
Nguyen, Ngoc Anh Thi; Yang, Hyung-Jeong; Kim, Sunhee
2017-08-01
Epilepsy forecasting has been extensively studied using high-order time series obtained from scalp-recorded electroencephalography (EEG). An accurate seizure prediction system would not only help significantly improve patients' quality of life, but would also facilitate new therapeutic strategies to manage epilepsy. This paper thus proposes an improved Kalman Filter (KF) algorithm to mine seizure forecasts from neural activity by modeling three properties in the high-order EEG time series: noise, temporal smoothness, and tensor structure. The proposed High-Order Kalman Filter (HOKF) is an extension of the standard Kalman filter, for which higher-order modeling is limited. The efficient dynamic of HOKF system preserves the tensor structure of the observations and latent states. As such, the proposed method offers two main advantages: (i) effectiveness with HOKF results in hidden variables that capture major evolving trends suitable to predict neural activity, even in the presence of missing values; and (ii) scalability in that the wall clock time of the HOKF is linear with respect to the number of time-slices of the sequence. The HOKF algorithm is examined in terms of its effectiveness and scalability by conducting forecasting and scalability experiments with a real epilepsy EEG dataset. The results of the simulation demonstrate the superiority of the proposed method over the original Kalman Filter and other existing methods. Copyright © 2017 Elsevier B.V. All rights reserved.
Modelling Tobacco Consumption with a Zero-Inflated Ordered Probit Model
Mark N. Harris; Xueyan Zhao (赵学燕)
2004-01-01
Data for discrete ordered random variables are often characterised by "excessive" zero observations. Traditional ordered probit models have limited capacity in explaining the preponderance of zero observations, especially when the zeros may relate to two distinct situations of non-participation and infrequent participation (or consumption), for example. We propose a zero-inflated ordered probit (ZIOP) model using a double-hurdle combination of a split (probit) model and an ordered probit mode...
Modeling the assembly order of multimeric heteroprotein complexes.
Directory of Open Access Journals (Sweden)
Lenna X Peterson
2018-01-01
Full Text Available Protein-protein interactions are the cornerstone of numerous biological processes. Although an increasing number of protein complex structures have been determined using experimental methods, relatively fewer studies have been performed to determine the assembly order of complexes. In addition to the insights into the molecular mechanisms of biological function provided by the structure of a complex, knowing the assembly order is important for understanding the process of complex formation. Assembly order is also practically useful for constructing subcomplexes as a step toward solving the entire complex experimentally, designing artificial protein complexes, and developing drugs that interrupt a critical step in the complex assembly. There are several experimental methods for determining the assembly order of complexes; however, these techniques are resource-intensive. Here, we present a computational method that predicts the assembly order of protein complexes by building the complex structure. The method, named Path-LzerD, uses a multimeric protein docking algorithm that assembles a protein complex structure from individual subunit structures and predicts assembly order by observing the simulated assembly process of the complex. Benchmarked on a dataset of complexes with experimental evidence of assembly order, Path-LZerD was successful in predicting the assembly pathway for the majority of the cases. Moreover, when compared with a simple approach that infers the assembly path from the buried surface area of subunits in the native complex, Path-LZerD has the strong advantage that it can be used for cases where the complex structure is not known. The path prediction accuracy decreased when starting from unbound monomers, particularly for larger complexes of five or more subunits, for which only a part of the assembly path was correctly identified. As the first method of its kind, Path-LZerD opens a new area of computational protein structure
Modeling the assembly order of multimeric heteroprotein complexes.
Peterson, Lenna X; Togawa, Yoichiro; Esquivel-Rodriguez, Juan; Terashi, Genki; Christoffer, Charles; Roy, Amitava; Shin, Woong-Hee; Kihara, Daisuke
2018-01-01
Protein-protein interactions are the cornerstone of numerous biological processes. Although an increasing number of protein complex structures have been determined using experimental methods, relatively fewer studies have been performed to determine the assembly order of complexes. In addition to the insights into the molecular mechanisms of biological function provided by the structure of a complex, knowing the assembly order is important for understanding the process of complex formation. Assembly order is also practically useful for constructing subcomplexes as a step toward solving the entire complex experimentally, designing artificial protein complexes, and developing drugs that interrupt a critical step in the complex assembly. There are several experimental methods for determining the assembly order of complexes; however, these techniques are resource-intensive. Here, we present a computational method that predicts the assembly order of protein complexes by building the complex structure. The method, named Path-LzerD, uses a multimeric protein docking algorithm that assembles a protein complex structure from individual subunit structures and predicts assembly order by observing the simulated assembly process of the complex. Benchmarked on a dataset of complexes with experimental evidence of assembly order, Path-LZerD was successful in predicting the assembly pathway for the majority of the cases. Moreover, when compared with a simple approach that infers the assembly path from the buried surface area of subunits in the native complex, Path-LZerD has the strong advantage that it can be used for cases where the complex structure is not known. The path prediction accuracy decreased when starting from unbound monomers, particularly for larger complexes of five or more subunits, for which only a part of the assembly path was correctly identified. As the first method of its kind, Path-LZerD opens a new area of computational protein structure modeling and will be
A low-order model of biological neural networks.
Lo, James Ting-Ho
2011-10-01
A biologically plausible low-order model (LOM) of biological neural networks is proposed. LOM is a recurrent hierarchical network of models of dendritic nodes and trees; spiking and nonspiking neurons; unsupervised, supervised covariance and accumulative learning mechanisms; feedback connections; and a scheme for maximal generalization. These component models are motivated and necessitated by making LOM learn and retrieve easily without differentiation, optimization, or iteration, and cluster, detect, and recognize multiple and hierarchical corrupted, distorted, and occluded temporal and spatial patterns. Four models of dendritic nodes are given that are all described as a hyperbolic polynomial that acts like an exclusive-OR logic gate when the model dendritic nodes input two binary digits. A model dendritic encoder that is a network of model dendritic nodes encodes its inputs such that the resultant codes have an orthogonality property. Such codes are stored in synapses by unsupervised covariance learning, supervised covariance learning, or unsupervised accumulative learning, depending on the type of postsynaptic neuron. A masking matrix for a dendritic tree, whose upper part comprises model dendritic encoders, enables maximal generalization on corrupted, distorted, and occluded data. It is a mathematical organization and idealization of dendritic trees with overlapped and nested input vectors. A model nonspiking neuron transmits inhibitory graded signals to modulate its neighboring model spiking neurons. Model spiking neurons evaluate the subjective probability distribution (SPD) of the labels of the inputs to model dendritic encoders and generate spike trains with such SPDs as firing rates. Feedback connections from the same or higher layers with different numbers of unit-delay devices reflect different signal traveling times, enabling LOM to fully utilize temporally and spatially associated information. Biological plausibility of the component models is
A natural sorbent, Luffa cylindrica for the removal of a model basic dye
Energy Technology Data Exchange (ETDEWEB)
Altinisik, Aylin; Guer, Emel [Dokuz Eyluel University, Faculty of Arts and Sciences, Department of Chemistry, Tinaztepe Campus, Buca Izmir (Turkey); Seki, Yoldas, E-mail: yoldas.seki@deu.edu.tr [Dokuz Eyluel University, Faculty of Arts and Sciences, Department of Chemistry, Tinaztepe Campus, Buca Izmir (Turkey)
2010-07-15
In this work, application of Luffa cylindrica in malachite green (MG) removal from aqueous solution was studied in a batch system. The effect of contact time, pH and temperature on removal of malachite green was also investigated. By the time pH was increased from 3 to 5, the amount of sorbed malachite green also increased. Beyond the pH value of 5, the amount of sorbed malachite green remains constant. The fits of equilibrium sorption data to Langmuir, Freundlich and Dubinin-Radushkevich equations were investigated. Langmuir isotherm exhibited best fit with the experimental data. Monolayer sorption capacity increased with the increasing of temperature. Sorption kinetic was evaluated by pseudo-first-order, pseudo-second-order, Elovich rate equations and intraparticle diffusion models. It was inferred that sorption follows pseudo-second-order kinetic model. Thermodynamic parameters for sorption process were also found out. Spontaneous and endothermic nature of sorption was obtained due to negative value of free energy ({Delta}G{sup o}) and positive value of enthalpy ({Delta}H{sup o}) changes. FTIR analyses were also conducted to confirm the sorption of malachite green onto L. cylindrica.
A natural sorbent, Luffa cylindrica for the removal of a model basic dye.
Altinişik, Aylin; Gür, Emel; Seki, Yoldaş
2010-07-15
In this work, application of Luffa cylindrica in malachite green (MG) removal from aqueous solution was studied in a batch system. The effect of contact time, pH and temperature on removal of malachite green was also investigated. By the time pH was increased from 3 to 5, the amount of sorbed malachite green also increased. Beyond the pH value of 5, the amount of sorbed malachite green remains constant. The fits of equilibrium sorption data to Langmuir, Freundlich and Dubinin-Radushkevich equations were investigated. Langmuir isotherm exhibited best fit with the experimental data. Monolayer sorption capacity increased with the increasing of temperature. Sorption kinetic was evaluated by pseudo-first-order, pseudo-second-order, Elovich rate equations and intraparticle diffusion models. It was inferred that sorption follows pseudo-second-order kinetic model. Thermodynamic parameters for sorption process were also found out. Spontaneous and endothermic nature of sorption was obtained due to negative value of free energy (DeltaG(o)) and positive value of enthalpy (DeltaH(o)) changes. FTIR analyses were also conducted to confirm the sorption of malachite green onto L. cylindrica. 2010 Elsevier B.V. All rights reserved.
Bivariate modelling of clustered continuous and ordered categorical outcomes.
Catalano, P J
1997-04-30
Simultaneous observation of continuous and ordered categorical outcomes for each subject is common in biomedical research but multivariate analysis of the data is complicated by the multiple data types. Here we construct a model for the joint distribution of bivariate continuous and ordinal outcomes by applying the concept of latent variables to a multivariate normal distribution. The approach is then extended to allow for clustering of the bivariate outcomes. The model can be parameterized in a way that allows writing the joint distribution as a product of a standard random effects model for the continuous variable and a correlated cumulative probit model for the ordinal outcome. This factorization suggests convenient parameter estimation using estimating equations. Foetal weight and malformation data from a developmental toxicity experiment illustrate the results.
ICA model order selection of task co-activation networks.
Ray, Kimberly L; McKay, D Reese; Fox, Peter M; Riedel, Michael C; Uecker, Angela M; Beckmann, Christian F; Smith, Stephen M; Fox, Peter T; Laird, Angela R
2013-01-01
Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders.
Kinetic modeling of liquid-phase adsorption of Congo red dye using guava leaf-based activated carbon
Ojedokun, Adedamola Titi; Bello, Olugbenga Solomon
2017-07-01
Guava leaf, a waste material, was treated and activated to prepare adsorbent. The adsorbent was characterized using Scanning Electron Microscopy (SEM), Fourier Transform Infra Red (FTIR) and Energy-Dispersive X-ray (EDX) techniques. The carbonaceous adsorbent prepared from guava leaf had appreciable carbon content (86.84 %). The adsorption of Congo red dye onto guava leaf-based activated carbon (GLAC) was studied in this research. Experimental data were analyzed by four different model equations: Langmuir, Freundlich, Temkin and Dubinin-Radushkevich isotherms and it was found to fit Freundlich equation most. Adsorption rate constants were determined using pseudo-first-order, pseudo-second-order, Elovich and intraparticle diffusion model equations. The results clearly showed that the adsorption of CR dye onto GLAC followed pseudo-second-order kinetic model. Intraparticle diffusion was involved in the adsorption process. The mean energy of adsorption calculated from D-R isotherm confirmed the involvement of physical adsorption. Thermodynamic parameters were obtained and it was found that the adsorption of CR dye onto GLAC was an exothermic and spontaneous process at the temperatures under investigation. The maximum adsorption of CR dye by GLAC was found to be 47.62 mg/g. The study shows that GLAC is an effective adsorbent for the adsorption of CR dye from aqueous solution.
Treatment of tannery effluent by passive uptake-parametric studies and kinetic modeling.
Natarajan, Rajamohan; Manivasagan, Rajasimman
2017-06-08
Galactomyces geotrichum was utilized as a potential biosorbent for the treatment of tannery effluent under controlled environmental conditions. Tannery effluent treatment was studied through parametric experiments to study the effect of effluent pH (3.0-10.0), initial COD (1100-4400 mg/L), and biosorbent dosage (0.3-3.0 g/L).The zeta potential of the biosorbent was determined and found to influence the optimal pH. Increase in effluent COD values resulted in decreased COD removal percentages which attributed to limited availability of surface active sites. The equation relating the COD removal efficiency and biosorbent dose was proposed. Two popular kinetic models, namely pseudo-second order and power function models, were employed to the experimental data. Pseudo-second order model proved to be a good fit with high values of regression coefficient (R (2) > 0.960). Potential application of a fungal biosorption process was explored and the optimal process parameters were identified.
An Ordered Regression Model to Predict Transit Passengers’ Behavioural Intentions
Energy Technology Data Exchange (ETDEWEB)
Oña, J. de; Oña, R. de; Eboli, L.; Forciniti, C.; Mazzulla, G.
2016-07-01
Passengers’ behavioural intentions after experiencing transit services can be viewed as signals that show if a customer continues to utilise a company’s service. Users’ behavioural intentions can depend on a series of aspects that are difficult to measure directly. More recently, transit passengers’ behavioural intentions have been just considered together with the concepts of service quality and customer satisfaction. Due to the characteristics of the ways for evaluating passengers’ behavioural intentions, service quality and customer satisfaction, we retain that this kind of issue could be analysed also by applying ordered regression models. This work aims to propose just an ordered probit model for analysing service quality factors that can influence passengers’ behavioural intentions towards the use of transit services. The case study is the LRT of Seville (Spain), where a survey was conducted in order to collect the opinions of the passengers about the existing transit service, and to have a measure of the aspects that can influence the intentions of the users to continue using the transit service in the future. (Author)
Optimized order estimation for autoregressive models to predict respiratory motion.
Dürichen, Robert; Wissel, Tobias; Schweikard, Achim
2013-11-01
To successfully ablate moving tumors in robotic radio-surgery, it is necessary to compensate for motion of inner organs caused by respiration. This can be achieved by tracking the body surface and correlating the external movement with the tumor position as it is implemented in the CyberKnife[Formula: see text] Synchrony system. Tracking errors, originating from system immanent time delays, are typically reduced by time series prediction. Many prediction algorithms exploit autoregressive (AR) properties of the signal. Estimating the optimal model order [Formula: see text] for these algorithms constitutes a challenge often solved via grid search or prior knowledge about the signal. Aiming at a more efficient approach instead, this study evaluates the Akaike information criterion (AIC), the corrected AIC, and the Bayesian information criterion (BIC) on the first minute of the respiratory signal. Exemplarily, we evaluated the approach for a least mean square (LMS) and a wavelet-based LMS (wLMS) predictor. Analyzing 12 motion traces, orders estimated by AIC had the highest prediction accuracy for both prediction algorithms. Extending the investigations to 304 real motion traces, the prediction error of wLMS using AIC was found to decrease significantly by 85.1 % of the data compared to the original implementation The overall results suggest that using AIC to estimate the model order [Formula: see text] for prediction algorithms based on AR properties is a valid method which avoids intensive grid search and leads to high prediction accuracy.
Reduced order methods for modeling and computational reduction
Rozza, Gianluigi
2014-01-01
This monograph addresses the state of the art of reduced order methods for modeling and computational reduction of complex parametrized systems, governed by ordinary and/or partial differential equations, with a special emphasis on real time computing techniques and applications in computational mechanics, bioengineering and computer graphics. Several topics are covered, including: design, optimization, and control theory in real-time with applications in engineering; data assimilation, geometry registration, and parameter estimation with special attention to real-time computing in biomedical engineering and computational physics; real-time visualization of physics-based simulations in computer science; the treatment of high-dimensional problems in state space, physical space, or parameter space; the interactions between different model reduction and dimensionality reduction approaches; the development of general error estimation frameworks which take into account both model and discretization effects. This...
Accelerated gravitational-wave parameter estimation with reduced order modeling
Canizares, Priscilla; Gair, Jonathan; Raymond, Vivien; Smith, Rory; Tiglio, Manuel
2014-01-01
Inferring the astrophysical parameters of coalescing compact binaries is a key science goal of the upcoming advanced LIGO-Virgo gravitational-wave detector network and, more generally, gravitational-wave astronomy. However, current parameter estimation approaches for such scenarios can lead to computationally intractable problems in practice. Therefore there is a pressing need for new, fast and accurate Bayesian inference techniques. In this letter we demonstrate that a reduced order modeling approach enables rapid parameter estimation studies. By implementing a reduced order quadrature scheme within the LIGO Algorithm Library, we show that Bayesian inference on the 9-dimensional parameter space of non-spinning binary neutron star inspirals can be sped up by a factor of 30 for the early advanced detectors' configurations. This speed-up will increase to about $150$ as the detectors improve their low-frequency limit to 10Hz, reducing to hours analyses which would otherwise take months to complete. Although thes...
Reduced-Order Modeling of a Heaving Airfoil
Haj-Hariri, H.; Murphy, P. C.; Lewin, G. C.
2005-01-01
A reduced-order model of a flapping airfoil is developed using Proper Orthogonal Decomposition (POD). The proper basis functions, developed from snapshots of full Navier-Stokes simulations, are used for a Galerkin projection of the governing equations. The resulting coupled, nonlinear ordinary di.erential equations have a low dimension because the first few basis members capture most of the energy of the flow. The reduced-order model is used to simulate heaving motions that are both similar to and different from the motion(s) used to generate the basis functions, and the errors in the model are quantified. Several methods are used to generate mode sets that can be used over a range of heaving parameters, including snapshots from one, two, and multiple Navier-Stokes simulations. As snapshots from additional simulations are added to the decomposition, the mode sets become richer and can simulate a wider range of parameter space, at some computational cost. Whereas the POD method is fully applicable in three dimensions, the simulation technique based on a body-fixed and body-fitted grid suffers large overhead when extended to three dimensions. To reduce the overhead, an embedding technique is discussed which embeds the solid wing into a fixed Cartesian grid. The wing, which can now have multiple pieces and also be flexible, is represented by a distribution of body forces. This distribution is determined to give exactly the flow around a flapping wing.
Damage analysis of biocomposite laminates using model order reduction.
Yoon, Gil Ho; Kim, Heung Soo
2014-10-01
The transient quasi-static Ritz vector method (TQSRV) is applied to efficiently calculate the transient response of a delaminated biocomposite laminate. Delamination of the laminated biocomposite structure was modeled using an improved layerwise displacement field. The piezoelectric coupling effect was modeled using higher order electric potential. One piezoelectric actuator was used to excite the laminated biocomposite plate, and one piezoelectric sensor was used to detect the transient structural response of the plate. Single discrete delamination was seeded in the laminated biocomposite plate, to investigate the effect of delamination. Three different locations of delamination through the thickness direction were considered, to study the effects of delamination on structural response. The Newmark-beta algorithm and the model order reduction (MOR) method were used, to obtain transient response of the delaminated composite plate under impulse loading. The effects of delamination were clearly observed in the power spectral density of the piezoelectric sensor output. From the results, it is concluded that the MOR is a very efficient method in predicting the damage effects of delaminated biocomposite structures.
Towards a Wind Turbine Wake Reduced-Order Model
Hamilton, Nicholas; Viggiano, Bianca; Calaf, Marc; Tutkun, Murat; Cal, Raúl Bayoán
2017-11-01
A reduced-order model for a wind turbine wake is sought for prediction and control. Basis functions from the proper orthogonal decomposition (POD) represent the spatially coherent turbulence structures in the wake; eigenvalues delineate the turbulence kinetic energy associated with each mode. Back-projecting the POD modes onto the velocity snapshots produces coefficients that express the amplitude of each mode in time. A reduced-order model of the wind turbine wake (wakeROM) is defined through a series of polynomial parameters that quantify mode interaction and the evolution of each mode coefficient. Tikhonov regularization is employed to recalibrate the dynamical system, reducing error in the modeled mode coefficients and adding stability to the system. The wakeROM is periodically reinitialized by relating the incoming turbulent velocity to the POD mode coefficients. A high-level view of the wakeROM provides as a platform to discuss promising research direction, alternate processes that will enhance stability, and portability to control methods. NSF- ECCS-1032647, NSF-CBET-1034581, Research Council of Norway Project Number 231491.
Modeling the mechanism involved during the sorption of methylene blue onto fly ash.
Kumar, K Vasanth; Ramamurthi, V; Sivanesan, S
2005-04-01
Batch sorption experiments were carried out to remove methylene blue from its aqueous solutions using fly ash as an adsorbent. Operating variables studied were initial dye concentration, fly ash mass, pH, and contact time. Maximum color removal was observed at a basic pH of 8. Equilibrium data were represented well by a Langmuir isotherm equation with a monolayer sorption capacity of 5.718 mg/g. Sorption data were fitted to both Lagergren first-order and pseudo-second-order kinetic models and the data were found to follow pseudo-second-order kinetics. Rate constants at different initial concentrations were estimated. The process mechanism was found to be complex, consisting of both surface adsorption and pore diffusion. The effective diffusion parameter D(i) values were estimated at different initial concentrations and the average value was determined to be 2.063 x 10(-9)cm2/s. Analysis of sorption data using a Boyd plot confirms the particle diffusion as the rate-limiting step for the dye concentration ranges studied in the present investigation (20 to 60 mg/L).
Directory of Open Access Journals (Sweden)
M. Kumar
2017-05-01
Full Text Available In this study, the activated carbon was prepared from Prosopis juliflora bark as a novel adsorbent. Removal of chromium (Cr was assessed by varying the parameters like metal concentration, temperature, pH, adsorbent dose and contact time. The feasibility of the sorption was studied using Freundlich and Langmuir isotherms including linear and non-linear regression methods. In Langmuir, various forms of linearized equations were evaluated. The isotherm parameter of dimensionless separation factor (RL was also studied. The kinetics of adsorption was studied by using Lagergren’s pseudo-first order and pseudo-second order equations and the results have shown that the adsorption process follows pseudo-second order kinetics and the adsorption process depends on both time and concentration. The mechanistic pathway of the adsorption process was evaluated with intraparticle diffusion model. The effect of heat of adsorption of the adsorbate onto the adsorbent material was determined using the thermodynamic parameters and the reusability of the adsorbent materials was ascertained with desorption studies. The adsorbent material characterization was done by using Fourier Transform Infrared Spectroscopy (FTIR, X-ray Diffraction (XRD method and morphology of the surface of adsorbent was identified with Scanning Electron Microscope (SEM.
Tibia Fracture Healing Prediction Using First-Order Mathematical Model
M Sridevi; Prakasam, P.; Kumaravel, S.; Madhava Sarma, P.
2015-01-01
The prediction of healing period of a tibia fracture in humans across limb using first-order mathematical model is demonstrated. At present, fracture healing is diagnosed using X-rays. Recent studies have demonstrated electric stimulation as a diagnostic tool in fracture healing. A DC electric voltage of 0.7 V was applied across the fracture and stabilized with Teflon coated carbon rings and the data was recorded at different time intervals until the fracture heals. The experimental data fitt...
Temporal aggregation in first order cointegrated vector autoregressive models
DEFF Research Database (Denmark)
La Cour, Lisbeth Funding; Milhøj, Anders
We study aggregation - or sample frequencies - of time series, e.g. aggregation from weekly to monthly or quarterly time series. Aggregation usually gives shorter time series but spurious phenomena, in e.g. daily observations, can on the other hand be avoided. An important issue is the effect of ...... of aggregation on the adjustment coefficient in cointegrated systems. We study only first order vector autoregressive processes for n dimensional time series Xt, and we illustrate the theory by a two dimensional and a four dimensional model for prices of various grades of gasoline...
Phase-field-crystal model for fcc ordering
Wu, Kuo-An; Adland, Ari; Karma, Alain
2010-06-01
We develop and analyze a two-mode phase-field-crystal model to describe fcc ordering. The model is formulated by coupling two different sets of crystal density waves corresponding to ⟨111⟩ and ⟨200⟩ reciprocal lattice vectors, which are chosen to form triads so as to produce a simple free-energy landscape with coexistence of crystal and liquid phases. The feasibility of the approach is demonstrated with numerical examples of polycrystalline and (111) twin growth. We use a two-mode amplitude expansion to characterize analytically the free-energy landscape of the model, identifying parameter ranges where fcc is stable or metastable with respect to bcc. In addition, we derive analytical expressions for the elastic constants for both fcc and bcc. Those expressions show that a nonvanishing amplitude of [200] density waves is essential to obtain mechanically stable fcc crystals with a nonvanishing tetragonal shear modulus (C11-C12)/2 . We determine the model parameters for specific materials by fitting the peak liquid structure factor properties and solid-density wave amplitudes following the approach developed for bcc [K.-A. Wu and A. Karma, Phys. Rev. B 76, 184107 (2007)]. This procedure yields reasonable predictions of elastic constants for both bcc Fe and fcc Ni using input parameters from molecular dynamics simulations. The application of the model to two-dimensional square lattices is also briefly examined.
Low-order modelling of droplets on hydrophobic surfaces
Matar, Omar; Wray, Alex; Kahouadji, Lyes; Davis, Stephen
2015-11-01
We consider the behaviour of a droplet deposited onto a hydrophobic substrate. This and associated problems have garnered a wide degree of attention due to their significance in industrial and experimental settings, such as the post-rupture dewetting problem. These problems have generally defied low-order analysis due to the multi-valued nature of the interface, but we show here how to overcome this in this instance. We first discuss the static problem: when the droplet is stationary, its shape is prescribed by an ordinary differential equation (ODE) given by balancing gravitational and capillary stresses at the interface. This is dependent on the contact angle, the Bond number and the volume of the drop. In the high Bond number limit, we derive several low-order models of varying complexity to predict the shape of such drops. These are compared against numerical calculations of the ODE. We then approach the dynamic problem: in this case, the full Stokes equations throughout the drop must be considered. A low-order approach is used by solving the biharmonic equation in a coordinate system naturally mapping to the droplet shape. The results are compared against direct numerical simulations. EPSRC Programme Grant, MEMPHIS, EP/K0039761/1, EPSRC Doctoral Prize Fellowship (AWW).
Quantifying and modeling birth order effects in autism.
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Tychele Turner
Full Text Available Autism is a complex genetic disorder with multiple etiologies whose molecular genetic basis is not fully understood. Although a number of rare mutations and dosage abnormalities are specific to autism, these explain no more than 10% of all cases. The high heritability of autism and low recurrence risk suggests multifactorial inheritance from numerous loci but other factors also intervene to modulate risk. In this study, we examine the effect of birth rank on disease risk which is not expected for purely hereditary genetic models. We analyzed the data from three publicly available autism family collections in the USA for potential birth order effects and studied the statistical properties of three tests to show that adequate power to detect these effects exist. We detect statistically significant, yet varying, patterns of birth order effects across these collections. In multiplex families, we identify V-shaped effects where middle births are at high risk; in simplex families, we demonstrate linear effects where risk increases with each additional birth. Moreover, the birth order effect is gender-dependent in the simplex collection. It is currently unknown whether these patterns arise from ascertainment biases or biological factors. Nevertheless, further investigation of parental age-dependent risks yields patterns similar to those observed and could potentially explain part of the increased risk. A search for genes considering these patterns is likely to increase statistical power and uncover novel molecular etiologies.
Quantifying and modeling birth order effects in autism.
Turner, Tychele; Pihur, Vasyl; Chakravarti, Aravinda
2011-01-01
Autism is a complex genetic disorder with multiple etiologies whose molecular genetic basis is not fully understood. Although a number of rare mutations and dosage abnormalities are specific to autism, these explain no more than 10% of all cases. The high heritability of autism and low recurrence risk suggests multifactorial inheritance from numerous loci but other factors also intervene to modulate risk. In this study, we examine the effect of birth rank on disease risk which is not expected for purely hereditary genetic models. We analyzed the data from three publicly available autism family collections in the USA for potential birth order effects and studied the statistical properties of three tests to show that adequate power to detect these effects exist. We detect statistically significant, yet varying, patterns of birth order effects across these collections. In multiplex families, we identify V-shaped effects where middle births are at high risk; in simplex families, we demonstrate linear effects where risk increases with each additional birth. Moreover, the birth order effect is gender-dependent in the simplex collection. It is currently unknown whether these patterns arise from ascertainment biases or biological factors. Nevertheless, further investigation of parental age-dependent risks yields patterns similar to those observed and could potentially explain part of the increased risk. A search for genes considering these patterns is likely to increase statistical power and uncover novel molecular etiologies.
Multicritical behavior in models with two competing order parameters.
Eichhorn, Astrid; Mesterházy, David; Scherer, Michael M
2013-10-01
We employ the nonperturbative functional renormalization group to study models with an O(N(1) ⊕O(N)(2)) symmetry. Here different fixed points exist in three dimensions, corresponding to bicritical and tetracritical behavior induced by the competition of two order parameters. We discuss the critical behavior of the symmetry-enhanced isotropic, the decoupled and the biconical fixed point, and analyze their stability in the N(1),N(2) plane. We study the fate of nontrivial fixed points during the transition from three to four dimensions, finding evidence for a triviality problem for coupled two-scalar models in high-energy physics. We also point out the possibility of noncanonical critical exponents at semi-Gaussian fixed points and show the emergence of Goldstone modes from discrete symmetries.
Ordered LOGIT Model approach for the determination of financial distress.
Kinay, B
2010-01-01
Nowadays, as a result of the global competition encountered, numerous companies come up against financial distresses. To predict and take proactive approaches for those problems is quite important. Thus, the prediction of crisis and financial distress is essential in terms of revealing the financial condition of companies. In this study, financial ratios relating to 156 industrial firms that are quoted in the Istanbul Stock Exchange are used and probabilities of financial distress are predicted by means of an ordered logit regression model. By means of Altman's Z Score, the dependent variable is composed by scaling the level of risk. Thus, a model that can compose an early warning system and predict financial distress is proposed.
Using the Neumann series expansion for assembling Reduced Order Models
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Nasisi S.
2014-06-01
Full Text Available An efficient method to remove the limitation in selecting the master degrees of freedom in a finite element model by means of a model order reduction is presented. A major difficulty of the Guyan reduction and IRS method (Improved Reduced System is represented by the need of appropriately select the master and slave degrees of freedom for the rate of convergence to be high. This study approaches the above limitation by using a particular arrangement of the rows and columns of the assembled matrices K and M and employing a combination between the IRS method and a variant of the analytical selection of masters presented in (Shah, V. N., Raymund, M., Analytical selection of masters for the reduced eigenvalue problem, International Journal for Numerical Methods in Engineering 18 (1 1982 in case first lowest frequencies had to be sought. One of the most significant characteristics of the approach is the use of the Neumann series expansion that motivates this particular arrangement of the matrices’ entries. The method shows a higher rate of convergence when compared to the standard IRS and very accurate results for the lowest reduced frequencies. To show the effectiveness of the proposed method two testing structures and the human vocal tract model employed in (Vampola, T., Horacek, J., Svec, J. G., FE modeling of human vocal tract acoustics. Part I: Prodution of Czech vowels, Acta Acustica United with Acustica 94 (3 2008 are presented.
Açıkyıldız, Metin; Gürses, Ahmet; Güneş, Kübra; Yalvaç, Duygu
2015-11-01
The present study was designed to compare the linear and non-linear methods used to check the compliance of the experimental data corresponding to the isotherm models (Langmuir, Freundlich, and Redlich-Peterson) and kinetics equations (pseudo-first order and pseudo-second order). In this context, adsorption experiments were carried out to remove an anionic dye, Remazol Brillant Yellow 3GL (RBY), from its aqueous solutions using a commercial activated carbon as a sorbent. The effects of contact time, initial RBY concentration, and temperature onto adsorbed amount were investigated. The amount of dye adsorbed increased with increased adsorption time and the adsorption equilibrium was attained after 240 min. The amount of dye adsorbed enhanced with increased temperature, suggesting that the adsorption process is endothermic. The experimental data was analyzed using the Langmuir, Freundlich, and Redlich-Peterson isotherm equations in order to predict adsorption isotherm. It was determined that the isotherm data were fitted to the Langmuir and Redlich-Peterson isotherms. The adsorption process was also found to follow a pseudo second-order kinetic model. According to the kinetic and isotherm data, it was found that the determination coefficients obtained from linear method were higher than those obtained from non-linear method.
Ultracapacitor Modelling and Control Using Discrete Fractional Order State-Space Model
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Andrzej Dzielinski
2008-03-01
Full Text Available In this paper the modelling of ultracapacitor system using discrete fractional order state-space system is presented. The obtained model is used for design and testing of state feedback controller with observer.
Theory and Low-Order Modeling of Unsteady Airfoil Flows
Ramesh, Kiran
Unsteady flow phenomena are prevalent in a wide range of problems in nature and engineering. These include, but are not limited to, aerodynamics of insect flight, dynamic stall in rotorcraft and wind turbines, leading-edge vortices in delta wings, micro-air vehicle (MAV) design, gust handling and flow control. The most significant characteristics of unsteady flows are rapid changes in the circulation of the airfoil, apparent-mass effects, flow separation and the leading-edge vortex (LEV) phenomenon. Although experimental techniques and computational fluid dynamics (CFD) methods have enabled the detailed study of unsteady flows and their underlying features, a reliable and inexpensive loworder method for fast prediction and for use in control and design is still required. In this research, a low-order methodology based on physical principles rather than empirical fitting is proposed. The objective of such an approach is to enable insights into unsteady phenomena while developing approaches to model them. The basis of the low-order model developed here is unsteady thin-airfoil theory. A time-stepping approach is used to solve for the vorticity on an airfoil camberline, allowing for large amplitudes and nonplanar wakes. On comparing lift coefficients from this method against data from CFD and experiments for some unsteady test cases, it is seen that the method predicts well so long as LEV formation does not occur and flow over the airfoil is attached. The formation of leading-edge vortices (LEVs) in unsteady flows is initiated by flow separation and the formation of a shear layer at the airfoil's leading edge. This phenomenon has been observed to have both detrimental (dynamic stall in helicopters) and beneficial (high-lift flight in insects) effects. To predict the formation of LEVs in unsteady flows, a Leading Edge Suction Parameter (LESP) is proposed. This parameter is calculated from inviscid theory and is a measure of the suction at the airfoil's leading edge. It
A dynamic neural field model of temporal order judgments.
Hecht, Lauren N; Spencer, John P; Vecera, Shaun P
2015-12-01
Temporal ordering of events is biased, or influenced, by perceptual organization-figure-ground organization-and by spatial attention. For example, within a region assigned figural status or at an attended location, onset events are processed earlier (Lester, Hecht, & Vecera, 2009; Shore, Spence, & Klein, 2001), and offset events are processed for longer durations (Hecht & Vecera, 2011; Rolke, Ulrich, & Bausenhart, 2006). Here, we present an extension of a dynamic field model of change detection (Johnson, Spencer, Luck, & Schöner, 2009; Johnson, Spencer, & Schöner, 2009) that accounts for both the onset and offset performance for figural and attended regions. The model posits that neural populations processing the figure are more active, resulting in a peak of activation that quickly builds toward a detection threshold when the onset of a target is presented. This same enhanced activation for some neural populations is maintained when a present target is removed, creating delays in the perception of the target's offset. We discuss the broader implications of this model, including insights regarding how neural activation can be generated in response to the disappearance of information. (c) 2015 APA, all rights reserved).
Vortex network community based reduced-order force model
Gopalakrishnan Meena, Muralikrishnan; Nair, Aditya; Taira, Kunihiko
2017-11-01
We characterize the vortical wake interactions by utilizing network theory and cluster-based approaches, and develop a data-inspired unsteady force model. In the present work, the vortical interaction network is defined by nodes representing vortical elements and the edges quantified by induced velocity measures amongst the vortices. The full vorticity field is reduced to a finite number of vortical clusters based on network community detection algorithm, which serves as a basis for a skeleton network that captures the essence of the wake dynamics. We use this reduced representation of the wake to develop a data-inspired reduced-order force model that can predict unsteady fluid forces on the body. The overall formulation is demonstrated for laminar flows around canonical bluff body wake and stalled flow over an airfoil. We also show the robustness of the present network-based model against noisy data, which motivates applications towards turbulent flows and experimental measurements. Supported by the National Science Foundation (Grant 1632003).
Basic first-order model theory in Mizar
Directory of Open Access Journals (Sweden)
Marco Bright Caminati
2010-01-01
Full Text Available The author has submitted to Mizar Mathematical Library a series of five articles introducing a framework for the formalization of classical first-order model theory.In them, Goedel's completeness and Lowenheim-Skolem theorems have also been formalized for the countable case, to offer a first application of it and to showcase its utility.This is an overview and commentary on some key aspects of this setup.It features exposition and discussion of a new encoding of basic definitions and theoretical gears needed for the task, remarks about the design strategies and approaches adopted in their implementation, and more general reflections about proof checking induced by the work done.
Dynamical analysis of fractional order model of immunogenic tumors
Directory of Open Access Journals (Sweden)
Sadia Arshad
2016-07-01
Full Text Available In this article, we examine the fractional order model of the cytotoxic T lymphocyte response to a growing tumor cell population. We investigate the long-term behavior of tumor growth and explore the conditions of tumor elimination analytically. We establish the conditions for the tumor-free equilibrium and tumor-infection equilibrium to be asymptotically stable and provide the expression of the basic reproduction number. Existence of physical significant tumor-infection equilibrium points is investigated analytically. We show that tumor growth rate, source rate of immune cells, and death rate of immune cells play vital role in tumor dynamics and system undergoes saddle-node and transcritical bifurcation based on these parameters. Furthermore, the effect of cancer treatment is discussed by varying the values of relevant parameters. Numerical simulations are presented to illustrate the analytical results.
Kinetic modelling for zinc (II) ions biosorption onto Luffa cylindrica
Oboh, I.; Aluyor, E.; Audu, T.
2015-03-01
The biosorption of Zinc (II) ions onto a biomaterial - Luffa cylindrica has been studied. This biomaterial was characterized by elemental analysis, surface area, pore size distribution, scanning electron microscopy, and the biomaterial before and after sorption, was characterized by Fourier Transform Infra Red (FTIR) spectrometer. The kinetic nonlinear models fitted were Pseudo-first order, Pseudo-second order and Intra-particle diffusion. A comparison of non-linear regression method in selecting the kinetic model was made. Four error functions, namely coefficient of determination (R2), hybrid fractional error function (HYBRID), average relative error (ARE), and sum of the errors squared (ERRSQ), were used to predict the parameters of the kinetic models. The strength of this study is that a biomaterial with wide distribution particularly in the tropical world and which occurs as waste material could be put into effective utilization as a biosorbent to address a crucial environmental problem.
Kinetic modelling for zinc (II) ions biosorption onto Luffa cylindrica
Energy Technology Data Exchange (ETDEWEB)
Oboh, I., E-mail: innocentoboh@uniuyo.edu.ng [Department of Chemical and Petroleum Engineering, University of Uyo, Uyo (Nigeria); Aluyor, E.; Audu, T. [Department of Chemical Engineering, University of Uyo, BeninCity, BeninCity (Nigeria)
2015-03-30
The biosorption of Zinc (II) ions onto a biomaterial - Luffa cylindrica has been studied. This biomaterial was characterized by elemental analysis, surface area, pore size distribution, scanning electron microscopy, and the biomaterial before and after sorption, was characterized by Fourier Transform Infra Red (FTIR) spectrometer. The kinetic nonlinear models fitted were Pseudo-first order, Pseudo-second order and Intra-particle diffusion. A comparison of non-linear regression method in selecting the kinetic model was made. Four error functions, namely coefficient of determination (R{sup 2}), hybrid fractional error function (HYBRID), average relative error (ARE), and sum of the errors squared (ERRSQ), were used to predict the parameters of the kinetic models. The strength of this study is that a biomaterial with wide distribution particularly in the tropical world and which occurs as waste material could be put into effective utilization as a biosorbent to address a crucial environmental problem.
Sparsity enabled cluster reduced-order models for control
Kaiser, Eurika; Morzyński, Marek; Daviller, Guillaume; Kutz, J. Nathan; Brunton, Bingni W.; Brunton, Steven L.
2018-01-01
Characterizing and controlling nonlinear, multi-scale phenomena are central goals in science and engineering. Cluster-based reduced-order modeling (CROM) was introduced to exploit the underlying low-dimensional dynamics of complex systems. CROM builds a data-driven discretization of the Perron-Frobenius operator, resulting in a probabilistic model for ensembles of trajectories. A key advantage of CROM is that it embeds nonlinear dynamics in a linear framework, which enables the application of standard linear techniques to the nonlinear system. CROM is typically computed on high-dimensional data; however, access to and computations on this full-state data limit the online implementation of CROM for prediction and control. Here, we address this key challenge by identifying a small subset of critical measurements to learn an efficient CROM, referred to as sparsity-enabled CROM. In particular, we leverage compressive measurements to faithfully embed the cluster geometry and preserve the probabilistic dynamics. Further, we show how to identify fewer optimized sensor locations tailored to a specific problem that outperform random measurements. Both of these sparsity-enabled sensing strategies significantly reduce the burden of data acquisition and processing for low-latency in-time estimation and control. We illustrate this unsupervised learning approach on three different high-dimensional nonlinear dynamical systems from fluids with increasing complexity, with one application in flow control. Sparsity-enabled CROM is a critical facilitator for real-time implementation on high-dimensional systems where full-state information may be inaccessible.
Second-order closure PBL model with new third-order moments: Comparison with LES data
Canuto, V. M.; Minotti, F.; Ronchi, C.; Ypma, R. M.; Zeman, O.
1994-01-01
This paper contains two parts. In the first part, a new set of diagnostic equations is derived for the third-order moments for a buoyancy-driven flow, by exact inversion of the prognostic equations for the third-order moment equations in the stationary case. The third-order moments exhibit a universal structure: they all are a linear combination of the derivatives of all the second-order moments, bar-w(exp 2), bar-w theta, bar-theta(exp 2), and bar-q(exp 2). Each term of the sum contains a turbulent diffusivity D(sub t), which also exhibits a universal structure of the form D(sub t) = a nu(sub t) + b bar-w theta. Since the sign of the convective flux changes depending on stable or unstable stratification, D(sub t) varies according to the type of stratification. Here nu(sub t) approximately equal to wl (l is a mixing length and w is an rms velocity) represents the 'mechanical' part, while the 'buoyancy' part is represented by the convective flux bar-w theta. The quantities a and b are functions of the variable N(sub tau)(exp 2), where N(exp 2) = g alpha derivative of Theta with respect to z and tau is the turbulence time scale. The new expressions for the third-order moments generalize those of Zeman and Lumley, which were subsequently adopted by Sun and Ogura, Chen and Cotton, and Finger and Schmidt in their treatments of the convective boundary layer. In the second part, the new expressions for the third-order moments are used to solve the ensemble average equations describing a purely convective boundary laye r heated from below at a constant rate. The computed second- and third-order moments are then compared with the corresponding Large Eddy Simulation (LES) results, most of which are obtained by running a new LES code, and part of which are taken from published results. The ensemble average results compare favorably with the LES data.
Bromate removal from aqueous solutions by ordered mesoporous carbon.
Xu, Chunhua; Wang, Xiaohong; Shi, Xiaolei; Lin, Sheng; Zhu, Liujia; Che, Yaming
2014-01-01
We investigated the feasibility of using ordered mesoporous carbon (OMC) for bromate removal from water. Batch experiments were performed to study the influence of various experimental parameters such as the effect of contact time, adsorbent dosage, initial bromate concentration, temperature, pH and effect of competing anions on bromate removal by OMC. The adsorption kinetics indicates that the uptake rate ofbromate was rapid at the beginning: 85% adsorption was completed in 1 h and equilibrium was achieved within 3 h. The sorption process was well described with pseudo-second-order kinetics. The maximum adsorption capacity of OMC for bromate removal was 17.6 mg g(-1) at 298 K. The adsorption data fit the Freundlich model well. The amount of bromate removed was found to be proportional to the influent bromate concentration. The effects of competing anions and solution pH (3-11) were negligible. These limited data suggest that OMC can be effectively utilized for bromate removal from drinking water.
Kopasakis, George
2014-01-01
The presentation covers a recently developed methodology to model atmospheric turbulence as disturbances for aero vehicle gust loads and for controls development like flutter and inlet shock position. The approach models atmospheric turbulence in their natural fractional order form, which provides for more accuracy compared to traditional methods like the Dryden model, especially for high speed vehicle. The presentation provides a historical background on atmospheric turbulence modeling and the approaches utilized for air vehicles. This is followed by the motivation and the methodology utilized to develop the atmospheric turbulence fractional order modeling approach. Some examples covering the application of this method are also provided, followed by concluding remarks.
Nonparametric Polytomous IRT Models for Invariant Item Ordering, with Results for Parametric Models.
Sijtsma, Klaas; Hemker, Bas T.
1998-01-01
The absence of the invariant item ordering (IIO) property in two nonparametric polytomous item response theory (IRT) models is discussed, and two nonparametric models are discussed that imply an IIO. Only two parametric polytomous IRT models are found to imply an IIO. A method is proposed to investigate whether an IIO is implied with empirical…
Construction of energy-stable Galerkin reduced order models.
Energy Technology Data Exchange (ETDEWEB)
Kalashnikova, Irina; Barone, Matthew Franklin; Arunajatesan, Srinivasan; van Bloemen Waanders, Bart Gustaaf
2013-05-01
This report aims to unify several approaches for building stable projection-based reduced order models (ROMs). Attention is focused on linear time-invariant (LTI) systems. The model reduction procedure consists of two steps: the computation of a reduced basis, and the projection of the governing partial differential equations (PDEs) onto this reduced basis. Two kinds of reduced bases are considered: the proper orthogonal decomposition (POD) basis and the balanced truncation basis. The projection step of the model reduction can be done in two ways: via continuous projection or via discrete projection. First, an approach for building energy-stable Galerkin ROMs for linear hyperbolic or incompletely parabolic systems of PDEs using continuous projection is proposed. The idea is to apply to the set of PDEs a transformation induced by the Lyapunov function for the system, and to build the ROM in the transformed variables. The resulting ROM will be energy-stable for any choice of reduced basis. It is shown that, for many PDE systems, the desired transformation is induced by a special weighted L2 inner product, termed the %E2%80%9Csymmetry inner product%E2%80%9D. Attention is then turned to building energy-stable ROMs via discrete projection. A discrete counterpart of the continuous symmetry inner product, a weighted L2 inner product termed the %E2%80%9CLyapunov inner product%E2%80%9D, is derived. The weighting matrix that defines the Lyapunov inner product can be computed in a black-box fashion for a stable LTI system arising from the discretization of a system of PDEs in space. It is shown that a ROM constructed via discrete projection using the Lyapunov inner product will be energy-stable for any choice of reduced basis. Connections between the Lyapunov inner product and the inner product induced by the balanced truncation algorithm are made. Comparisons are also made between the symmetry inner product and the Lyapunov inner product. The performance of ROMs constructed
Higher Order Hierarchical Legendre Basis Functions for Electromagnetic Modeling
DEFF Research Database (Denmark)
Jørgensen, Erik; Volakis, John L.; Meincke, Peter
2004-01-01
This paper presents a new hierarchical basis of arbitrary order for integral equations solved with the Method of Moments (MoM). The basis is derived from orthogonal Legendre polynomials which are modified to impose continuity of vector quantities between neighboring elements while maintaining most....... In addition, all higher-order terms in the expansion have two vanishing moments.In contrast to existing formulations, these properties allow the use of very high-order basis functions without introducing ill-conditioning of the resulting MoM matrix. Numerical results confirm that the condition number...... of the MoM matrix obtained with this new basis is much lower than existing higher-order interpolatory and hierarchical basis functions. As a consequence of the excellent condition numbers, we demonstrate that even very high-order MoM systems, e.g. 10th order, can be solved efficiently with an iterative...
The discrete ordered median problem models and solution methods
Domínguez-Marín, Patricia
2003-01-01
This is the first book about the discrete ordered median problem (DOMP), which unifies many classical and new facility location problems Several exact and heuristic approaches are developed in this book in order to solve the DOMP Audience The book is suitable for researchers in location theory, and graduate students in combinatorial optimization
Frequency Weighted Model Order Reduction Technique and Error Bounds for Discrete Time Systems
Muhammad Imran; Abdul Ghafoor; Victor Sreeram
2014-01-01
Model reduction is a process of approximating higher order original models by comparatively lower order models with reasonable accuracy in order to provide ease in design, modeling and simulation for large complex systems. Generally, model reduction techniques approximate the higher order systems for whole frequency range. However, certain applications (like controller reduction) require frequency weighted approximation, which introduce the concept of using frequency weights in model reductio...
Reduced Order Aeroservoelastic Models with Rigid Body Modes Project
National Aeronautics and Space Administration — Complex aeroelastic and aeroservoelastic phenomena can be modeled on complete aircraft configurations generating models with millions of degrees of freedom. Starting...
Energy Technology Data Exchange (ETDEWEB)
Farzin Nejad, N., E-mail: Farzinnejadn@ripi.ir [Petroleum Refining Technology Development Division, Research Institute of Petroleum Industry, Tehran 14857-33111 (Iran, Islamic Republic of); Shams, E.; Amini, M.K. [Department of Chemistry, University of Isfahan, Isfahan 81746-73441 (Iran, Islamic Republic of)
2015-09-15
In this work, magnetic ordered mesoporous carbon adsorbent was synthesized using soft templating method to adsorb sulfur from model oil (dibenzothiophene in n-hexane). Through this research, pluronic F-127, resorcinol-formaldehyde and hydrated iron nitrate were respectively used as soft template, carbon source and iron source. The adsorbent was characterized by X-ray diffraction, nitrogen adsorption–desorption isotherm and transmission electron microscopy. Nitrogen adsorption–desorption measurement revealed the high surface area (810 m{sup 2} g{sup −1}), maxima pore size of 3.3 nm and large pore volume (1.01 cm{sup 3} g{sup −1}) of the synthesized sample. The adsorbent showed a maximum adsorption capacity of 111 mg dibenzothiophene g{sup −1} of adsorbent. Sorption process was described by the pseudo-second-order rate equation and could be better fitted by the Freundlich model, showing the heterogeneous feature of the adsorption process. In addition, the adsorption capacity of regenerated adsorbent was 78.6% of the initial level, after five regeneration cycles. - Highlights: • Adsorptive desulfurization of model oil with magnetic ordered mesoporous carbon adsorbent, Fe-OMC, was studied. • Maximum adsorption capacity (q{sub max}) of Fe-OMC for DBT was found to be 111.1 mg g{sup −1}. • Freundlich isotherm best represents the equilibrium adsorption data. • Rate of DBT adsorption process onto Fe-OMC is controlled by at least two steps.
Directory of Open Access Journals (Sweden)
Christer Dalen
2017-10-01
Full Text Available A model reduction technique based on optimization theory is presented, where a possible higher order system/model is approximated with an unstable DIPTD model by using only step response data. The DIPTD model is used to tune PD/PID controllers for the underlying possible higher order system. Numerous examples are used to illustrate the theory, i.e. both linear and nonlinear models. The Pareto Optimal controller is used as a reference controller.
Isotropy dependence of spiral order in triangular lattice Hubbard model
Directory of Open Access Journals (Sweden)
P Sahebsara
2016-06-01
Full Text Available Investigation of broken symmetry phases with long range order in strongly correlated electron systems is among subjects that have always been of interest to condensed matter scientists. In this paper we tried to study the existence of the 120 degrees magnetic spiral order, based on anisotropy in geometrically frustrated triangular lattices, using variational cluster approximation. We observed that by increasing the anisotropy in the system, the spiral order can be found for U≥7.5t and for t'<1.35; however, it is limited by decreasing t' since antiferromagnetism is dominant for t'<0.85t. Studying the Mott transition shows that a paramagnetic insulating phase, called quantum spin liquid, happens in the neighborhood of the spiral ordered phase
Quantifying and modeling birth order effects in autism
National Research Council Canada - National Science Library
Turner, Tychele; Pihur, Vasyl; Chakravarti, Aravinda
2011-01-01
.... We analyzed the data from three publicly available autism family collections in the USA for potential birth order effects and studied the statistical properties of three tests to show that adequate...
Winding number order in the Haldane model with interactions
Alba, E.; Pachos, J. K.; García-Ripoll, J. J.
2016-03-01
We study the Haldane model with nearest-neighbor interactions. This model is physically motivated by the associated implementation with ultracold atoms. We show that the topological phase of the interacting model can be characterized by a physically observable winding number. The robustness of this number extends well beyond the topological insulator phase towards attractive and repulsive interactions that are comparable to the kinetic energy scale of the model. We identify and characterize the relevant phases of the model as a function of the interaction strength.
A second-order modeling study of confined swirling flow
Nikjooy, M.; Mongia, H. C.
1991-01-01
A numerical study of a confined strong swirling flow is presented. Computations are performed using a differential second-moment closure. The effect of inlet dissipation rate on calculated mean and turbulence fields is investigated. Two pressure-strain models with various model constants are examined to demonstrate their influences on the predicted results. Finally, comparison of the differential second-moment calculation is made with algebraic second-moment results to determine a better suited model for complex swirling flows.
Topological signatures of medium range order in amorphous semiconductor models
Energy Technology Data Exchange (ETDEWEB)
Treacy, M. M. J.; Voyles, P. M.; Gibson, J. M.
2000-05-23
The topological local cluster (or Schlaefli cluster) concept of Marians and Hobbs is used to detect topologically crystalline regions in models of disordered tetrahedral semiconductors. The authors present simple algorithms for detecting both Wells-type shortest circuits and O'Keeffe-type rings, which can be used to delineate alternative forms of the Schlaefli cluster in models.
Calculus for cognitive scientists higher order models and their analysis
Peterson, James K
2016-01-01
This book offers a self-study program on how mathematics, computer science and science can be profitably and seamlessly intertwined. This book focuses on two variable ODE models, both linear and nonlinear, and highlights theoretical and computational tools using MATLAB to explain their solutions. It also shows how to solve cable models using separation of variables and the Fourier Series.
Temporal Aggregation in First Order Cointegrated Vector Autoregressive models
DEFF Research Database (Denmark)
Milhøj, Anders; la Cour, Lisbeth Funding
2011-01-01
with the frequency of the data. We also introduce a graphical representation that will prove useful as an additional informational tool for deciding the appropriate cointegration rank of a model. In two examples based on models of time series of different grades of gasoline, we demonstrate the usefulness of our...
Methods for eigenvalue problems with applications in model order reduction
Rommes, J.
2007-01-01
Physical structures and processes are modeled by dynamical systems in a wide range of application areas. The increasing demand for complex components and large structures, together with an increasing demand for detail and accuracy, makes the models larger and more complicated. To be able to simulate
Higher-Order Hamiltonian Model for Unidirectional Water Waves
Bona, J. L.; Carvajal, X.; Panthee, M.; Scialom, M.
2017-10-01
Formally second-order correct, mathematical descriptions of long-crested water waves propagating mainly in one direction are derived. These equations are analogous to the first-order approximations of KdV- or BBM-type. The advantage of these more complex equations is that their solutions corresponding to physically relevant initial perturbations of the rest state may be accurate on a much longer timescale. The initial value problem for the class of equations that emerges from our derivation is then considered. A local well-posedness theory is straightforwardly established by a contraction mapping argument. A subclass of these equations possess a special Hamiltonian structure that implies the local theory can be continued indefinitely.
Reduced-order LPV model of flexible wind turbines from high fidelity aeroelastic codes
DEFF Research Database (Denmark)
Adegas, Fabiano Daher; Sønderby, Ivan Bergquist; Hansen, Morten Hartvig
2013-01-01
Linear aeroelastic models used for stability analysis of wind turbines are commonly of very high order. These high-order models are generally not suitable for control analysis and synthesis. This paper presents a methodology to obtain a reduced-order linear parameter varying (LPV) model from a se...
Artificial neural network (ANN) approach for modeling Zn(II) adsorption in batch process
Energy Technology Data Exchange (ETDEWEB)
Yildiz, Sayiter [Engineering Faculty, Cumhuriyet University, Sivas (Turkmenistan)
2017-09-15
Artificial neural networks (ANN) were applied to predict adsorption efficiency of peanut shells for the removal of Zn(II) ions from aqueous solutions. Effects of initial pH, Zn(II) concentrations, temperature, contact duration and adsorbent dosage were determined in batch experiments. The sorption capacities of the sorbents were predicted with the aid of equilibrium and kinetic models. The Zn(II) ions adsorption onto peanut shell was better defined by the pseudo-second-order kinetic model, for both initial pH, and temperature. The highest R{sup 2} value in isotherm studies was obtained from Freundlich isotherm for the inlet concentration and from Temkin isotherm for the sorbent amount. The high R{sup 2} values prove that modeling the adsorption process with ANN is a satisfactory approach. The experimental results and the predicted results by the model with the ANN were found to be highly compatible with each other.
A generalized cellular automata approach to modeling first order ...
Indian Academy of Sciences (India)
kuleuven.be ... is observed that cellular automata can capture the essential features of a discrete real system, consisting of space, time and ... istry, can be achieved through stochastic modeling (Seybold et al 1997). In fact, the intrinsic nature of any ...
Reduced-Order Models for Acoustic Response Prediction
2011-07-01
predicted frequencies from a FEM. The first two axial natural frequencies were measured using a pair of small piezoelectric strain actuators, one...test. Displacement and velocity relative to the shaker head were measured with a Polytec Model OVF-512 Differential Fiber Optic Vibrometer . The...The vibrometer controller processes the object and reference beams to produce differential velocity and displacement. Dynamic strains were
Multilevel Higher-Order Item Response Theory Models
Huang, Hung-Yu; Wang, Wen-Chung
2014-01-01
In the social sciences, latent traits often have a hierarchical structure, and data can be sampled from multiple levels. Both hierarchical latent traits and multilevel data can occur simultaneously. In this study, we developed a general class of item response theory models to accommodate both hierarchical latent traits and multilevel data. The…
1st order mass balance model - Excel and GAMS
ALS-NSCORT,
2004-01-01
Provider Notes:This zipfile contains the Excel files and GAMS code for a solvable version of the NSCORT mass balance.unzip this in an ECN working directory 1. in model_june04.xls, read the intro sheet and update the working directory cell. 2. run the macro create
Mixed Higher Order Variational Model for Image Recovery
Directory of Open Access Journals (Sweden)
Pengfei Liu
2014-01-01
Full Text Available A novel mixed higher order regularizer involving the first and second degree image derivatives is proposed in this paper. Using spectral decomposition, we reformulate the new regularizer as a weighted L1-L2 mixed norm of image derivatives. Due to the equivalent formulation of the proposed regularizer, an efficient fast projected gradient algorithm combined with monotone fast iterative shrinkage thresholding, called, FPG-MFISTA, is designed to solve the resulting variational image recovery problems under majorization-minimization framework. Finally, we demonstrate the effectiveness of the proposed regularization scheme by the experimental comparisons with total variation (TV scheme, nonlocal TV scheme, and current second degree methods. Specifically, the proposed approach achieves better results than related state-of-the-art methods in terms of peak signal to ratio (PSNR and restoration quality.
Han, Suyue; Chang, Gary Han; Schirmer, Clemens; Modarres-Sadeghi, Yahya
2016-11-01
We construct a reduced-order model (ROM) to study the Wall Shear Stress (WSS) distributions in image-based patient-specific aneurysms models. The magnitude of WSS has been shown to be a critical factor in growth and rupture of human aneurysms. We start the process by running a training case using Computational Fluid Dynamics (CFD) simulation with time-varying flow parameters, such that these parameters cover the range of parameters of interest. The method of snapshot Proper Orthogonal Decomposition (POD) is utilized to construct the reduced-order bases using the training CFD simulation. The resulting ROM enables us to study the flow patterns and the WSS distributions over a range of system parameters computationally very efficiently with a relatively small number of modes. This enables comprehensive analysis of the model system across a range of physiological conditions without the need to re-compute the simulation for small changes in the system parameters.
A Reduced Order, One Dimensional Model of Joint Response
Energy Technology Data Exchange (ETDEWEB)
DOHNER,JEFFREY L.
2000-11-06
As a joint is loaded, the tangent stiffness of the joint reduces due to slip at interfaces. This stiffness reduction continues until the direction of the applied load is reversed or the total interface slips. Total interface slippage in joints is called macro-slip. For joints not undergoing macro-slip, when load reversal occurs the tangent stiffness immediately rebounds to its maximum value. This occurs due to stiction effects at the interface. Thus, for periodic loads, a softening and rebound hardening cycle is produced which defines a hysteretic, energy absorbing trajectory. For many jointed sub-structures, this hysteretic trajectory can be approximated using simple polynomial representations. This allows for complex joint substructures to be represented using simple non-linear models. In this paper a simple one dimensional model is discussed.
Modeling Genetic Regulatory Networks Using First-Order Probabilistic Logic
2013-03-01
that model GRNs from real data. PRISM, a probabilistic learning framework based on B- prolog , was used to program the Bayesian networks. Instead of...intelligence, prolog , gene regulation, “Raf” pathway 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF PAGES 28 19a...probabilistic logic paradigm. PRISM is a probabilistic logical framework based on B- prolog the language extends the Horn clauses to include random variables
Satisfaction classes in nonstandard models of first-order arithmetic
Engström, Fredrik
2002-01-01
A satisfaction class is a set of nonstandard sentences respecting Tarski's truth definition. We are mainly interested in full satisfaction classes, i.e., satisfaction classes which decides all nonstandard sentences. Kotlarski, Krajewski and Lachlan proved in 1981 that a countable model of PA admits a satisfaction class if and only if it is recursively saturated. A proof of this fact is presented in detail in such a way that it is adaptable to a language with function symbols. The idea that a ...
Nonparametric spatial models for clustered ordered periodontal data
Bandyopadhyay, Dipankar; Canale, Antonio
2016-01-01
Clinical attachment level (CAL) is regarded as the most popular measure to assess periodontal disease (PD). These probed tooth-site level measures are usually rounded and recorded as whole numbers (in mm) producing clustered (site measures within a mouth) error-prone ordinal responses representing some ordering of the underlying PD progression. In addition, it is hypothesized that PD progression can be spatially-referenced, i.e., proximal tooth-sites share similar PD status in comparison to sites that are distantly located. In this paper, we develop a Bayesian multivariate probit framework for these ordinal responses where the cut-point parameters linking the observed ordinal CAL levels to the latent underlying disease process can be fixed in advance. The latent spatial association characterizing conditional independence under Gaussian graphs is introduced via a nonparametric Bayesian approach motivated by the probit stick-breaking process, where the components of the stick-breaking weights follows a multivariate Gaussian density with the precision matrix distributed as G-Wishart. This yields a computationally simple, yet robust and flexible framework to capture the latent disease status leading to a natural clustering of tooth-sites and subjects with similar PD status (beyond spatial clustering), and improved parameter estimation through sharing of information. Both simulation studies and application to a motivating PD dataset reveal the advantages of considering this flexible nonparametric ordinal framework over other alternatives. PMID:27524839
Modelling fast forms of visual neural plasticity using a modified second-order motion energy model.
Pavan, Andrea; Contillo, Adriano; Mather, George
2014-12-01
The Adelson-Bergen motion energy sensor is well established as the leading model of low-level visual motion sensing in human vision. However, the standard model cannot predict adaptation effects in motion perception. A previous paper Pavan et al.(Journal of Vision 10:1-17, 2013) presented an extension to the model which uses a first-order RC gain-control circuit (leaky integrator) to implement adaptation effects which can span many seconds, and showed that the extended model's output is consistent with psychophysical data on the classic motion after-effect. Recent psychophysical research has reported adaptation over much shorter time periods, spanning just a few hundred milliseconds. The present paper further extends the sensor model to implement rapid adaptation, by adding a second-order RC circuit which causes the sensor to require a finite amount of time to react to a sudden change in stimulation. The output of the new sensor accounts accurately for psychophysical data on rapid forms of facilitation (rapid visual motion priming, rVMP) and suppression (rapid motion after-effect, rMAE). Changes in natural scene content occur over multiple time scales, and multi-stage leaky integrators of the kind proposed here offer a computational scheme for modelling adaptation over multiple time scales.
Quantization of second-order Lagrangians: Model problem
Moore, R. A.; Scott, T. C.
1991-08-01
Many aspects of a model problem, the Lagrangian of which contains a term depending quadratically on the acceleration, are examined in the regime where the classical solution consists of two independent normal modes. It is shown that the techniques of conversion to a problem of Lagrange, generalized mechanics, and Dirac's method for constrained systems all yield the same canonical form for the Hamiltonian. It is also seen that the resultant canonical equations of motion are equivalent to the Euler-Lagrange equations. In canonical form, all of the standard results apply, quantization follows in the usual way, and the interpretation of the results is straightforward. It is also demonstrated that perturbative methods fail, both classically and quantum mechanically, indicating the need for the nonperturbative techniques applied herein. Finally, it is noted that this result may have fundamental implications for certain relativistic theories.
First-Order Parametric Model of Reflectance Spectra for Dyed Fabrics
2016-02-19
Naval Research Laboratory Washington, DC 20375-5320 NRL/MR/5708--16-9666 First-Order Parametric Model of Reflectance Spectra for Dyed Fabrics D...LIMITATION OF ABSTRACT First-Order Parametric Model of Reflectance Spectra for Dyed Fabrics D. Aiken, S. Ramsey, T. Mayo, S.G. Lambrakos, and J. Peak Naval...Unclassified Unlimited 31 Daniel Aiken (202) 279-5293 Parametric modeling Inverse/direct analysis This report describes a first-order parametric model of
Adsorption kinetics for the removal of chromium (VI) from aqueous ...
African Journals Online (AJOL)
A comparison of kinetic models applied to the adsorption of Cr(VI) ions on the adsorbents was evaluated for the pseudo first-order, the pseudo second-order, Elovich and intraparticle diffusion kinetic models, respectively. Results show that the pseudo second-order kinetic model was found to correlate the experimental data ...
An ordered generalised extreme value model with application to alcohol consumption in Australia.
Harris, Mark N; Ramful, Preety; Zhao, Xueyan
2006-07-01
An Ordered Generalised Extreme Value (OGEV) model by Small (1987) is proposed for application to ordered discrete choice data. Relative to conventional Ordered Probit/Logit (OP/OL) and Multinomial Logit (MNL) models, the OGEV model is flexible, is defined by random utility maximization, and allows for correlation across choices via unobservable individual characteristics according to locations of the choices in the ordering. The OGEV model is applied to unit record data from Australia to study the impacts of prices, income and demographic characteristics on levels of alcohol consumption. Model selection analysis suggests that OGEV is preferred to both OP and MNL for the application.
Kar, J. K.; Panda, Saswati; Rout, G. C.
2017-05-01
We propose here a tight binding model study of the interplay between charge and spin orderings in the CMR manganites taking anisotropic effect due to electron hoppings and spin exchanges. The Hamiltonian consists of the kinetic energies of eg and t2g electrons of manganese ion. It further includes double exchange and Heisenberg interactions. The charge density wave interaction (CDW) describes an extra mechanism for the insulating character of the system. The CDW gap and spin parameters are calculated using Zubarev's Green's function technique and computed self-consistently. The results are reported in this communication.
Saha, Ajoy; Ahammed Shabeer Tp; Gajbhiye, V T; Gupta, Suman; Kumar, Rajesh
2013-07-01
Removal of mixed pesticides, namely alachlor, metolachlor, chlorpyriphos, fipronil, α-endosulfan, β-endosulfan, p,p'-DDT and two metabolites p,p'-DDE and endosulfan sulphate from aqueous solution by batch adsorption onto three commercial organo-modified montmorillonite clays [modified with octadecylamine (ODA-M), modified with dimethyl- dialkylamine (DMDA-M) and modified with octadecylamine and aminopropyltriethoxysilane (ODAAPS-M)] were investigated. Effect of process variables, mainly contact time and initial concentration of mixed pesticides, on adsorption phenomenon were evaluated. To understand the adsorption kinetic pseudo-first-order and pseudo-second-order models were tested. The pseudo-second-order model provided the best fit for explaining adsorption kinetics, on the basis of high correlation coefficient (r) and normalized percent deviation values. The adsorption equilibrium was explained by the Freundlich isotherm (r = 0.951-0.992). High values (0.17-0.52 mg g⁻¹) of Freundlich constant (K(f)) indicated higher affinity of pesticides towards all three organoclays, as a result of hydrophobic interaction between the adsorbent/adsorbate systems. Pesticides with high octanol-water partition coefficient (K(ow)) and low water solubility showed faster adsorption with higher K(f) values as compared to the pesticides with low K(ow) and high water solubility. The order of organoclays for removal efficiency of mixed pesticide was ODAAPS-M > DMDA-M > ODA-M. These findings may find application to decontaminate or treat mixed pesticide contaminated industrial/agricultural waste waters.
Ability, Breadth, and Parsimony in Computational Models of Higher-Order Cognition
Cassimatis, Nicholas L.; Bello, Paul; Langley, Pat
2008-01-01
Computational models will play an important role in our understanding of human higher-order cognition. How can a model's contribution to this goal be evaluated? This article argues that three important aspects of a model of higher-order cognition to evaluate are (a) its ability to reason, solve problems, converse, and learn as well as people do;…
Directory of Open Access Journals (Sweden)
Dilek Teker
2013-01-01
Full Text Available The aim of this research is to compose a new rating methodology and provide credit notches to 23 countries which of 13 are developed and 10 are emerging. There are various literature that explains the determinants of credit ratings. Following the literature, we select 11 variables for our model which of 5 are eliminated by the factor analysis. We use specific dummies to investigate the structural breaks in time and cross section such as pre crises, post crises, BRIC membership, EU membership, OPEC membership, shipbuilder country and platinum reserved country. Then we run an ordered probit model and give credit notches to the countries. We use FITCH ratings as benchmark. Thus, at the end we compare the notches of FITCH with the ones we derive out of our estimated model.
Cheng, Weiwei; Liu, Guoqin; Wang, Xuede; Han, Lipeng
2017-11-08
Acid-washed oil palm wood-based activated carbon (OPAC) has been investigated for its potential application as a promising adsorbent in the removal of glycidyl esters (GEs) from both palm oil and oil model (hexadecane) solution. It was observed that the removal rate of GEs in palm oil was up to >95%, which was significantly higher than other adsorbents used in this study. In batch adsorption system, the adsorption efficiency and performance of acid-washed OPAC were evaluated as a function of several experimental parameters such as contact time, initial glycidyl palmitate (PGE) concentration, adsorbent dose, and temperature. The Langmuir, Freundlich, and Dubinin-Radushkevich models were used to describe the adsorption equilibrium isotherm, and the equilibrium data were fitted best by the Langmuir model. The maximum adsorption capacity of acid-washed OPAC was found to be 36.23 mg/g by using the Langmuir model. The thermodynamic analysis indicated that the adsorption of PGE on acid-washed OPAC was an endothermic and physical process in nature. The experimental data were fitted by using pseudo-first-order, pseudo-second-order, and intraparticle diffusion models. It was found that the kinetic of PGE adsorption onto acid-washed OPAC followed well the pseudo-second-order model for various initial PGE concentrations and the adsorption process was controlled by both film diffusion and intraparticle diffusion. The desorption test indicated the removal of GEs from palm oil was attributed to not only the adsorption of GEs on acid-washed OPAC, but also the degradation of GEs adsorbed at activated sites with acidic character. Furthermore, no significant difference between before and after PGE adsorption in oil quality was observed.
Bayesian Modeling of ChIP-chip Data Through a High-Order Ising Model
Mo, Qianxing
2010-01-29
ChIP-chip experiments are procedures that combine chromatin immunoprecipitation (ChIP) and DNA microarray (chip) technology to study a variety of biological problems, including protein-DNA interaction, histone modification, and DNA methylation. The most important feature of ChIP-chip data is that the intensity measurements of probes are spatially correlated because the DNA fragments are hybridized to neighboring probes in the experiments. We propose a simple, but powerful Bayesian hierarchical approach to ChIP-chip data through an Ising model with high-order interactions. The proposed method naturally takes into account the intrinsic spatial structure of the data and can be used to analyze data from multiple platforms with different genomic resolutions. The model parameters are estimated using the Gibbs sampler. The proposed method is illustrated using two publicly available data sets from Affymetrix and Agilent platforms, and compared with three alternative Bayesian methods, namely, Bayesian hierarchical model, hierarchical gamma mixture model, and Tilemap hidden Markov model. The numerical results indicate that the proposed method performs as well as the other three methods for the data from Affymetrix tiling arrays, but significantly outperforms the other three methods for the data from Agilent promoter arrays. In addition, we find that the proposed method has better operating characteristics in terms of sensitivities and false discovery rates under various scenarios. © 2010, The International Biometric Society.
Adaptively trained reduced-order model for acceleration of oscillatory flow simulations
CSIR Research Space (South Africa)
Oxtoby, Oliver F
2012-07-01
Full Text Available We present an adaptively trained Reduced-Order Model (ROM) to dramatically speed up flow simulations of an oscillatory nature. Such repetitive flowfields are frequently encountered in fluid-structure interaction modelling, aeroelastic flutter being...
Dynamics of a Fractional Order HIV Infection Model with Specific Functional Response and Cure Rate
Directory of Open Access Journals (Sweden)
Adnane Boukhouima
2017-01-01
Full Text Available We propose a fractional order model in this paper to describe the dynamics of human immunodeficiency virus (HIV infection. In the model, the infection transmission process is modeled by a specific functional response. First, we show that the model is mathematically and biologically well posed. Second, the local and global stabilities of the equilibria are investigated. Finally, some numerical simulations are presented in order to illustrate our theoretical results.
An agent-based model of a historical word order change
Bloem, J.; Versloot, A.; Weerman, F.; Berwick, R.; Korhonen, A.; Lenci, A.; Poibeau, T.; Villavicencio, A.
2015-01-01
We aim to demonstrate that agent-based models can be a useful tool for historical linguists, by modeling the historical development of verbal cluster word order in Germanic languages. Our results show that the current order in German may have developed due to increased use of subordinate clauses,
Group-ICA Model Order Highlights Patterns of Functional Brain Connectivity.
Abou Elseoud, Ahmed; Littow, Harri; Remes, Jukka; Starck, Tuomo; Nikkinen, Juha; Nissilä, Juuso; Timonen, Markku; Tervonen, Osmo; Kiviniemi, Vesa
2011-01-01
Resting-state networks (RSNs) can be reliably and reproducibly detected using independent component analysis (ICA) at both individual subject and group levels. Altering ICA dimensionality (model order) estimation can have a significant impact on the spatial characteristics of the RSNs as well as their parcellation into sub-networks. Recent evidence from several neuroimaging studies suggests that the human brain has a modular hierarchical organization which resembles the hierarchy depicted by different ICA model orders. We hypothesized that functional connectivity between-group differences measured with ICA might be affected by model order selection. We investigated differences in functional connectivity using so-called dual regression as a function of ICA model order in a group of unmedicated seasonal affective disorder (SAD) patients compared to normal healthy controls. The results showed that the detected disease-related differences in functional connectivity alter as a function of ICA model order. The volume of between-group differences altered significantly as a function of ICA model order reaching maximum at model order 70 (which seems to be an optimal point that conveys the largest between-group difference) then stabilized afterwards. Our results show that fine-grained RSNs enable better detection of detailed disease-related functional connectivity changes. However, high model orders show an increased risk of false positives that needs to be overcome. Our findings suggest that multilevel ICA exploration of functional connectivity enables optimization of sensitivity to brain disorders.
An isotonic partial credit model for ordering subjects on the basis of their sum scores
Ligtvoet, R.
2012-01-01
In practice, the sum of the item scores is often used as a basis for comparing subjects. For items that have more than two ordered score categories, only the partial credit model (PCM) and special cases of this model imply that the subjects are stochastically ordered on the common latent variable.
Group-ICA model order highlights patterns of functional brain connectivity
Directory of Open Access Journals (Sweden)
Ahmed eAbou Elseoud
2011-06-01
Full Text Available Resting-state networks (RSNs can be reliably and reproducibly detected using independent component analysis (ICA at both individual subject and group levels. Altering ICA dimensionality (model order estimation can have a significant impact on the spatial characteristics of the RSNs as well as their parcellation into sub-networks. Recent evidence from several neuroimaging studies suggests that the human brain has a modular hierarchical organization which resembles the hierarchy depicted by different ICA model orders. We hypothesized that functional connectivity between-group differences measured with ICA might be affected by model order selection. We investigated differences in functional connectivity using so-called dual-regression as a function of ICA model order in a group of unmedicated seasonal affective disorder (SAD patients compared to normal healthy controls. The results showed that the detected disease-related differences in functional connectivity alter as a function of ICA model order. The volume of between-group differences altered significantly as a function of ICA model order reaching maximum at model order 70 (which seems to be an optimal point that conveys the largest between-group difference then stabilized afterwards. Our results show that fine-grained RSNs enable better detection of detailed disease-related functional connectivity changes. However, high model orders show an increased risk of false positives that needs to be overcome. Our findings suggest that multilevel ICA exploration of functional connectivity enables optimization of sensitivity to brain disorders.
Higgs boson mass in the standard model at two-loop order and beyond
Energy Technology Data Exchange (ETDEWEB)
Martin, Stephen P.; Robertson, David G.
2014-10-01
We calculate the mass of the Higgs boson in the standard model in terms of the underlying Lagrangian parameters at complete 2-loop order with leading 3-loop corrections. A computer program implementing the results is provided. The program also computes and minimizes the standard model effective potential in Landau gauge at 2-loop order with leading 3-loop corrections.
Hossain, M A; Ngo, H H; Guo, W S; Nguyen, T V
2012-06-01
Palm oil fruit shells were evaluated as a new bioadsorbent to eliminate toxic copper from water and wastewater. Without any chemical treatment, palm oil fruit shells were washed, dried and grounded into powder (copper was significantly high (between 28 and 60 mg/g) at room temperature. Nonlinear regression analyses for isotherm models revealed that three-parameter isotherms had a better fit to the experimental data (R(2)>0.994) than that of two-parameter isotherms. The copper sorption system was heterogeneous as the values of exponents were lying between 0 and 1. The highly correlated pseudo-second-order kinetics model (R(2)>0.998) ascertained the applicability of copper removal by palm oil fruit shells. Copyright © 2011 Elsevier Ltd. All rights reserved.
{epsilon} expansion analysis of very weak first-order transitions in the cubic anisotropy model. I
Energy Technology Data Exchange (ETDEWEB)
Arnold, P.; Yaffe, L.G. [Department of Physics, University of Washington, Seattle, Washington 98195 (United States)
1997-06-01
The cubic anisotropy model provides a simple example of a system with an arbitrarily weak first-order phase transition. We present an analysis of this model using {epsilon}-expansion techniques with results up to next-to-next-to-leading order in {epsilon}. Specifically, we compute the relative discontinuity of various physical quantities across the transition in the limit that the transition becomes arbitrarily weakly first order. {copyright} {ital 1997} {ital The American Physical Society}
Directory of Open Access Journals (Sweden)
Renxin Xiao
2016-03-01
Full Text Available In order to properly manage lithium-ion batteries of electric vehicles (EVs, it is essential to build the battery model and estimate the state of charge (SOC. In this paper, the fractional order forms of Thevenin and partnership for a new generation of vehicles (PNGV models are built, of which the model parameters including the fractional orders and the corresponding resistance and capacitance values are simultaneously identified based on genetic algorithm (GA. The relationships between different model parameters and SOC are established and analyzed. The calculation precisions of the fractional order model (FOM and integral order model (IOM are validated and compared under hybrid test cycles. Finally, extended Kalman filter (EKF is employed to estimate the SOC based on different models. The results prove that the FOMs can simulate the output voltage more accurately and the fractional order EKF (FOEKF can estimate the SOC more precisely under dynamic conditions.
Stochastic Ordering Using the Latent Trait and the Sum Score in Polytomous IRT Models.
Hemker, Bas T.; Sijtsma, Klaas; Molenaar, Ivo W.; Junker, Brian W.
1997-01-01
Stochastic ordering properties are investigated for a broad class of item response theory (IRT) models for which the monotone likelihood ratio does not hold. A taxonomy is given for nonparametric and parametric models for polytomous models based on the hierarchical relationship between the models. (SLD)
Yeddou Mezenner, N.; Lagha, H.; Kais, H.; Trari, M.
2017-11-01
This study explores the feasibility of pre-treated coffee waste (PCW) as biosorbent for the removal of diazinon. The effect of the pesticide concentration (6-20 mg L-1), contact time, adsorbent dose (0.2-1.2 g L-1), solution pH (3-11.5), temperature (15-40 °C) and co-existing inorganic ions (H2PO4 -, NO3 -) on the diazinon biosorption over PCW is investigated. The experimental results indicate an optimal pH of 7.3 for the diazinon elimination on PCW (1 g L-1). The Langmuir model describes well the isotherm data with a high regression coefficient ( R 2 > 0.990) and a maximum monolayer biosorption capacity of 18.52 mg g-1 at 15 °C. It is also observed that the intra-particle diffusion is not the rate-controlling step. A comparison is evaluated between the pseudo-second-order and intra-particle diffusion kinetic models; the experimental data are well fitted by the pseudo-second-order kinetic model. The biosorption capacity decreases with increasing temperature for a diazinon concentration of 10 mg L-1. The negative enthalpy Δ H° (-63.57 kJ/mol) indicates that the diazinon biosorption onto PCW is exothermic. Under optimal conditions, the biosorption reaches 95% after 90 min. The removal efficiency decreases from 95 to 65.67 and 48.9% for the diazinon alone and in the presence of NO3 - and H2PO4 - (100 mg L-1), respectively.
Index-aware model order reduction methods applications to differential-algebraic equations
Banagaaya, N; Schilders, W H A
2016-01-01
The main aim of this book is to discuss model order reduction (MOR) methods for differential-algebraic equations (DAEs) with linear coefficients that make use of splitting techniques before applying model order reduction. The splitting produces a system of ordinary differential equations (ODE) and a system of algebraic equations, which are then reduced separately. For the reduction of the ODE system, conventional MOR methods can be used, whereas for the reduction of the algebraic systems new methods are discussed. The discussion focuses on the index-aware model order reduction method (IMOR) and its variations, methods for which the so-called index of the original model is automatically preserved after reduction.
Stripe order in the underdoped region of the two-dimensional Hubbard model
Zheng, Bo-Xiao; Chung, Chia-Min; Corboz, Philippe; Ehlers, Georg; Qin, Ming-Pu; Noack, Reinhard M.; Shi, Hao; White, Steven R.; Zhang, Shiwei; Chan, Garnet Kin-Lic
2017-12-01
Competing inhomogeneous orders are a central feature of correlated electron materials, including the high-temperature superconductors. The two-dimensional Hubbard model serves as the canonical microscopic physical model for such systems. Multiple orders have been proposed in the underdoped part of the phase diagram, which corresponds to a regime of maximum numerical difficulty. By combining the latest numerical methods in exhaustive simulations, we uncover the ordering in the underdoped ground state. We find a stripe order that has a highly compressible wavelength on an energy scale of a few kelvin, with wavelength fluctuations coupled to pairing order. The favored filled stripe order is different from that seen in real materials. Our results demonstrate the power of modern numerical methods to solve microscopic models, even in challenging settings.
An alternative assessment of second-order closure models in turbulent shear flows
Speziale, Charles G.; Gatski, Thomas B.
1994-01-01
The performance of three recently proposed second-order closure models is tested in benchmark turbulent shear flows. Both homogeneous shear flow and the log-layer of an equilibrium turbulent boundary layer are considered for this purpose. An objective analysis of the results leads to an assessment of these models that stands in contrast to that recently published by other authors. A variety of pitfalls in the formulation and testing of second-order closure models are uncovered by this analysis.
The confluence model: birth order as a within-family or between-family dynamic?
Zajonc, R B; Sulloway, Frank J
2007-09-01
The confluence model explains birth-order differences in intellectual performance by quantifying the changing dynamics within the family. Wichman, Rodgers, and MacCallum (2006) claimed that these differences are a between-family phenomenon--and hence are not directly related to birth order itself. The study design and analyses presented by Wichman et al. nevertheless suffer from crucial shortcomings, including their use of unfocused tests, which cause statistically significant trends to be overlooked. In addition, Wichman et al. treated birth-order effects as a linear phenomenon thereby ignoring the confluence model's prediction that these two samples may manifest opposing results based on age. This article cites between- and within-family data that demonstrate systematic birth-order effects as predicted by the confluence model. The corpus of evidence invoked here offers strong support for the assumption of the confluence model that birth-order differences in intellectual performance are primarily a within-family phenomenon.
Shah, A A; Xing, W W; Triantafyllidis, V
2017-04-01
In this paper, we develop reduced-order models for dynamic, parameter-dependent, linear and nonlinear partial differential equations using proper orthogonal decomposition (POD). The main challenges are to accurately and efficiently approximate the POD bases for new parameter values and, in the case of nonlinear problems, to efficiently handle the nonlinear terms. We use a Bayesian nonlinear regression approach to learn the snapshots of the solutions and the nonlinearities for new parameter values. Computational efficiency is ensured by using manifold learning to perform the emulation in a low-dimensional space. The accuracy of the method is demonstrated on a linear and a nonlinear example, with comparisons with a global basis approach.
A seventh-order model for dynamic response of an electro-hydraulic servo valve
Directory of Open Access Journals (Sweden)
Liu Changhai
2014-12-01
Full Text Available In this paper, taking two degrees of freedom on the armature–flapper assembly into account, a seventh-order model is deduced and proposed for the dynamic response of a two-stage electro-hydraulic servo valve from nonlinear equations. These deductions are based on fundamental laws of electromagnetism, fluid, and general mechanics. The coefficients of the proposed seventh-order model are derived in terms of servo valve physical parameters and fluid properties explicitly. For validating the results of the proposed model, an AMESim simulation model based on physical laws and the existing low-order models validated by other researchers through experiments are used to compare with the seventh-order model. The results show that the seventh-order model can reflect the physical behavior of the servo valve more explicitly than the existing low-order models and it could provide guidance more easily for a linear control design approach and sensitivity analysis than the AMESim simulation model.
A delta-rule model of numerical and non-numerical order processing.
Verguts, Tom; Van Opstal, Filip
2014-06-01
Numerical and non-numerical order processing share empirical characteristics (distance effect and semantic congruity), but there are also important differences (in size effect and end effect). At the same time, models and theories of numerical and non-numerical order processing developed largely separately. Currently, we combine insights from 2 earlier models to integrate them in a common framework. We argue that the same learning principle underlies numerical and non-numerical orders, but that environmental features determine the empirical differences. Implications for current theories on order processing are pointed out. PsycINFO Database Record (c) 2014 APA, all rights reserved.
SOLVING FRACTIONAL-ORDER COMPETITIVE LOTKA-VOLTERRA MODEL BY NSFD SCHEMES
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S.ZIBAEI
2016-12-01
Full Text Available In this paper, we introduce fractional-order into a model competitive Lotka- Volterra prey-predator system. We will discuss the stability analysis of this fractional system. The non-standard nite difference (NSFD scheme is implemented to study the dynamic behaviors in the fractional-order Lotka-Volterra system. Proposed non-standard numerical scheme is compared with the forward Euler and fourth order Runge-Kutta methods. Numerical results show that the NSFD approach is easy and accurate for implementing when applied to fractional-order Lotka-Volterra model.
Daisuke Nagakura
2004-01-01
In this note, I show that the ordered and sequential probit models are special cases of the multinomial probit model where the disturbance terms in the latent variables degenerate or those variances converge to zero at a certain rate.
Identification and non-integer order modelling of synchronous machines operating as generator
Directory of Open Access Journals (Sweden)
Szymon Racewicz
2012-09-01
Full Text Available This paper presents an original mathematical model of a synchronous generator using derivatives of fractional order. In contrast to classical models composed of a large number of R-L ladders, it comprises half-order impedances, which enable the accurate description of the electromagnetic induction phenomena in a wide frequency range, while minimizing the order and number of model parameters. The proposed model takes into account the skin eff ect in damper cage bars, the eff ects of eddy currents in rotor solid parts, and the saturation of the machine magnetic circuit. The half-order transfer functions used for modelling these phenomena were verifi ed by simulation of ferromagnetic sheet impedance using the fi nite elements method. The analysed machine’s parameters were identified on the basis of SSFR (StandStill Frequency Response characteristics measured on a gradually magnetised synchronous machine.
Directory of Open Access Journals (Sweden)
C. Sumithra
2014-03-01
Full Text Available The feasibility of activated carbon prepared from Moringa oleifera fruit shell waste to remove Basic Violet 3 from aqueous solution was investigated through batch mode contact time studies. The surface chemistry of activated carbon is studied using Boehm titrations and pH of PZC measurements indicates that the surface oxygenated groups are mainly basic in nature. The surface area of the activated carbon is determined using BET method. The kinetics of Basic Violet 3 adsorption are observed to be pH dependent. The experimental data can be explained by Pseudo second order kinetic model. For, Basic Violet 3, the Langmuir model is best suited to stimulate the adsorption isotherms.
An error bound for a discrete reduced order model of a linear multivariable system
Al-Saggaf, Ubaid M.; Franklin, Gene F.
1987-01-01
The design of feasible controllers for high dimension multivariable systems can be greatly aided by a method of model reduction. In order for the design based on the order reduction to include a guarantee of stability, it is sufficient to have a bound on the model error. Previous work has provided such a bound for continuous-time systems for algorithms based on balancing. In this note an L-infinity bound is derived for model error for a method of order reduction of discrete linear multivariable systems based on balancing.
Parameter Sensitivity Analysis for Fractional-Order Modeling of Lithium-Ion Batteries
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Daming Zhou
2016-02-01
Full Text Available This paper presents a novel-fractional-order lithium-ion battery model that is suitable for use in embedded applications. The proposed model uses fractional calculus with an improved Oustaloup approximation method to describe all the internal battery dynamic behaviors. The fractional-order model parameters, such as equivalent circuit component coefficients and fractional-order values, are identified by a genetic algorithm. A modeling parameters sensitivity study using the statistical Multi-Parameter Sensitivity Analysis (MPSA method is then performed and discussed in detail. Through the analysis, the dynamic effects of parameters on the model output performance are obtained. It has been found out from the analysis that the fractional-order values and their corresponding internal dynamics have different degrees of impact on model outputs. Thus, they are considered as crucial parameters to accurately describe a battery’s dynamic voltage responses. To experimentally verify the accuracy of developed fractional-order model and evaluate its performance, the experimental tests are conducted with a hybrid pulse test and a dynamic stress test (DST on two different types of lithium-ion batteries. The results demonstrate the accuracy and usefulness of the proposed fractional-order model on battery dynamic behavior prediction.
Reduced-Order Observer Model for Antiaircraft Artillery (AAA) Tracker Response
1979-08-01
basic concept of this research and for many valuable discussions. 2. TABLE OF CONTENTS Section Page I INTRODUCTION 6 II REDUCED-ORDER OBSERVER MODEL...stochastic part of the gunner model. These randomness sources include the modelling error, the observation error, the neuromotor noise, etc. Mathema
The fractional-order modeling and synchronization of electrically coupled neuron systems
Moaddy, K.
2012-11-01
In this paper, we generalize the integer-order cable model of the neuron system into the fractional-order domain, where the long memory dependence of the fractional derivative can be a better fit for the neuron response. Furthermore, the chaotic synchronization with a gap junction of two or multi-coupled-neurons of fractional-order are discussed. The circuit model, fractional-order state equations and the numerical technique are introduced in this paper for individual and multiple coupled neuron systems with different fractional-orders. Various examples are introduced with different fractional orders using the non-standard finite difference scheme together with the Grünwald-Letnikov discretization process which is easily implemented and reliably accurate. © 2011 Elsevier Ltd. All rights reserved.
Integrated model for pricing, delivery time setting, and scheduling in make-to-order environments
Garmdare, Hamid Sattari; Lotfi, M. M.; Honarvar, Mahboobeh
2017-05-01
Usually, in make-to-order environments which work only in response to the customer's orders, manufacturers for maximizing the profits should offer the best price and delivery time for an order considering the existing capacity and the customer's sensitivity to both the factors. In this paper, an integrated approach for pricing, delivery time setting and scheduling of new arrival orders are proposed based on the existing capacity and accepted orders in system. In the problem, the acquired market demands dependent on the price and delivery time of both the manufacturer and its competitors. A mixed-integer non-linear programming model is presented for the problem. After converting to a pure non-linear model, it is validated through a case study. The efficiency of proposed model is confirmed by comparing it to both the literature and the current practice. Finally, sensitivity analysis for the key parameters is carried out.
A Low Order Model for Analyzing effects of Blade Fatigue Load Control
DEFF Research Database (Denmark)
Kallesøe, Bjarne Skovmose
2006-01-01
A new low order mathematical model is introduced to analyse blade dynamics and blade load reducing control strategies for wind turbines. The model consists of a typical wing section model combined with a rotor speed model, leading to four structural degrees of freedom (flapwise, edgewise, and tor......A new low order mathematical model is introduced to analyse blade dynamics and blade load reducing control strategies for wind turbines. The model consists of a typical wing section model combined with a rotor speed model, leading to four structural degrees of freedom (flapwise, edgewise......, and torsional blade oscillations, and rotor speed). The aerodynamics is described by a model of unsteady aerodynamic. The equations of motion are derived in nonlinear and linear form. The linear equations of motion are used for stability analysis and control design. The nonlinear equations of motion are used...
John Carroll’s Views on Intelligence: Bi-Factor vs. Higher-Order Models
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A. Alexander Beaujean
2015-10-01
Full Text Available The development of factor models is inextricably tied to the history of intelligence research. One of the most commonly-cited scholars in the field is John Carroll, whose three-stratum theory of cognitive ability has been one of the most influential models of cognitive ability in the past 20 years. Nonetheless, there is disagreement about how Carroll conceptualized the factors in his model. Some argue that his model is best represented through a higher-order model, while others argue that a bi-factor model is a better representation. Carroll was explicit about what he perceived the best way to represent his model, but his writings are not always easy to understand. In this article, I clarify his position by first describing the details and implications of bi-factor and higher-order models then show that Carroll’s published views are better represented by a bi-factor model.
Fractional-order mathematical model of an irrigation main canal pool
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Shlomi N. Calderon-Valdez
2015-09-01
Full Text Available In this paper a fractional order model for an irrigation main canal is proposed. It is based on the experiments developed in a laboratory prototype of a hydraulic canal and the application of a direct system identification methodology. The hydraulic processes that take place in this canal are equivalent to those that occur in real main irrigation canals and the results obtained here can therefore be easily extended to real canals. The accuracy of the proposed fractional order model is compared by deriving two other integer-order models of the canal of a complexity similar to that proposed here. The parameters of these three mathematical models have been identified by minimizing the Integral Square Error (ISE performance index existing between the models and the real-time experimental data obtained from the canal prototype. A comparison of the performances of these three models shows that the fractional-order model has the lowest error and therefore the higher accuracy. Experiments showed that our model outperformed the accuracy of the integer-order models by about 25%, which is a significant improvement as regards to capturing the canal dynamics.
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Poornima G. Hiremath
2016-01-01
Full Text Available The present study involves usage of more efficient and eco-friendly zirconium-doped, fluoride-resistant fungal biosorbents for removal of excess fluoride from groundwater. It was observed that >94% fluoride removal was possible at optimal conditions for the four fungal species studied. The adsorption isotherm studies indicated that zirconium-doped Aspergillus ficuum SIT-CH-2, Aspergillus terreus SIT-CH-3, and Aspergillus flavipes SIT-CH-4 were best described by Freundlich isotherm and zirconium-doped Penicillium camemberti SIT-CH-1 fitted well with Langmuir adsorption isotherm equation. The pseudo-second-order kinetics model showed the best fit for all of the four zirconium-doped fungal species for the fluoride biosorption.
Constructing set-valued fundamental diagrams from jamiton solutions in second order traffic models
Seibold, Benjamin
2013-09-01
Fundamental diagrams of vehicular traiic ow are generally multivalued in the congested ow regime. We show that such set-valued fundamental diagrams can be constructed systematically from simple second order macroscopic traiic models, such as the classical Payne-Whitham model or the inhomogeneous Aw-Rascle-Zhang model. These second order models possess nonlinear traveling wave solutions, called jamitons, and the multi-valued parts in the fundamental diagram correspond precisely to jamiton-dominated solutions. This study shows that transitions from function-valued to set-valued parts in a fundamental diagram arise naturally in well-known second order models. As a particular consequence, these models intrinsically reproduce traiic phases. © American Institute of Mathematical Sciences.
A new fuzzy multi-objective higher order moment portfolio selection model for diversified portfolios
Yue, Wei; Wang, Yuping
2017-01-01
Due to the important effect of the higher order moments to portfolio returns, the aim of this paper is to make use of the third and fourth moments for fuzzy multi-objective portfolio selection model. Firstly, in order to overcome the low diversity of the obtained solution set and lead to corner solutions for the conventional higher moment portfolio selection models, a new entropy function based on Minkowski measure is proposed as a new objective function and a novel fuzzy multi-objective weighted possibilistic higher order moment portfolio model is presented. Secondly, to solve the proposed model efficiently, a new multi-objective evolutionary algorithm is designed. Thirdly, several portfolio performance evaluation techniques are used to evaluate the performance of the portfolio models. Finally, some experiments are conducted by using the data of Shanghai Stock Exchange and the results indicate the efficiency and effectiveness of the proposed model and algorithm.
Gomes, Marta Castilho; Barbosa-Povoa, Ana Paula; Novais, Augusto Queiroz
2010-01-01
Abstract This research presents a new reactive scheduling methodology for job shop, make-to-order industries. An integer linear programming formulation previously developed by the authors to schedule this type of industries is extended to address the problem of inserting new orders in a predetermined schedule, which is important in order-driven industries. A reactive scheduling algorithm is introduced to iteratively update the schedules. Numerical results on realistic examples o...
Derivation and test of high order fluid model for streamer discharges
A. Markosyan (Aram); S. Dujko (Sasa); U. Ebert (Ute); A. Blaszczyk; R. Hiptmair; P. Leuchtmann; J. Ostrowski
2012-01-01
textabstractA high order fluid model for streamer dynamics is developed by closing the system after the 4th moment of the Boltzmann equation in local mean energy approximation. This is done by approximating the high order pressure tensor in the heat flux equation through the previous moments.
Reardon, Sean F.; Shear, Benjamin R.; Castellano, Katherine E.; Ho, Andrew D.
2017-01-01
Test score distributions of schools or demographic groups are often summarized by frequencies of students scoring in a small number of ordered proficiency categories. We show that heteroskedastic ordered probit (HETOP) models can be used to estimate means and standard deviations of multiple groups' test score distributions from such data. Because…
An efficient flexible-order model for 3D nonlinear water waves
DEFF Research Database (Denmark)
Engsig-Karup, Allan Peter; Bingham, Harry B.; Lindberg, Ole
2009-01-01
The flexible-order, finite difference based fully nonlinear potential flow model described in [H.B. Bingham, H. Zhang, On the accuracy of finite difference solutions for nonlinear water waves, J. Eng. Math. 58 (2007) 211-228] is extended to three dimensions (3D). In order to obtain an optimal...
Stochastic order in dichotomous item response models for fixed, adaptive, and multidimensional tests
van der Linden, Willem J.
Dichotomous IRT models can be viewed as families of stochastically ordered distributions of responses to test items. This paper explores several properties of such distributions. In particular, it is examined under what conditions stochastic order in families of conditional distributions is
Teaching Higher Order Thinking in the Introductory MIS Course: A Model-Directed Approach
Wang, Shouhong; Wang, Hai
2011-01-01
One vision of education evolution is to change the modes of thinking of students. Critical thinking, design thinking, and system thinking are higher order thinking paradigms that are specifically pertinent to business education. A model-directed approach to teaching and learning higher order thinking is proposed. An example of application of the…
The formulation and estimation of a spatial skew-normal generalized ordered-response model.
2016-06-01
This paper proposes a new spatial generalized ordered response model with skew-normal kernel error terms and an : associated estimation method. It contributes to the spatial analysis field by allowing a flexible and parametric skew-normal : distribut...
Regression model for tuning the PID controller with fractional order time delay system
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S.P. Agnihotri
2014-12-01
Full Text Available In this paper a regression model based for tuning proportional integral derivative (PID controller with fractional order time delay system is proposed. The novelty of this paper is that tuning parameters of the fractional order time delay system are optimally predicted using the regression model. In the proposed method, the output parameters of the fractional order system are used to derive the regression function. Here, the regression model depends on the weights of the exponential function. By using the iterative algorithm, the best weight of the regression model is evaluated. Using the regression technique, fractional order time delay systems are tuned and the stability parameters of the system are maintained. The effectiveness and feasibility of the proposed technique is demonstrated through the MATLAB/Simulink platform, as well as testing and comparison using the classical PID controller, Ziegler–Nichols tuning method, Wang tuning method and curve fitting technique base tuning method.
Novel Reduced Order in Time Models for Problems in Nonlinear Aeroelasticity Project
National Aeronautics and Space Administration — Research is proposed for the development and implementation of state of the art, reduced order models for problems in nonlinear aeroelasticity. Highly efficient and...
Universal block diagram based modeling and simulation schemes for fractional-order control systems.
Bai, Lu; Xue, Dingyü
2017-05-08
Universal block diagram based schemes are proposed for modeling and simulating the fractional-order control systems in this paper. A fractional operator block in Simulink is designed to evaluate the fractional-order derivative and integral. Based on the block, the fractional-order control systems with zero initial conditions can be modeled conveniently. For modeling the system with nonzero initial conditions, the auxiliary signal is constructed in the compensation scheme. Since the compensation scheme is very complicated, therefore the integrator chain scheme is further proposed to simplify the modeling procedures. The accuracy and effectiveness of the schemes are assessed in the examples, the computation results testify the block diagram scheme is efficient for all Caputo fractional-order ordinary differential equations (FODEs) of any complexity, including the implicit Caputo FODEs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Nonlinear AeroServoElastic Reduced Order Model for Active Structural Control Project
National Aeronautics and Space Administration — The overall goal of the proposed effort is to develop and demonstrate rigorous model order reduction (MOR) technologies to automatically generate fully coupled,...
The Rural Roads Impact on Education Performance in Antioquia (Colombia): an ordered probit model
National Research Council Canada - National Science Library
Guillermo David Hincapie; Ivan Montoya Gomez; John Jaime Bustamante
2017-01-01
.... For this we propose an ordered logistic model of educational performance, defining as a independent variable a transformation of the rural road densities in relation to the supply of sewage, energy...
Optimizing lengths of confidence intervals: fourth-order efficiency in location models
Klaassen, C.; Venetiaan, S.
2010-01-01
Under regularity conditions the maximum likelihood estimator of the location parameter in a location model is asymptotically efficient among translation equivariant estimators. Additional regularity conditions warrant third- and even fourth-order efficiency, in the sense that no translation
A second order model of noise in saturated semiconductor optical amplifiers
DEFF Research Database (Denmark)
Öhman, Filip; Tromborg, Bjarne; Mørk, Jesper
2005-01-01
We have developed a second order model of spontaneous emission noise in semiconductor optical amplifiers (SOAs). The resulting noise distributions agree well with statistical simulations and explain the measured redistribution of noise in saturated SOAs.......We have developed a second order model of spontaneous emission noise in semiconductor optical amplifiers (SOAs). The resulting noise distributions agree well with statistical simulations and explain the measured redistribution of noise in saturated SOAs....
Nunez-Yanez, Jose L; Chouliaras, Vassilios A.
2005-01-01
First to introduce the concepts of Variable order Markov Modelling into a hardware core. The combination of this powerful statistical modelling technique and arithmetic coding delivers excellent compression ratios superior to classical dictionary-based compression techniques such as ZIP or ARJ. Statistical algorithms are recognized as optimal but software implementations run at orders of magnitude slower than dictionary-based techniques. The innovative hardware architecture obtained in this w...
Long-Range Charge Order in the Extended Holstein-Hubbard Model
Miyao, Tadahiro
2016-10-01
This study investigated the extended Holstein-Hubbard model at half-filling as a model for describing the interplay of electron-electron and electron-phonon couplings. When the electron-phonon and nearest-neighbor electron-electron interactions are strong, we prove the existence of long-range charge order in three or more dimensions at a sufficiently low temperature. As a result, we rigorously justify the phase competition between the antiferromagnetism and charge orders.
Validity testing of third-order nonlinear models for synchronous generators
Energy Technology Data Exchange (ETDEWEB)
Arjona, M.A. [Division de Estudios de Posgrado e Investigacion, Instituto Tecnologico de La Laguna Torreon, Coah. (Mexico); Escarela-Perez, R. [Universidad Autonoma Metropolitana - Azcapotzalco, Departamento de Energia, Av. San Pablo 180, Col. Reynosa, C.P. 02200 (Mexico); Espinosa-Perez, G. [Division de Estudios Posgrado de la Facultad de Ingenieria Universidad Nacional Autonoma de Mexico (Mexico); Alvarez-Ramirez, J. [Universidad Autonoma Metropolitana -Iztapalapa, Division de Ciencias Basicas e Ingenieria (Mexico)
2009-06-15
Third-order nonlinear models are commonly used in control theory for the analysis of the stability of both open-loop and closed-loop synchronous machines. However, the ability of these models to describe the electrical machine dynamics has not been tested experimentally. This work focuses on this issue by addressing the parameters identification problem for third-order models for synchronous generators. For a third-order model describing the dynamics of power angle {delta}, rotor speed {omega} and quadrature axis transient EMF E{sub q}{sup '}, it is shown that the parameters cannot be identified because of the effects of the unknown initial condition of E{sub q}{sup '}. To avoid this situation, a model that incorporates the measured electrical power dynamics is considered, showing that state measurements guarantee the identification of the model parameters. Data obtained from a 7 kVA lab-scale synchronous generator and from a 150 MVA finite-element simulation were used to show that, at least for the worked examples, the estimated parameters display only moderate variations over the operating region. This suggests that third-order models can suffice to describe the main dynamical features of synchronous generators, and that third-order models can be used to design and tune power system stabilizers and voltage regulators. (author)
New Reference State Model of the Third-Order PWL Dynamical Systems
Directory of Open Access Journals (Sweden)
J. Horska
2000-09-01
Full Text Available Starting from the first elementary canonical state model, the newsimple state model of the third-order dynamical systems that belong toClass C is derived. A typical property of this new model is a verysimple form of its partial transformation matrix in the conditions oflinear topological conjugacy, which represents the unity matrix.Complete state equations and the corresponding integrator-based blockdiagram of this model are shown and relation to the first canonicalform is graphically illustrated.
Doping driven metal-insulator transitions and charge orderings in the extended Hubbard model
Kapcia, K J; Capone, M; Amaricci, A
2016-01-01
We perform a thorough study of an extended Hubbard model featuring local and nearest-neighbor Coulomb repulsion. Using dynamical mean-field theory we investigated the zero temperature phase-diagram of this model as a function of the chemical doping. The interplay between local and non-local interaction drives a variety of phase-transitions connecting two distinct charge-ordered insulators, i.e., half-filled and quarter-filled, a charge-ordered metal and a Mott insulating phase. We characterize these transitions and the relative stability of the solutions and we show that the two interactions conspire to stabilize the quarter-filled charge ordered phase.
Three Order Parameters in Quantum XZ Spin-Oscillator Models with Gibbsian Ground States
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Wolodymyr I. Skrypnik
2008-01-01
Full Text Available Quantum models on the hyper-cubic d-dimensional lattice of spin-1/2 particles interacting with linear oscillators are shown to have three ferromagnetic ground state order parameters. Two order parameters coincide with the magnetization in the first and third directions and the third one is a magnetization in a continuous oscillator variable. The proofs use a generalized Peierls argument and two Griffiths inequalities. The class of spin-oscillator Hamiltonians considered manifest maximal ordering in their ground states. The models have relevance for hydrogen-bond ferroelectrics. The simplest of these is proven to have a unique Gibbsian ground state.
Modeling 3D PCMI using the Extended Finite Element Method with higher order elements
Energy Technology Data Exchange (ETDEWEB)
Jiang, W. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Spencer, Benjamin W. [Idaho National Lab. (INL), Idaho Falls, ID (United States)
2017-03-31
This report documents the recent development to enable XFEM to work with higher order elements. It also demonstrates the application of higher order (quadratic) elements to both 2D and 3D models of PCMI problems, where discrete fractures in the fuel are represented using XFEM. The modeling results demonstrate the ability of the higher order XFEM to accurately capture the effects of a crack on the response in the vicinity of the intersecting surfaces of cracked fuel and cladding, as well as represent smooth responses in the regions away from the crack.
Yang, Yi; Wang, Shuqing; Liu, Yang
2014-01-01
Order insertion often occurs in the scheduling process of logistics service supply chain (LSSC), which disturbs normal time scheduling especially in the environment of mass customization logistics service. This study analyses order similarity coefficient and order insertion operation process and then establishes an order insertion scheduling model of LSSC with service capacity and time factors considered. This model aims to minimize the average unit volume operation cost of logistics service integrator and maximize the average satisfaction degree of functional logistics service providers. In order to verify the viability and effectiveness of our model, a specific example is numerically analyzed. Some interesting conclusions are obtained. First, along with the increase of completion time delay coefficient permitted by customers, the possible inserting order volume first increases and then trends to be stable. Second, supply chain performance reaches the best when the volume of inserting order is equal to the surplus volume of the normal operation capacity in mass service process. Third, the larger the normal operation capacity in mass service process is, the bigger the possible inserting order's volume will be. Moreover, compared to increasing the completion time delay coefficient, improving the normal operation capacity of mass service process is more useful. PMID:25276851
A MATHEMATICAL MODELLING APPROACH TO ONE-DAY CRICKET BATTING ORDERS
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Matthews Ovens1
2006-12-01
Full Text Available While scoring strategies and player performance in cricket have been studied, there has been little published work about the influence of batting order with respect to One-Day cricket. We apply a mathematical modelling approach to compute efficiently the expected performance (runs distribution of a cricket batting order in an innings. Among other applications, our method enables one to solve for the probability of one team beating another or to find the optimal batting order for a set of 11 players. The influence of defence and bowling ability can be taken into account in a straightforward manner. In this presentation, we outline how we develop our Markov Chain approach to studying the progress of runs for a batting order of non- identical players along the lines of work in baseball modelling by Bukiet et al., 1997. We describe the issues that arise in applying such methods to cricket, discuss ideas for addressing these difficulties and note limitations on modelling batting order for One-Day cricket. By performing our analysis on a selected subset of the possible batting orders, we apply the model to quantify the influence of batting order in a game of One Day cricket using available real-world data for current players
Modelling stock order flows with non-homogeneous intensities from high-frequency data
Gorshenin, Andrey K.; Korolev, Victor Yu.; Zeifman, Alexander I.; Shorgin, Sergey Ya.; Chertok, Andrey V.; Evstafyev, Artem I.; Korchagin, Alexander Yu.
2013-10-01
A micro-scale model is proposed for the evolution of such information system as the limit order book in financial markets. Within this model, the flows of orders (claims) are described by doubly stochastic Poisson processes taking account of the stochastic character of intensities of buy and sell orders that determine the price discovery mechanism. The proposed multiplicative model of stochastic intensities makes it possible to analyze the characteristics of the order flows as well as the instantaneous proportion of the forces of buyers and sellers, that is, the imbalance process, without modelling the external information background. The proposed model gives the opportunity to link the micro-scale (high-frequency) dynamics of the limit order book with the macro-scale models of stock price processes of the form of subordinated Wiener processes by means of limit theorems of probability theory and hence, to use the normal variance-mean mixture models of the corresponding heavy-tailed distributions. The approach can be useful in different areas with similar properties (e.g., in plasma physics).
Directory of Open Access Journals (Sweden)
Lubna Moin
2009-04-01
Full Text Available This research paper basically explores and compares the different modeling and analysis techniques and than it also explores the model order reduction approach and significance. The traditional modeling and simulation techniques for dynamic systems are generally adequate for single-domain systems only, but the Bond Graph technique provides new strategies for reliable solutions of multi-domain system. They are also used for analyzing linear and non linear dynamic production system, artificial intelligence, image processing, robotics and industrial automation. This paper describes a unique technique of generating the Genetic design from the tree structured transfer function obtained from Bond Graph. This research work combines bond graphs for model representation with Genetic programming for exploring different ideas on design space tree structured transfer function result from replacing typical bond graph element with their impedance equivalent specifying impedance lows for Bond Graph multiport. This tree structured form thus obtained from Bond Graph is applied for generating the Genetic Tree. Application studies will identify key issues and importance for advancing this approach towards becoming on effective and efficient design tool for synthesizing design for Electrical system. In the first phase, the system is modeled using Bond Graph technique. Its system response and transfer function with conventional and Bond Graph method is analyzed and then a approach towards model order reduction is observed. The suggested algorithm and other known modern model order reduction techniques are applied to a 11th order high pass filter [1], with different approach. The model order reduction technique developed in this paper has least reduction errors and secondly the final model retains structural information. The system response and the stability analysis of the system transfer function taken by conventional and by Bond Graph method is compared and
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C.G. Ozoegwu
2016-01-01
Full Text Available The general least squares model for milling process state term is presented. A discrete map for milling stability analysis that is based on the third-order case of the presented general least squares milling state term model is first studied and compared with its third-order counterpart that is based on the interpolation theory. Both numerical rate of convergence and chatter stability results of the two maps are compared using the single degree of freedom (1DOF milling model. The numerical rate of convergence of the presented third-order model is also studied using the two degree of freedom (2DOF milling process model. Comparison gave that stability results from the two maps agree closely but the presented map demonstrated reduction in number of needed calculations leading to about 30% savings in computational time (CT. It is seen in earlier works that accuracy of milling stability analysis using the full-discretization method rises from first-order theory to second-order theory and continues to rise to the third-order theory. The present work confirms this trend. In conclusion, the method presented in this work will enable fast and accurate computation of stability diagrams for use by machinists.
Shi, Jinfei; Zhu, Songqing; Chen, Ruwen
2017-12-01
An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.
Qi, Di
Turbulent dynamical systems are ubiquitous in science and engineering. Uncertainty quantification (UQ) in turbulent dynamical systems is a grand challenge where the goal is to obtain statistical estimates for key physical quantities. In the development of a proper UQ scheme for systems characterized by both a high-dimensional phase space and a large number of instabilities, significant model errors compared with the true natural signal are always unavoidable due to both the imperfect understanding of the underlying physical processes and the limited computational resources available. One central issue in contemporary research is the development of a systematic methodology for reduced order models that can recover the crucial features both with model fidelity in statistical equilibrium and with model sensitivity in response to perturbations. In the first part, we discuss a general mathematical framework to construct statistically accurate reduced-order models that have skill in capturing the statistical variability in the principal directions of a general class of complex systems with quadratic nonlinearity. A systematic hierarchy of simple statistical closure schemes, which are built through new global statistical energy conservation principles combined with statistical equilibrium fidelity, are designed and tested for UQ of these problems. Second, the capacity of imperfect low-order stochastic approximations to model extreme events in a passive scalar field advected by turbulent flows is investigated. The effects in complicated flow systems are considered including strong nonlinear and non-Gaussian interactions, and much simpler and cheaper imperfect models with model error are constructed to capture the crucial statistical features in the stationary tracer field. Several mathematical ideas are introduced to improve the prediction skill of the imperfect reduced-order models. Most importantly, empirical information theory and statistical linear response theory are
Zhu, Zhiwei; Zhou, Xiaoqin
2012-01-01
The main contribution of this paper is the development of a linearized model for describing the dynamic hysteresis behaviors of piezoelectrically actuated fast tool servo (FTS). A linearized hysteresis force model is proposed and mathematically described by a fractional order differential equation. Combining the dynamic modeling of the FTS mechanism, a linearized fractional order dynamic hysteresis (LFDH) model for the piezoelectrically actuated FTS is established. The unique features of the LFDH model could be summarized as follows: (a) It could well describe the rate-dependent hysteresis due to its intrinsic characteristics of frequency-dependent nonlinear phase shifts and amplitude modulations; (b) The linearization scheme of the LFDH model would make it easier to implement the inverse dynamic control on piezoelectrically actuated micro-systems. To verify the effectiveness of the proposed model, a series of experiments are conducted. The toolpaths of the FTS for creating two typical micro-functional surfaces involving various harmonic components with different frequencies and amplitudes are scaled and employed as command signals for the piezoelectric actuator. The modeling errors in the steady state are less than ±2.5% within the full span range which is much smaller than certain state-of-the-art modeling methods, demonstrating the efficiency and superiority of the proposed model for modeling dynamic hysteresis effects. Moreover, it indicates that the piezoelectrically actuated micro systems would be more suitably described as a fractional order dynamic system.
Reduced Order Models for Decision Analysis and Upscaling of Aquifer Heterogeneity
Vesselinov, V. V.; O'Malley, D.; Alexandrov, B.; Moore, B.
2016-12-01
Model-based analyses such as uncertainty quantification, sensitivity analysis, and decision support using complex physics models with numerous model parameters and typically require a huge number of model evaluations (on order of 106). Furthermore, model simulations of complex physics may require substantial computational time. For example, accounting for simultaneously occurring physical processes such as fluid flow and biogeochemical reactions in heterogeneous porous medium may require days of wall-clock computational time. To address these issues, we have developed a methodology for reduced order modeling, which couples support vector regression (SVR), genetic algorithms (GA) and artificial and convolutional neural network (ANN/CNN). SVR is applied to predict the model behavior within prior uncertainty ranges associated with the model parameters. ANN and CNN procedures are applied to upscale heterogeneity of the porous medium. In the upscaling process, fine-scale high-resolution models of heterogeneity are applied to inform coarse-resolution models which have improved computational efficiency while capturing the impact of fine-scale effects at the course scale of interest. These techniques are tested independently on a series of synthetic problems. We also present a decision analysis related to contaminant remediation where the developed reduced order models are applied to reproduce groundwater flow and contaminant transport in a synthetic heterogeneous aquifer. The decision analysis is performed using Bayesian-Information-Gap Decision Theory which is implemented as part of the MADS framework (https://github.com/madsjulia/Mads.jl).
Time-Frequency Analysis Using Warped-Based High-Order Phase Modeling
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Ioana Cornel
2005-01-01
Full Text Available The high-order ambiguity function (HAF was introduced for the estimation of polynomial-phase signals (PPS embedded in noise. Since the HAF is a nonlinear operator, it suffers from noise-masking effects and from the appearance of undesired cross-terms when multicomponents PPS are analyzed. In order to improve the performances of the HAF, the multi-lag HAF concept was proposed. Based on this approach, several advanced methods (e.g., product high-order ambiguity function (PHAF have been recently proposed. Nevertheless, performances of these new methods are affected by the error propagation effect which drastically limits the order of the polynomial approximation. This phenomenon acts especially when a high-order polynomial modeling is needed: representation of the digital modulation signals or the acoustic transient signals. This effect is caused by the technique used for polynomial order reduction, common for existing approaches: signal multiplication with the complex conjugated exponentials formed with the estimated coefficients. In this paper, we introduce an alternative method to reduce the polynomial order, based on the successive unitary signal transformation, according to each polynomial order. We will prove that this method reduces considerably the effect of error propagation. Namely, with this order reduction method, the estimation error at a given order will depend only on the performances of the estimation method.
Order reduction and efficient implementation of nonlinear nonlocal cochlear response models.
Filo, Maurice; Karameh, Fadi; Awad, Mariette
2016-12-01
The cochlea is an indispensable preliminary processing stage in auditory perception that employs mechanical frequency-tuning and electrical transduction of incoming sound waves. Cochlear mechanical responses are shown to exhibit active nonlinear spatiotemporal response dynamics (e.g., otoacoustic emission). To model such phenomena, it is often necessary to incorporate cochlear fluid-membrane interactions. This results in both excessively high-order model formulations and computationally intensive solutions that limit their practical use in simulating the model and analyzing its response even for simple single-tone inputs. In order to address these limitations, the current work employs a control-theoretic framework to reformulate a nonlinear two-dimensional cochlear model into discrete state space models that are of considerably lower order (factor of 8) and are computationally much simpler (factor of 25). It is shown that the reformulated models enjoy sparse matrix structures which permit efficient numerical manipulations. Furthermore, the spatially discretized models are linearized and simplified using balanced transformation techniques to result in lower-order (nonlinear) realizations derived from the dominant Hankel singular values of the system dynamics. Accuracy and efficiency of the reduced-order reformulations are demonstrated under the response to two fixed tones, sweeping tones and, more generally, a brief speech signal. The corresponding responses are compared to those produced by the original model in both frequency and spatiotemporal domains. Although carried out on a specific instance of cochlear models, the introduced framework of control-theoretic model reduction could be applied to a wide class of models that address the micro- and macro-mechanical properties of the cochlea.
Modeling the Monthly Water Balance of a First Order Coastal Forested Watershed
S. V. Harder; Devendra M. Amatya; T. J. Callahan; Carl C. Trettin
2006-01-01
A study has been conducted to evaluate a spreadsheet-based conceptual Thornthwaite monthly water balance model and the process-based DRAINMOD model for their reliability in predicting monthly water budgets of a poorly drained, first order forested watershed at the Santee Experimental Forest located along the Lower Coastal Plain of South Carolina. Measured precipitation...
Extensions of the Ordered Response Model Applied to Consumer Valuation of New Products
Das, J.W.M.
1995-01-01
In an ordered response model the observed variable is based upon classifying an unobserved variable into one out of a finite number of intervals forming a dissection of the real line (cf. Amemiya, 1981). This model considers the boundaries of the intervals as (unknown) deterministic parameters, the
Short-Term Memory for Serial Order: A Recurrent Neural Network Model
Botvinick, Matthew M.; Plaut, David C.
2006-01-01
Despite a century of research, the mechanisms underlying short-term or working memory for serial order remain uncertain. Recent theoretical models have converged on a particular account, based on transient associations between independent item and context representations. In the present article, the authors present an alternative model, according…
First-order fire effects models for land Management: Overview and issues
Elizabeth D. Reinhardt; Matthew B. Dickinson
2010-01-01
We give an overview of the science application process at work in supporting fire management. First-order fire effects models, such as those discussed in accompanying papers, are the building blocks of software systems designed for application to landscapes over time scales from days to centuries. Fire effects may be modeled using empirical, rule based, or process...
Accuracy analysis of the zero-order hold model for digital pulsewidth modulation
DEFF Research Database (Denmark)
Ma, Junpeng; Wang, Xiongfei; Blaabjerg, Frede
2017-01-01
This paper examines the accuracy of the zero-order hold (ZOH) model of the digital pulsewidth modulator (DPWM). The influence of the computational delay on the precision of this equivalent DPWM model is discussed in detail. A compensation method is proposed to compensate the deviation of this DPWM...
Uebersax, John S.
1999-01-01
Describes flexible measures that relax restrictive conditional independence assumptions of latent class analysis. Dichotomous and ordered category manifest variables are viewed as discretized latent continuous variables. Discusses the relationship between the multivariate probit model proposed and the mixed Rasch model of J. Rost (1991). (SLD)
Optimized Second-Order Dynamical Systems and Their RLC Circuit Models with PWL Controlled Sources
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J. Brzobohaty
2004-09-01
Full Text Available Complementary active RLC circuit models with a voltage-controlledvoltage source (VCVS and a current-controlled current source (CCCSfor the second-order autonomous dynamical system realization areproposed. The main advantage of these equivalent circuits is the simplerelation between the state model parameters and their correspondingcircuit parameters, which leads also to simple design formulas.
Approximating recreation site choice: the predictive capability of a lexicographic semi-order model
Alan E. Watson; Joseph W. Roggenbuck
1985-01-01
The relevancy of a lexicographic semi-order model, as a basis for development of a microcomputer-based decision aid for backcountry hikers, was investigated. In an interactive microcomputer exercise, it was found that a decision aid based upon this model may assist recreationists in reduction of an alternative set to a cognitively manageable number.
Li, C.; Reichert, M.U.; Wombacher, Andreas
2009-01-01
In various cases we need to transform a process model into a matrix representation for further analysis. In this paper, we introduce the notion of Order Matrix, which enables unique representation of block-structured process models. We present algorithms for transforming a block-structured process
Fractional-order modeling and State-of-Charge estimation for ultracapacitors
Zhang, Lei; Hu, Xiaosong; Wang, Zhenpo; Sun, Fengchun; Dorrell, David G.
2016-05-01
Ultracapacitors (UCs) have been widely recognized as an enabling energy storage technology in various industrial applications. They hold several advantages including high power density and exceptionally long lifespan over the well-adopted battery technology. Accurate modeling and State-of-Charge (SOC) estimation of UCs are essential for reliability, resilience, and safety in UC-powered system operations. In this paper, a novel fractional-order model composed of a series resistor, a constant-phase-element (CPE), and a Walburg-like element, is proposed to emulate the UC dynamics. The Grünald-Letnikov derivative (GLD) is then employed to discretize the continuous-time fractional-order model. The model parameters are optimally extracted using genetic algorithm (GA), based on the time-domain data acquired through the Federal Urban Driving Schedule (FUDS) test. By means of this fractional-order model, a fractional Kalman filter is synthesized to recursively estimate the UC SOC. Validation results prove that the proposed fractional-order modeling and state estimation scheme is accurate and outperforms current practice based on integer-order techniques.
Variable-order sequence modeling improves bacterial strain discrimination for Ion Torrent DNA reads.
Poulsen, Thomas M; Frith, Martin
2017-06-12
Genome sequencing provides a powerful tool for pathogen detection and can help resolve outbreaks that pose public safety and health risks. Mapping of DNA reads to genomes plays a fundamental role in this approach, where accurate alignment and classification of sequencing data is crucial. Standard mapping methods crudely treat bases as independent from their neighbors. Accuracy might be improved by using higher order paired hidden Markov models (HMMs), which model neighbor effects, but introduce design and implementation issues that have typically made them impractical for read mapping applications. We present a variable-order paired HMM that we term VarHMM, which addresses central issues involved with higher order modeling for sequence alignment. Compared with existing alignment methods, VarHMM is able to model higher order distributions and quantify alignment probabilities with greater detail and accuracy. In a series of comparison tests, in which Ion Torrent sequenced DNA was mapped to similar bacterial strains, VarHMM consistently provided better strain discrimination than any of the other alignment methods that we compared with. Our results demonstrate the advantages of higher ordered probability distribution modeling and also suggest that further development of such models would benefit read mapping in a range of other applications as well.
General two-order-parameter Ginzburg-Landau model with quadratic and quartic interactions.
Ivanov, I P
2009-02-01
The Ginzburg-Landau model with two-order parameters appears in many condensed-matter problems. However, even for scalar order parameters, the most general U(1)-symmetric Landau potential with all quadratic and quartic terms contains 13 independent coefficients and cannot be minimized with straightforward algebra. Here, we develop a geometric approach that circumvents this computational difficulty and allows one to study properties of the model without knowing the exact position of the minimum. In particular, we find the number of minima of the potential, classify explicit symmetries possible in this model, establish conditions when and how these symmetries are spontaneously broken, and explicitly describe the phase diagram.
Empirical analyses of a choice model that captures ordering among attribute values
DEFF Research Database (Denmark)
Mabit, Stefan Lindhard
2017-01-01
In most choice models, the evaluation of attributes depends on differences of attribute values. Some research, mainly in marketing and psychology, suggests that the differences do not give the full picture of how decision makers evaluate choice alternatives, e.g. some decision makers may penalise...... an alternative additionally because it has the highest price. In this paper, we specify a discrete choice model that takes into account the ordering of attribute values across alternatives. This model is used to investigate the effect of attribute value ordering in three case studies related to alternative...
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Bin Wang
2016-01-01
Full Text Available This paper studies the application of frequency distributed model for finite time control of a fractional order nonlinear hydroturbine governing system (HGS. Firstly, the mathematical model of HGS with external random disturbances is introduced. Secondly, a novel terminal sliding surface is proposed and its stability to origin is proved based on the frequency distributed model and Lyapunov stability theory. Furthermore, based on finite time stability and sliding mode control theory, a robust control law to ensure the occurrence of the sliding motion in a finite time is designed for stabilization of the fractional order HGS. Finally, simulation results show the effectiveness and robustness of the proposed scheme.
Analysis of credit linked demand in an inventory model with varying ordering cost.
Banu, Ateka; Mondal, Shyamal Kumar
2016-01-01
In this paper, we have considered an economic order quantity model for deteriorating items with two-level trade credit policy in which a delay in payment is offered by a supplier to a retailer and also an another delay in payment is offered by the retailer to his/her all customers. Here, it is proposed that the demand function is dependent on the length of the customer's credit period and also the duration of offering the credit period. In this article, it is considered that the retailer's ordering cost per order depends on the number of replenishment cycles. The objective of this model is to establish a deterministic EOQ model of deteriorating items for the retailer to decide the position of customers credit period and the number of replenishment cycles in finite time horizon such that the retailer gets the maximum profit. Also, the model is explained with the help of some numerical examples.
Fuzzy Economic Order Quantity (FEOQ Model with Units Lost Due to Deterioration
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Monalisha Pattnaik
2014-08-01
Full Text Available This model investigates the instantaneous fuzzy economic order quantity model by allocating the percentage of units lost dueto deterioration in an on-hand inventory by framing variable ordering cost. The objective is to maximize the fuzzy net profit so as to determine the order quantity, the cycle length and number of units lost due to deterioration in fuzzy decision space. For any given number of replenishment cycles the existence of aunique optimal replenishment schedule are proved and mathematical model is developed to find some important characteristics for the concavity of the fuzzy net profit function. Numerical examples are provided to illustrate the results of proposed model which benefit the retailer and this policy is important, especially for wasting of deteriorating items. Finally, sensitivity analyses of the fuzzy optimal solution with respect to the major parameters are also studied.
A posteriori model validation for the temporal order of directed functional connectivity maps
Beltz, Adriene M.; Molenaar, Peter C. M.
2015-01-01
A posteriori model validation for the temporal order of neural directed functional connectivity maps is rare. This is striking because models that require sequential independence among residuals are regularly implemented. The aim of the current study was (a) to apply to directed functional connectivity maps of functional magnetic resonance imaging data an a posteriori model validation procedure (i.e., white noise tests of one-step-ahead prediction errors combined with decision criteria for revising the maps based upon Lagrange Multiplier tests), and (b) to demonstrate how the procedure applies to single-subject simulated, single-subject task-related, and multi-subject resting state data. Directed functional connectivity was determined by the unified structural equation model family of approaches in order to map contemporaneous and first order lagged connections among brain regions at the group- and individual-levels while incorporating external input, then white noise tests were run. Findings revealed that the validation procedure successfully detected unmodeled sequential dependencies among residuals and recovered higher order (greater than one) simulated connections, and that the procedure can accommodate task-related input. Findings also revealed that lags greater than one were present in resting state data: With a group-level network that contained only contemporaneous and first order connections, 44% of subjects required second order, individual-level connections in order to obtain maps with white noise residuals. Results have broad methodological relevance (e.g., temporal validation is necessary after directed functional connectivity analyses because the presence of unmodeled higher order sequential dependencies may bias parameter estimates) and substantive implications (e.g., higher order lags may be common in resting state data). PMID:26379489
Ordering Cost Reduction in Inventory Model with Defective Items and Backorder Price Discount
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Karuppuchamy Annadurai
2014-01-01
Full Text Available In the real market, as unsatisfied demands occur, the longer the length of lead time is, the smaller the proportion of backorder would be. In order to make up for the inconvenience and even the losses of royal and patient customers, the supplier may offer a backorder price discount to secure orders during the shortage period. Also, ordering policies determined by conventional inventory models may be inappropriate for the situation in which an arrival lot contains some defective items. To compensate for the inconvenience of backordering and to secure orders, the supplier may offer a price discount on the stockout item. The purpose of this study is to explore a coordinated inventory model including defective arrivals by allowing the backorder price discount and ordering cost as decision variables. There are two inventory models proposed in this paper, one with normally distributed demand and another with distribution free demand. A computer code using the software Matlab 7.0 is developed to find the optimal solution and present numerical examples to illustrate the models. The results in the numerical examples indicate that the savings of the total cost are realized through ordering cost reduction and backorder price discount.
Variable-order rotated staggered-grid method for elastic-wave forward modeling
Wang, Wei-Zhong; Hu, Tian-Yue; Lu, Xue-Mei; Qin, Zhen; Li, Yan-Dong; Zhang, Yan
2015-09-01
Numerical simulations of a seismic wavefield are important to analyze seismic wave propagation. Elastic-wave equations are used in data simulation for modeling migration and imaging. In elastic wavefield numerical modeling, the rotated staggered-grid method (RSM) is a modification of the standard staggered-grid method (SSM). The variable-order method is based on the method of variable-length spatial operators and wavefield propagation, and it calculates the real dispersion error by adapting different finite-difference orders to different velocities. In this study, the variable-order rotated staggered-grid method (VRSM) is developed after applying the variable-order method to RSM to solve the numerical dispersion problem of RSM in low-velocity regions and reduce the computation cost. Moreover, based on theoretical dispersion and the real dispersion error of wave propagation calculated with the wave separation method, the application of the original method is extended from acoustic to shear waves, and the calculation is modified from theoretical to time-varying values. A layered model and an overthrust model are used to demonstrate the applicability of VRSM. We also evaluate the order distribution, wave propagation, and computation time. The results suggest that the VRSM order distribution is reasonable and VRSM produces high-precision results with a minimal computation cost.
Improved first-order uncertainty method for water-quality modeling
Melching, C.S.; Anmangandla, S.
1992-01-01
Uncertainties are unavoidable in water-quality modeling and subsequent management decisions. Monte Carlo simulation and first-order uncertainty analysis (involving linearization at central values of the uncertain variables) have been frequently used to estimate probability distributions for water-quality model output due to their simplicity. Each method has its drawbacks: Monte Carlo simulation's is mainly computational time; and first-order analysis are mainly questions of accuracy and representativeness, especially for nonlinear systems and extreme conditions. An improved (advanced) first-order method is presented, where the linearization point varies to match the output level whose exceedance probability is sought. The advanced first-order method is tested on the Streeter-Phelps equation to estimate the probability distribution of critical dissolved-oxygen deficit and critical dissolved oxygen using two hypothetical examples from the literature. The advanced first-order method provides a close approximation of the exceedance probability for the Streeter-Phelps model output estimated by Monte Carlo simulation using less computer time - by two orders of magnitude - regardless of the probability distributions assumed for the uncertain model parameters.
Benchmark experiments for higher-order and full-Stokes ice sheet models (ISMIP–HOM
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F. Pattyn
2008-08-01
Full Text Available We present the results of the first ice sheet model intercomparison project for higher-order and full-Stokes ice sheet models. These models are compared and verified in a series of six experiments of which one has an analytical solution obtained from a perturbation analysis. The experiments are applied to both 2-D and 3-D geometries; five experiments are steady-state diagnostic, and one has a time-dependent prognostic solution. All participating models give results that are in close agreement. A clear distinction can be made between higher-order models and those that solve the full system of equations. The full-Stokes models show a much smaller spread, hence are in better agreement with one another and with the analytical solution.
Frequency Weighted Model Order Reduction Technique and Error Bounds for Discrete Time Systems
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Muhammad Imran
2014-01-01
for whole frequency range. However, certain applications (like controller reduction require frequency weighted approximation, which introduce the concept of using frequency weights in model reduction techniques. Limitations of some existing frequency weighted model reduction techniques include lack of stability of reduced order models (for two sided weighting case and frequency response error bounds. A new frequency weighted technique for balanced model reduction for discrete time systems is proposed. The proposed technique guarantees stable reduced order models even for the case when two sided weightings are present. Efficient technique for frequency weighted Gramians is also proposed. Results are compared with other existing frequency weighted model reduction techniques for discrete time systems. Moreover, the proposed technique yields frequency response error bounds.
Nonperturbative Effects and the Large-Order Behavior of Matrix Models and Topological Strings
Marino, Marcos; Weiss, Marlene
2007-01-01
This work addresses nonperturbative effects in both matrix models and topological strings, and their relation with the large-order behavior of perturbation theory. We study instanton configurations in generic one-cut matrix models, obtaining explicit results for the one-instanton amplitude at both one and two loops. The holographic description of topological strings in terms of matrix models implies that our nonperturbative results also apply to topological strings on toric Calabi-Yau manifolds. This yields very precise predictions for the large-order behavior of the perturbative genus expansion, both in conventional matrix models and in topological string theory. We test these predictions in detail in various examples, including the quartic matrix model, topological strings on the local curve, and Hurwitz theory. In all these cases we provide extensive numerical checks which heavily support our nonperturbative analytical results. Moreover, since all these models have a critical point describing two-dimension...
A Reduced-Order Model of Transport Phenomena for Power Plant Simulation
Energy Technology Data Exchange (ETDEWEB)
Paul Cizmas; Brian Richardson; Thomas Brenner; Raymond Fontenot
2009-09-30
A reduced-order model based on proper orthogonal decomposition (POD) has been developed to simulate transient two- and three-dimensional isothermal and non-isothermal flows in a fluidized bed. Reduced-order models of void fraction, gas and solids temperatures, granular energy, and z-direction gas and solids velocity have been added to the previous version of the code. These algorithms are presented and their implementation is discussed. Verification studies are presented for each algorithm. A number of methods to accelerate the computations performed by the reduced-order model are presented. The errors associated with each acceleration method are computed and discussed. Using a combination of acceleration methods, a two-dimensional isothermal simulation using the reduced-order model is shown to be 114 times faster than using the full-order model. In the pursue of achieving the objectives of the project and completing the tasks planned for this program, several unplanned and unforeseen results, methods and studies have been generated. These additional accomplishments are also presented and they include: (1) a study of the effect of snapshot sampling time on the computation of the POD basis functions, (2) an investigation of different strategies for generating the autocorrelation matrix used to find the POD basis functions, (3) the development and implementation of a bubble detection and tracking algorithm based on mathematical morphology, (4) a method for augmenting the proper orthogonal decomposition to better capture flows with discontinuities, such as bubbles, and (5) a mixed reduced-order/full-order model, called point-mode proper orthogonal decomposition, designed to avoid unphysical due to approximation errors. The limitations of the proper orthogonal decomposition method in simulating transient flows with moving discontinuities, such as bubbling flows, are discussed and several methods are proposed to adapt the method for future use.
Wagner, Jacob W.; Dannenhoffer-Lafage, Thomas; Jin, Jaehyeok; Voth, Gregory A.
2017-07-01
Order parameters (i.e., collective variables) are often used to describe the behavior of systems as they capture different features of the free energy surface. Yet, most coarse-grained (CG) models only employ two- or three-body non-bonded interactions between the CG particles. In situations where these interactions are insufficient for the CG model to reproduce the structural distributions of the underlying fine-grained (FG) model, additional interactions must be included. In this paper, we introduce an approach to expand the basis sets available in the multiscale coarse-graining (MS-CG) methodology by including order parameters. Then, we investigate the ability of an additive local order parameter (e.g., density) and an additive global order parameter (i.e., distance from a hard wall) to improve the description of CG models in interfacial systems. Specifically, we study methanol liquid-vapor coexistence, acetonitrile liquid-vapor coexistence, and acetonitrile liquid confined by hard-wall plates, all using single site CG models. We find that the use of order parameters dramatically improves the reproduction of structural properties of interfacial CG systems relative to the FG reference as compared with pairwise CG interactions alone.
Visual deficits in amblyopia constrain normal models of second-order motion processing.
Simmers, A J; Ledgeway, T; Hutchinson, C V; Knox, P J
2011-09-15
It is well established that amblyopes exhibit deficits in processing first-order (luminance-defined) patterns. This is readily manifest by measuring spatiotemporal sensitivity (i.e. the "window of visibility") to moving luminance gratings. However the window of visibility to moving second-order (texture-defined) patterns has not been systematically studied in amblyopia. To address this issue monocular modulation sensitivity (1/threshold) to first-order motion and four different varieties of second-order motion (modulations of either the contrast, flicker, size or orientation of visual noise) was measured over a five-octave range of spatial and temporal frequencies. Compared to normals amblyopes are not only impaired in the processing of first-order motion, but overall they exhibit both higher thresholds and a much narrower window of visibility to second-order images. However amblyopia can differentially impair the perception of some types of second-order motion much more than others and crucially the precise pattern of deficits varies markedly between individuals (even for those with the same conventional visual acuity measures). For the most severely impaired amblyopes certain second-order (texture) cues to movement in the environment are effectively invisible. These results place important constraints on the possible architecture of models of second-order motion perception in human vision. Copyright © 2011 Elsevier Ltd. All rights reserved.
Eluru, Naveen; Chakour, Vincent; Chamberlain, Morgan; Miranda-Moreno, Luis F
2013-10-01
Vehicle operating speed measured on roadways is a critical component for a host of analysis in the transportation field including transportation safety, traffic flow modeling, roadway geometric design, vehicle emissions modeling, and road user route decisions. The current research effort contributes to the literature on examining vehicle speed on urban roads methodologically and substantively. In terms of methodology, we formulate a new econometric model framework for examining speed profiles. The proposed model is an ordered response formulation of a fractional split model. The ordered nature of the speed variable allows us to propose an ordered variant of the fractional split model in the literature. The proposed formulation allows us to model the proportion of vehicles traveling in each speed interval for the entire segment of roadway. We extend the model to allow the influence of exogenous variables to vary across the population. Further, we develop a panel mixed version of the fractional split model to account for the influence of site-specific unobserved effects. The paper contributes substantively by estimating the proposed model using a unique dataset from Montreal consisting of weekly speed data (collected in hourly intervals) for about 50 local roads and 70 arterial roads. We estimate separate models for local roads and arterial roads. The model estimation exercise considers a whole host of variables including geometric design attributes, roadway attributes, traffic characteristics and environmental factors. The model results highlight the role of various street characteristics including number of lanes, presence of parking, presence of sidewalks, vertical grade, and bicycle route on vehicle speed proportions. The results also highlight the presence of site-specific unobserved effects influencing the speed distribution. The parameters from the modeling exercise are validated using a hold-out sample not considered for model estimation. The results indicate
Dynamo onset as a first-order transition: lessons from a shell model for magnetohydrodynamics.
Sahoo, Ganapati; Mitra, Dhrubaditya; Pandit, Rahul
2010-03-01
We carry out systematic and high-resolution studies of dynamo action in a shell model for magnetohydrodynamic (MHD) turbulence over wide ranges of the magnetic Prandtl number PrM and the magnetic Reynolds number ReM. Our study suggests that it is natural to think of dynamo onset as a nonequilibrium first-order phase transition between two different turbulent, but statistically steady, states. The ratio of the magnetic and kinetic energies is a convenient order parameter for this transition. By using this order parameter, we obtain the stability diagram (or nonequilibrium phase diagram) for dynamo formation in our MHD shell model in the (PrM-1,ReM) plane. The dynamo boundary, which separates dynamo and no-dynamo regions, appears to have a fractal character. We obtain a hysteretic behavior of the order parameter across this boundary and suggestions of nucleation-type phenomena.
Accuracy Analysis of the Zero-Order Hold Model for Digital Pulsewidth Modulation
DEFF Research Database (Denmark)
Ma, Junpeng; Wang, Xiongfei; Blaabjerg, Frede
2018-01-01
This paper analyzes the accuracy of the zero-order hold (ZOH) model for the digital pulsewidth modulator (DPWM) in the s-domain. The s-domain model and the exact z-domain model for the control loop of the single-phase inverter with L-type filter is elaborated for quantifying the deviation...... of the ZOH model for DPWM. The influence of the different computational delay and duty-cycle update modes on this deviation is analyzed in detail. The compensation method for this deviation of the ZOH model is proposed for accurately predicting the stability region of the control system in the s...
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Maneesha Gupta
2013-01-01
Full Text Available Second and third order digital integrators (DIs have been optimized first using Particle Swarm Optimization (PSO with minimized error fitness function obtained by registering mean, median, and standard deviation values in different random iterations. Later indirect discretization using Continued Fraction Expansion (CFE has been used to ascertain a better fitting of proposed integer order optimized DIs into their corresponding fractional counterparts by utilizing their refined properties, now restored in them due to PSO algorithm. Simulation results for the comparisons of the frequency responses of proposed 2nd and 3rd order optimized DIs and proposed discretized mathematical models of half integrators based on them, with their respective existing operators, have been presented. Proposed integer order PSO optimized integrators as well as fractional order integrators (FOIs have been observed to outperform the existing recently published operators in their respective domains reasonably well in complete range of Nyquist frequency.
The second order phase transition in Sn2P2S6 crystals: anharmonic oscillator model
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Yu.M. Vysochanskii
2008-09-01
Full Text Available Statistical theory for ferroelectrics based on triple well anharmonic potential was used for the case of structural second order phase transition in Sn2P2S6 crystals. Parameters of effective Hamiltonian of the model were estimated using available experimental data. These findings confirm the assumption that the phase transition in these crystals is located in crossover region between order-disorder and displacive type, and very closely to tricritical point.
Arslan, Burcu; Taatgen, Niels A; Verbrugge, Rineke
2017-01-01
The focus of studies on second-order false belief reasoning generally was on investigating the roles of executive functions and language with correlational studies. Different from those studies, we focus on the question how 5-year-olds select and revise reasoning strategies in second-order false belief tasks by constructing two computational cognitive models of this process: an instance-based learning model and a reinforcement learning model. Unlike the reinforcement learning model, the instance-based learning model predicted that children who fail second-order false belief tasks would give answers based on first-order theory of mind (ToM) reasoning as opposed to zero-order reasoning. This prediction was confirmed with an empirical study that we conducted with 72 5- to 6-year-old children. The results showed that 17% of the answers were correct and 83% of the answers were wrong. In line with our prediction, 65% of the wrong answers were based on a first-order ToM strategy, while only 29% of them were based on a zero-order strategy (the remaining 6% of subjects did not provide any answer). Based on our instance-based learning model, we propose that when children get feedback "Wrong," they explicitly revise their strategy to a higher level instead of implicitly selecting one of the available ToM strategies. Moreover, we predict that children's failures are due to lack of experience and that with exposure to second-order false belief reasoning, children can revise their wrong first-order reasoning strategy to a correct second-order reasoning strategy.
Arslan, Burcu; Taatgen, Niels A.; Verbrugge, Rineke
2017-01-01
The focus of studies on second-order false belief reasoning generally was on investigating the roles of executive functions and language with correlational studies. Different from those studies, we focus on the question how 5-year-olds select and revise reasoning strategies in second-order false belief tasks by constructing two computational cognitive models of this process: an instance-based learning model and a reinforcement learning model. Unlike the reinforcement learning model, the instance-based learning model predicted that children who fail second-order false belief tasks would give answers based on first-order theory of mind (ToM) reasoning as opposed to zero-order reasoning. This prediction was confirmed with an empirical study that we conducted with 72 5- to 6-year-old children. The results showed that 17% of the answers were correct and 83% of the answers were wrong. In line with our prediction, 65% of the wrong answers were based on a first-order ToM strategy, while only 29% of them were based on a zero-order strategy (the remaining 6% of subjects did not provide any answer). Based on our instance-based learning model, we propose that when children get feedback “Wrong,” they explicitly revise their strategy to a higher level instead of implicitly selecting one of the available ToM strategies. Moreover, we predict that children’s failures are due to lack of experience and that with exposure to second-order false belief reasoning, children can revise their wrong first-order reasoning strategy to a correct second-order reasoning strategy. PMID:28293206
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Alok Dhaundiyal
2016-10-01
Full Text Available This article focuses on the influence of relevant parameters of biomass pyrolysis on the numerical solution of the isothermal nth-order distributed activation energy model (DAEM using the Rayleigh distribution as the initial distribution function F(E of the activation energies. In this study, the integral upper limit, the frequency factor, the reaction order and the scale parameters are investigated. This paper also derived the asymptotic approximation for the DAEM. The influence of these parameters is used to calculate the kinetic parameters of the isothermal nth-order DAEM with the help of thermo-analytical results of TGA/DTG analysis.
Evaluation of the Component Chemical Potentials in Analytical Models for Ordered Alloy Phases
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W. A. Oates
2011-01-01
Full Text Available The component chemical potentials in models of solution phases with a fixed number of sites can be evaluated easily when the Helmholtz energy is known as an analytical function of composition. In the case of ordered phases, however, the situation is less straightforward, because the Helmholtz energy is a functional involving internal order parameters. Because of this, the chemical potentials are usually obtained numerically from the calculated integral Helmholtz energy. In this paper, we show how the component chemical potentials can be obtained analytically in ordered phases via the use of virtual cluster chemical potentials. Some examples are given which illustrate the simplicity of the method.
Study on the Business Cycle Model with Fractional-Order Time Delay under Random Excitation
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Zifei Lin
2017-07-01
Full Text Available Time delay of economic policy and memory property in a real economy system is omnipresent and inevitable. In this paper, a business cycle model with fractional-order time delay which describes the delay and memory property of economic control is investigated. Stochastic averaging method is applied to obtain the approximate analytical solution. Numerical simulations are done to verify the method. The effects of the fractional order, time delay, economic control and random excitation on the amplitude of the economy system are investigated. The results show that time delay, fractional order and intensity of random excitation can all magnify the amplitude and increase the volatility of the economy system.
A high-order model of rotating stall in axial compressors with inlet distortion
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Peng LIN
2017-06-01
Full Text Available In this paper, a high-order distortion model is proposed for analyzing the rotating stall inception process induced by inlet distortion in axial compressors. A distortion-generating screen in the compressor inlet is considered. By assuming a quadratic function for the local flow total pressure-drop, the existing Mansoux model is extended to include the effects of static inlet distortion, and a new high-order distortion model is derived. To illustrate the effectiveness of the distortion model, numerical simulations are performed on an eighteenth-order model. It is demonstrated that long length-scale disturbances emerge out of the distorted background flow, and further induce the onset of rotating stall in advance. In addition, the circumferential non-uniform distribution and time evolution of the axial flow are also shown to be consistent with the existing features. It is thus shown that the high-order distortion model is capable of describing the transient behavior of stall inception and will contribute further to stall detection under inlet distortion.
Approaches for Reduced Order Modeling of Electrically Actuated von Karman Microplates
Saghir, Shahid
2016-07-25
This article presents and compares different approaches to develop reduced order models for the nonlinear von Karman rectangular microplates actuated by nonlinear electrostatic forces. The reduced-order models aim to investigate the static and dynamic behavior of the plate under small and large actuation forces. A fully clamped microplate is considered. Different types of basis functions are used in conjunction with the Galerkin method to discretize the governing equations. First we investigate the convergence with the number of modes retained in the model. Then for validation purpose, a comparison of the static results is made with the results calculated by a nonlinear finite element model. The linear eigenvalue problem for the plate under the electrostatic force is solved for a wide range of voltages up to pull-in. Results among the various reduced-order modes are compared and are also validated by comparing to results of the finite-element model. Further, the reduced order models are employed to capture the forced dynamic response of the microplate under small and large vibration amplitudes. Comparison of the different approaches are made for this case. Keywords: electrically actuated microplates, static analysis, dynamics of microplates, diaphragm vibration, large amplitude vibrations, nonlinear dynamics
An Efficient Stock Recommendation Model Based on Big Order Net Inflow
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Yang Yujun
2016-01-01
Full Text Available In general, the stock trend is mainly driven by the big order transactions. Believing that the stock rise with a large volume is closely associated with the big order net inflow, we propose an efficient stock recommendation model based on big order net inflow in the paper. In order to compute the big order net inflow of stock, we use the M/G/1 queue system to measure all tick-by-tick transaction data. Based on an indicator of the big order net inflow of stock, we select some stocks with the higher value of the net inflow to constitute the prerecommended stock set for the target investor user. In order to recommend some stocks with which this style is familiar them to the target users, we divide lots of investors into several categories using fuzzy clustering method and we should do our best to choose stocks from the stock set once operated by those investors who are in the same category with the target user. The experiment results show that the recommended stocks have better gains during the several days after the recommended stock day and the proposed model can provide reliable investment guidance for the target investors and let them get more stock returns.
Macroscopic degeneracy and order in the 3D plaquette Ising model
Johnston, Desmond A.; Mueller, Marco; Janke, Wolfhard
2015-07-01
The purely plaquette 3D Ising Hamiltonian with the spins living at the vertices of a cubic lattice displays several interesting features. The symmetries of the model lead to a macroscopic degeneracy of the low-temperature phase and prevent the definition of a standard magnetic order parameter. Consideration of the strongly anisotropic limit of the model suggests that a layered, “fuki-nuke” order still exists and we confirm this with multi-canonical simulations. The macroscopic degeneracy of the low-temperature phase also changes the finite-size scaling corrections at the first-order transition in the model and we see this must be taken into account when analyzing our measurements.
Determination of Economic Order Quantity in a fuzzy EOQ Model using of GMIR Deffuzification
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Hamidreza Salmani Mojaveri
2017-03-01
Full Text Available Inappropriate inventory control policies and its incorrect implementation can cause improper operation and uncompetitive advantage of organization logistic operation in the market. Therefore, analysis inventory control policies are important to be understood, including carrying cost, ordering cost, warehouse renting cost, and buying cost. In this research, Economic Order Quantity (EOQ problem in fuzzy condition is reviewed in two different situations. The first model concerned to costs (carrying cost, ordering cost, warehouse renting cost and buying cost, which is considered as triangular fuzzy numbers. The second model was in addition to inventory the cost system, in which annual demand is also reviewed as fuzzy numbers. In each model, graded mean integration representation (GMIR deffuzification was used for parameters deffuzification. Then, the final objective from this analysis was to obtain economic quantity formula through derivation.
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Lin Wen Feng
2016-01-01
Full Text Available In this article, we study inventory models to determine the optimal special order and maximum saving cost of imperfective items when the supplier offers a temporary discount. The received items are not all perfect and the defectives can be screened out by the end of 100% screening process. Three models are considered according to the special order occurs at regular replenishment time, non-regular replenishment time, and screening time of economic order quantity cycle. Each model has two sub-cases to be discussed. In temporary discount problems, in general, there are integer operators in objective functions. We suggest theorems to find the closed-form solutions to these kinds of problems. Furthermore, numerical examples and sensitivity analysis are given to illustrate the results of the proposed properties and theorems.
Gómez-Aguilar, J. F.
2018-03-01
In this paper, we analyze an alcoholism model which involves the impact of Twitter via Liouville-Caputo and Atangana-Baleanu-Caputo fractional derivatives with constant- and variable-order. Two fractional mathematical models are considered, with and without delay. Special solutions using an iterative scheme via Laplace and Sumudu transform were obtained. We studied the uniqueness and existence of the solutions employing the fixed point postulate. The generalized model with variable-order was solved numerically via the Adams method and the Adams-Bashforth-Moulton scheme. Stability and convergence of the numerical solutions were presented in details. Numerical examples of the approximate solutions are provided to show that the numerical methods are computationally efficient. Therefore, by including both the fractional derivatives and finite time delays in the alcoholism model studied, we believe that we have established a more complete and more realistic indicator of alcoholism model and affect the spread of the drinking.
Yang, Qingxia; Xu, Jun; Cao, Binggang; Li, Xiuqing
2017-01-01
Identification of internal parameters of lithium-ion batteries is a useful tool to evaluate battery performance, and requires an effective model and algorithm. Based on the least square genetic algorithm, a simplified fractional order impedance model for lithium-ion batteries and the corresponding parameter identification method were developed. The simplified model was derived from the analysis of the electrochemical impedance spectroscopy data and the transient response of lithium-ion batteries with different states of charge. In order to identify the parameters of the model, an equivalent tracking system was established, and the method of least square genetic algorithm was applied using the time-domain test data. Experiments and computer simulations were carried out to verify the effectiveness and accuracy of the proposed model and parameter identification method. Compared with a second-order resistance-capacitance (2-RC) model and recursive least squares method, small tracing voltage fluctuations were observed. The maximum battery voltage tracing error for the proposed model and parameter identification method is within 0.5%; this demonstrates the good performance of the model and the efficiency of the least square genetic algorithm to estimate the internal parameters of lithium-ion batteries. PMID:28212405
Efficient parametric analysis of the chemical master equation through model order reduction
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Waldherr Steffen
2012-07-01
Full Text Available Abstract Background Stochastic biochemical reaction networks are commonly modelled by the chemical master equation, and can be simulated as first order linear differential equations through a finite state projection. Due to the very high state space dimension of these equations, numerical simulations are computationally expensive. This is a particular problem for analysis tasks requiring repeated simulations for different parameter values. Such tasks are computationally expensive to the point of infeasibility with the chemical master equation. Results In this article, we apply parametric model order reduction techniques in order to construct accurate low-dimensional parametric models of the chemical master equation. These surrogate models can be used in various parametric analysis task such as identifiability analysis, parameter estimation, or sensitivity analysis. As biological examples, we consider two models for gene regulation networks, a bistable switch and a network displaying stochastic oscillations. Conclusions The results show that the parametric model reduction yields efficient models of stochastic biochemical reaction networks, and that these models can be useful for systems biology applications involving parametric analysis problems such as parameter exploration, optimization, estimation or sensitivity analysis.
[Biomass dynamics of tree branches of higher order. A model analysis].
Galitskiĭ, V V
2012-01-01
The sectional model of biomass dynamics of freely growing tree brahcnes of all orders is presented. The model is an extension of the sectional tree biomass model proposed earlier. The branches model showed bell-shaped dynamics of a branches biomass and, accordingly, boundedness of branch orders number. The important element of the model of branches system is the inter-verticil green biomass. The model is parameterized on the basis of published data on lifespan of branches of different orders and age in which the biomass of skeletal branches of spruce, Picea abies (L.) Karst, reaches the maximum. When adding known peculiarities of spruce growth (such as the initial growth inhibiton and presence of the inter-verticil branches) to the model of biomass dynamics of regular branches system, good appproximation of all natural data by model values is obtained. The possible mechanism of inter-verticil branches appearance in response to improvement of a tree growth conditions, and also their function in a tree growth process, namely replacement of regular branches incapable of appropriate response, is described. Initiation of appearing and/or waking of the sleeping (adventive) buds which give rise to inter-verticil branches is probably caused by rise of pressure of photosynthates in a tree phloem what the published results of experiments on a decapitaion of branches of Wollemia nobilis (Araucariaceae) also testify.
Order Matters: Sequencing Scale-Realistic Versus Simplified Models to Improve Science Learning
Chen, Chen; Schneps, Matthew H.; Sonnert, Gerhard
2016-10-01
Teachers choosing between different models to facilitate students' understanding of an abstract system must decide whether to adopt a model that is simplified and striking or one that is realistic and complex. Only recently have instructional technologies enabled teachers and learners to change presentations swiftly and to provide for learning based on multiple models, thus giving rise to questions about the order of presentation. Using disjoint individual growth modeling to examine the learning of astronomical concepts using a simulation of the solar system on tablets for 152 high school students (age 15), the authors detect both a model effect and an order effect in the use of the Orrery, a simplified model that exaggerates the scale relationships, and the True-to-scale, a proportional model that more accurately represents the realistic scale relationships. Specifically, earlier exposure to the simplified model resulted in diminution of the conceptual gain from the subsequent realistic model, but the realistic model did not impede learning from the following simplified model.
Identification of reduced-order model for an aeroelastic system from flutter test data
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Wei Tang
2017-02-01
Full Text Available Recently, flutter active control using linear parameter varying (LPV framework has attracted a lot of attention. LPV control synthesis usually generates controllers that are at least of the same order as the aeroelastic models. Therefore, the reduced-order model is required by synthesis for avoidance of large computation cost and high-order controller. This paper proposes a new procedure for generation of accurate reduced-order linear time-invariant (LTI models by using system identification from flutter testing data. The proposed approach is in two steps. The well-known poly-reference least squares complex frequency (p-LSCF algorithm is firstly employed for modal parameter identification from frequency response measurement. After parameter identification, the dominant physical modes are determined by clear stabilization diagrams and clustering technique. In the second step, with prior knowledge of physical poles, the improved frequency-domain maximum likelihood (ML estimator is presented for building accurate reduced-order model. Before ML estimation, an improved subspace identification considering the poles constraint is also proposed for initializing the iterative procedure. Finally, the performance of the proposed procedure is validated by real flight flutter test data.
Higher-order anisotropies in the blast-wave model: Disentangling flow and density field anisotropies
Energy Technology Data Exchange (ETDEWEB)
Cimerman, Jakub [Czech Technical University in Prague, FNSPE, Prague (Czech Republic); Comenius University, FMPI, Bratislava (Slovakia); Tomasik, Boris [Czech Technical University in Prague, FNSPE, Prague (Czech Republic); Univerzita Mateja Bela, FPV, Banska Bystrica (Slovakia); Csanad, Mate; Loekoes, Sandor [Eoetvoes Lorand University, Budapest (Hungary)
2017-08-15
We formulate a generalisation of the blast-wave model which is suitable for the description of higher-order azimuthal anisotropies of the hadron production. The model includes anisotropy in the density profile as well as an anisotropy in the transverse expansion velocity field. We then study how these two kinds of anisotropies influence the single-particle distributions and the correlation radii of two-particle correlation functions. Particularly we focus on the third-order anisotropy and consideration is given averaging over different orientations of the event plane. (orig.)
Kinetic order-disorder transitions in a pause-and-go swarming model with memory.
Rimer, Oren; Ariel, Gil
2017-04-21
A two dimensional model of self-propelled particles combining both a pause-and-go movement pattern and memory is studied in simulations. It is shown, that in contrast to previously studied agent based models in two-dimensions, order and disorder are metastable states that can co-exist at some parameter range. In particular, this implies that the formation and decay of global order in swarms may be kinetic rather than a phase transition. Our results explain metastability recently observed in swarming locust and fish. Copyright © 2017 Elsevier Ltd. All rights reserved.
A review of estimators for the fixed-effects ordered logit model
Arne Risa Hole; Andy Dickerson; Luke Munford
2011-01-01
It is well-known that the dummy variable estimator for the fixed-effects ordered logit model is inconsistent when T, the dimension of the panel, is fixed. This talk will review a range of alternative fixed-effects ordered logit estimators that are based on Chamberlain's fixed-effects estimator for the binary logit model. The talk will present Stata code for the estimators and discuss the available evidence on their finite-sample performance. We will conclude by presenting an empirical example...
Kerfriden, P.; Goury, O.; Rabczuk, T.; Bordas, S.P.A.
2013-01-01
We propose in this paper a reduced order modelling technique based on domain partitioning for parametric problems of fracture. We show that coupling domain decomposition and projection-based model order reduction permits to focus the numerical effort where it is most needed: around the zones where damage propagates. No a priori knowledge of the damage pattern is required, the extraction of the corresponding spatial regions being based solely on algebra. The efficiency of the proposed approach is demonstrated numerically with an example relevant to engineering fracture. PMID:23750055
Comlekoglu, T.; Weinberg, S. H.
2017-09-01
Cardiac memory is the dependence of electrical activity on the prior history of one or more system state variables, including transmembrane potential (Vm), ionic current gating, and ion concentrations. While prior work has represented memory either phenomenologically or with biophysical detail, in this study, we consider an intermediate approach of a minimal three-variable cardiomyocyte model, modified with fractional-order dynamics, i.e., a differential equation of order between 0 and 1, to account for history-dependence. Memory is represented via both capacitive memory, due to fractional-order Vm dynamics, that arises due to non-ideal behavior of membrane capacitance; and ionic current gating memory, due to fractional-order gating variable dynamics, that arises due to gating history-dependence. We perform simulations for varying Vm and gating variable fractional-orders and pacing cycle length and measure action potential duration (APD) and incidence of alternans, loss of capture, and spontaneous activity. In the absence of ionic current gating memory, we find that capacitive memory, i.e., decreased Vm fractional-order, typically shortens APD, suppresses alternans, and decreases the minimum cycle length (MCL) for loss of capture. However, in the presence of ionic current gating memory, capacitive memory can prolong APD, promote alternans, and increase MCL. Further, we find that reduced Vm fractional order (typically less than 0.75) can drive phase 4 depolarizations that promote spontaneous activity. Collectively, our results demonstrate that memory reproduced by a fractional-order model can play a role in alternans formation and pacemaking, and in general, can greatly increase the range of electrophysiological characteristics exhibited by a minimal model.
Comlekoglu, T; Weinberg, S H
2017-09-01
Cardiac memory is the dependence of electrical activity on the prior history of one or more system state variables, including transmembrane potential (Vm), ionic current gating, and ion concentrations. While prior work has represented memory either phenomenologically or with biophysical detail, in this study, we consider an intermediate approach of a minimal three-variable cardiomyocyte model, modified with fractional-order dynamics, i.e., a differential equation of order between 0 and 1, to account for history-dependence. Memory is represented via both capacitive memory, due to fractional-order Vm dynamics, that arises due to non-ideal behavior of membrane capacitance; and ionic current gating memory, due to fractional-order gating variable dynamics, that arises due to gating history-dependence. We perform simulations for varying Vm and gating variable fractional-orders and pacing cycle length and measure action potential duration (APD) and incidence of alternans, loss of capture, and spontaneous activity. In the absence of ionic current gating memory, we find that capacitive memory, i.e., decreased Vm fractional-order, typically shortens APD, suppresses alternans, and decreases the minimum cycle length (MCL) for loss of capture. However, in the presence of ionic current gating memory, capacitive memory can prolong APD, promote alternans, and increase MCL. Further, we find that reduced Vm fractional order (typically less than 0.75) can drive phase 4 depolarizations that promote spontaneous activity. Collectively, our results demonstrate that memory reproduced by a fractional-order model can play a role in alternans formation and pacemaking, and in general, can greatly increase the range of electrophysiological characteristics exhibited by a minimal model.
Second-order stochastic differential equation model as an alternative for the ALT and CALT models
Oud, J.H.L.
2010-01-01
The paper first discusses the autoregressive latent trajectory (ALT) model and presents in detail its improved version, the continuous-time autoregressive latent trajectory (CALT) model. Next, serious problems related to the linear components in the ALT and CALT models are dealt with. As an
Point model equations for neutron correlation counting: Extension of Böhnel's equations to any order
Favalli, Andrea; Croft, Stephen; Santi, Peter
2015-09-01
Various methods of autocorrelation neutron analysis may be used to extract information about a measurement item containing spontaneously fissioning material. The two predominant approaches being the time correlation analysis (that make use of a coincidence gate) methods of multiplicity shift register logic and Feynman sampling. The common feature is that the correlated nature of the pulse train can be described by a vector of reduced factorial multiplet rates. We call these singlets, doublets, triplets etc. Within the point reactor model the multiplet rates may be related to the properties of the item, the parameters of the detector, and basic nuclear data constants by a series of coupled algebraic equations - the so called point model equations. Solving, or inverting, the point model equations using experimental calibration model parameters is how assays of unknown items is performed. Currently only the first three multiplets are routinely used. In this work we develop the point model equations to higher order multiplets using the probability generating functions approach combined with the general derivative chain rule, the so called Faà di Bruno Formula. Explicit expression up to 5th order are provided, as well the general iterative formula to calculate any order. This work represents the first necessary step towards determining if higher order multiplets can add value to nondestructive measurement practice for nuclear materials control and accountancy.
Dyjas, Oliver; Ulrich, Rolf
2014-01-01
In typical discrimination experiments, participants are presented with a constant standard and a variable comparison stimulus and their task is to judge which of these two stimuli is larger (comparative judgement). In these experiments, discrimination sensitivity depends on the temporal order of these stimuli (Type B effect) and is usually higher when the standard precedes rather than follows the comparison. Here, we outline how two models of stimulus discrimination can account for the Type B effect, namely the weighted difference model (or basic Sensation Weighting model) and the Internal Reference Model. For both models, the predicted psychometric functions for comparative judgements as well as for equality judgements, in which participants indicate whether they perceived the two stimuli to be equal or not equal, are derived and it is shown that the models also predict a Type B effect for equality judgements. In the empirical part, the models' predictions are evaluated. To this end, participants performed a duration discrimination task with comparative judgements and with equality judgements. In line with the models' predictions, a Type B effect was observed for both judgement types. In addition, a time-order error, as indicated by shifts of the psychometric functions, and differences in response times were observed only for the equality judgement. Since both models entail distinct additional predictions, it seems worthwhile for future research to unite the two models into one conceptual framework.
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Philipp Singer
Full Text Available One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google's PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work.
Singer, Philipp; Helic, Denis; Taraghi, Behnam; Strohmaier, Markus
2014-01-01
One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google's PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form) and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work.
The Effect of Units Lost Due to Deterioration in Fuzzy Economic Order Quantity (FEOQ Model
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M. Pattnaik
2013-07-01
Full Text Available For several decades, the Economic Order Quantity (EOQ model and its variations have received much attention from researchers. Recently, there has been an investigation into an EOQ model incorporating effect of units lost due to deterioration in infinite planning horizon with crisp decision environment. Accounting for holding and ordering cost, as has traditionally been the case of modeling inventory systems in fuzzy environment are investigated which are not precisely known and defined on a bounded interval of real numbers. The question is how reliable are the EOQ models when items stocked deteriorate one time. This paper introduces Fuzzy Economic Order Quantity (FEOQ model in which it assumes that units lost due to deterioration is included in the objective function to properly model the problem in finite planning horizon. The numerical analysis shows that an appropriate fuzzy policy can benefit the retailer and that is significant, especially for deteriorating items is shown to be superior to that of crisp decision making. A computational algorithm using LINGO 13.0 and MATLAB (R2009a software are developed to find the optimal solution. Sensitivity analysis of the optimal solution is also studied and managerial insights are drawn which shows the influence of key model parameters.
Lie Symmetry Analysis of a First-Order Feedback Model of Option Pricing
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Winter Sinkala
2015-01-01
Full Text Available A first-order feedback model of option pricing consisting of a coupled system of two PDEs, a nonliner generalised Black-Scholes equation and the classical Black-Scholes equation, is studied using Lie symmetry analysis. This model arises as an extension of the classical Black-Scholes model when liquidity is incorporated into the market. We compute the admitted Lie point symmetries of the system and construct an optimal system of the associated one-dimensional subalgebras. We also construct some invariant solutions of the model.
Prediction model of sinoatrial node field potential using high order partial least squares.
Feng, Yu; Cao, Hui; Zhang, Yanbin
2015-01-01
High order partial least squares (HOPLS) is a novel data processing method. It is highly suitable for building prediction model which has tensor input and output. The objective of this study is to build a prediction model of the relationship between sinoatrial node field potential and high glucose using HOPLS. The three sub-signals of the sinoatrial node field potential made up the model's input. The concentration and the actuation duration of high glucose made up the model's output. The results showed that on the premise of predicting two dimensional variables, HOPLS had the same predictive ability and a lower dispersion degree compared with partial least squares (PLS).
Energy Technology Data Exchange (ETDEWEB)
Meeks, E.; Chou, C. -P.; Garratt, T.
2013-03-31
Engineering simulations of coal gasifiers are typically performed using computational fluid dynamics (CFD) software, where a 3-D representation of the gasifier equipment is used to model the fluid flow in the gasifier and source terms from the coal gasification process are captured using discrete-phase model source terms. Simulations using this approach can be very time consuming, making it difficult to imbed such models into overall system simulations for plant design and optimization. For such system-level designs, process flowsheet software is typically used, such as Aspen Plus® [1], where each component where each component is modeled using a reduced-order model. For advanced power-generation systems, such as integrated gasifier/gas-turbine combined-cycle systems (IGCC), the critical components determining overall process efficiency and emissions are usually the gasifier and combustor. Providing more accurate and more computationally efficient reduced-order models for these components, then, enables much more effective plant-level design optimization and design for control. Based on the CHEMKIN-PRO and ENERGICO software, we have developed an automated methodology for generating an advanced form of reduced-order model for gasifiers and combustors. The reducedorder model offers representation of key unit operations in flowsheet simulations, while allowing simulation that is fast enough to be used in iterative flowsheet calculations. Using high-fidelity fluiddynamics models as input, Reaction Design’s ENERGICO® [2] software can automatically extract equivalent reactor networks (ERNs) from a CFD solution. For the advanced reduced-order concept, we introduce into the ERN a much more detailed kinetics model than can be included practically in the CFD simulation. The state-of-the-art chemistry solver technology within CHEMKIN-PRO allows that to be accomplished while still maintaining a very fast model turn-around time. In this way, the ERN becomes the basis for
Massa, F.; Turpin, I.; Tison, T.
2017-11-01
The paper focuses on the definition of a reduced order model for linear modal analysis. The aim is to supply a suitable mathematical alternative tool compatible for multiparametric analysis of large finite element model considering numerous variable parameters, numerous mode shapes and significant levels of variation. The initial full eigenvalue problem is so replaced by a reduced one considering an efficient projection basis. To build it, we propose to combine homotopy transformation and perturbation technique for each parameter direction to define a reduced order model compatible with the design space. Finally, a complete finite element application highlights the capabilities of the proposal in terms of precision and computational time.
Modeling two-vehicle crash severity by a bivariate generalized ordered probit approach.
Chiou, Yu-Chiun; Hwang, Cherng-Chwan; Chang, Chih-Chin; Fu, Chiang
2013-03-01
This study simultaneously models crash severity of both parties in two-vehicle accidents at signalized intersections in Taipei City, Taiwan, using a novel bivariate generalized ordered probit (BGOP) model. Estimation results show that the BGOP model performs better than the conventional bivariate ordered probit (BOP) model in terms of goodness-of-fit indices and prediction accuracy and provides a better approach to identify the factors contributing to different severity levels. According to estimated parameters in latent propensity functions and elasticity effects, several key risk factors are identified-driver type (age>65), vehicle type (motorcycle), violation type (alcohol use), intersection type (three-leg and multiple-leg), collision type (rear ended), and lighting conditions (night and night without illumination). Corresponding countermeasures for these risk factors are proposed. Copyright © 2012 Elsevier Ltd. All rights reserved.
Reprint of "Modeling two-vehicle crash severity by a bivariate generalized ordered probit approach".
Chiou, Yu-Chiun; Hwang, Cherng-Chwan; Chang, Chih-Chin; Fu, Chiang
2013-12-01
This study simultaneously models crash severity of both parties in two-vehicle accidents at signalized intersections in Taipei City, Taiwan, using a novel bivariate generalized ordered probit (BGOP) model. Estimation results show that the BGOP model performs better than the conventional bivariate ordered probit (BOP) model in terms of goodness-of-fit indices and prediction accuracy and provides a better approach to identify the factors contributing to different severity levels. According to estimated parameters in latent propensity functions and elasticity effects, several key risk factors are identified-driver type (age>65), vehicle type (motorcycle), violation type (alcohol use), intersection type (three-leg and multiple-leg), collision type (rear ended), and lighting conditions (night and night without illumination). Corresponding countermeasures for these risk factors are proposed. Copyright © 2012 Elsevier Ltd. All rights reserved.
A Comparison of Reduced Order Modeling Techniques Used in Dynamic Substructuring.
Energy Technology Data Exchange (ETDEWEB)
Roettgen, Dan; Seegar, Ben; Tai, Wei Che; Baek, Seunghun; Dossogne, Tilan; Allen, Matthew; Kuether, Robert J.; Brake, Matthew Robert; Mayes, Randall L.
2015-10-01
Experimental dynamic substructuring is a means whereby a mathematical model for a substructure can be obtained experimentally and then coupled to a model for the rest of the assembly to predict the response. Recently, various methods have been proposed that use a transmission simulator to overcome sensitivity to measurement errors and to exercise the interface between the substructures; including the Craig-Bampton, Dual Craig-Bampton, and Craig-Mayes methods. This work compares the advantages and disadvantages of these reduced order modeling strategies for two dynamic substructuring problems. The methods are first used on an analytical beam model to validate the methodologies. Then they are used to obtain an experimental model for structure consisting of a cylinder with several components inside connected to the outside case by foam with uncertain properties. This represents an exceedingly difficult structure to model and so experimental substructuring could be an attractive way to obtain a model of the system.
A Comparison of Reduced Order Modeling Techniques Used in Dynamic Substructuring [PowerPoint
Energy Technology Data Exchange (ETDEWEB)
Roettgen, Dan [Wisc; Seeger, Benjamin [Stuttgart; Tai, Wei Che [Washington; Baek, Seunghun [Michigan; Dossogne, Tilan [Liege; Allen, Matthew S [Wisc; Kuether, Robert J.; Brake, Matthew Robert; Mayes, Randall L.
2016-01-01
Experimental dynamic substructuring is a means whereby a mathematical model for a substructure can be obtained experimentally and then coupled to a model for the rest of the assembly to predict the response. Recently, various methods have been proposed that use a transmission simulator to overcome sensitivity to measurement errors and to exercise the interface between the substructures; including the Craig-Bampton, Dual Craig-Bampton, and Craig-Mayes methods. This work compares the advantages and disadvantages of these reduced order modeling strategies for two dynamic substructuring problems. The methods are first used on an analytical beam model to validate the methodologies. Then they are used to obtain an experimental model for structure consisting of a cylinder with several components inside connected to the outside case by foam with uncertain properties. This represents an exceedingly difficult structure to model and so experimental substructuring could be an attractive way to obtain a model of the system.
Stability Analysis of Flock and Mill rings for 2nd Order Models in Swarming
Albi, G.; Balagué, D.; Carrillo, J. A.; von Brecht, J.
2013-01-01
We study the linear stability of flock and mill ring solutions of two individual based models for biological swarming. The individuals interact via a nonlocal interaction potential that is repulsive in the short range and attractive in the long range. We relate the instability of the flock rings with the instability of the ring solution of the first order model. We observe that repulsive-attractive interactions lead to new configurations for the flock rings such as clustering and fattening fo...
GEORGEON, Olivier L.; Bellet, Thierry; Mille, Alain; Letisserand, Daniel; Martin, Robert
2005-01-01
International audience; Our global objective is to define a framework for car driving behaviour analysis in order to assess driver's situation awareness. We present models, methods and software tools inspired from the "Experience Based Reasoning" theory coming from the field of artificial intelligence. It allows a construction of a representation of the driving activity including data collected in real driving situations as well as interpretations made on the driver's mental model of the situ...
Factors Influencing the Incidence of Obesity in Australia: A Generalized Ordered Probit Model
Avsar, Gulay; Ham, Roger; Tannous, W. Kathy
2017-01-01
The increasing health costs of and the risks factors associated with obesity are well documented. From this perspective, it is important that the propensity of individuals towards obesity is analyzed. This paper uses longitudinal data from the Household Income and Labour Dynamics in Australia (HILDA) Survey for 2005 to 2010 to model those variables which condition the probability of being obese. The model estimated is a random effects generalized ordered probit, which exploits two sources of ...
Liu, Qun; Jiang, Daqing; Hayat, Tasawar; Ahmad, Bashir
2017-09-01
In this paper, we investigate two stochastic SIR epidemic models with higher order perturbation. For the nonautonomous periodic case of the model, by using Has'minskii's theory of periodic solution, we show that the system has at least one nontrivial positive T-periodic solution. For the system disturbed by both the white noise and telephone noise, we establish sufficient conditions for positive recurrence and the existence of ergodic stationary distribution of the positive solution.
Order reduction for models of space structures using modal cost analysis
Skelton, R. E.; Hughes, P. C.; Hablani, H. B.
1982-01-01
Modal cost analysis furnishes a promising methodology for developing dynamical models of space structures for use in control systems analysis. Economy and accuracy can be attained by only retaining vibration modes that contribute significantly to an appropriately defined cost function. Expressions for modal costs (especially simple for 'lightly damped' structures) are derived for attitude control, vibration suppression, and shape control. These techniques are illustrated through application to a high-order finite element model of a large platform-type structure.
High-order rogue wave solutions of the classical massive Thirring model equations
Guo, Lijuan; Wang, Lihong; Cheng, Yi; He, Jingsong
2017-11-01
The nth-order solutions of the classical massive Thirring model (MTM) equations are derived by using the n-fold Darboux transformation. These solutions are expressed by the ratios of the two determinants consisted of 2n eigenfunctions under the reduction conditions. Using this method, rogue waves are constructed explicitly up to the third-order. Three patterns, i.e., fundamental, triangular and circular patterns, of the rogue waves are discussed. The parameter μ in the MTM model plays the role of the mass in the relativistic field theory while in optics it is related to the medium periodic constant, which also results in a significant rotation and a remarkable lengthening of the first-order rogue wave. These results provide new opportunities to observe rouge waves by using a combination of electromagnetically induced transparency and the Bragg scattering four-wave mixing because of large amplitudes.
Vague Sets Security Measure for Steganographic System Based on High-Order Markov Model
Directory of Open Access Journals (Sweden)
Chun-Juan Ouyang
2017-01-01
Full Text Available Security measure is of great importance in both steganography and steganalysis. Considering that statistical feature perturbations caused by steganography in an image are always nondeterministic and that an image is considered nonstationary, in this paper, the steganography is regarded as a fuzzy process. Here a steganographic security measure is proposed. This security measure evaluates the similarity between two vague sets of cover images and stego images in terms of n-order Markov chain to capture the interpixel correlation. The new security measure has proven to have the properties of boundedness, commutativity, and unity. Furthermore, the security measures of zero order, first order, second order, third order, and so forth are obtained by adjusting the order value of n-order Markov chain. Experimental results indicate that the larger n is, the better the measuring ability of the proposed security measure will be. The proposed security measure is more sensitive than other security measures defined under a deterministic distribution model, when the embedding is low. It is expected to provide a helpful guidance for designing secure steganographic algorithms or reliable steganalytic methods.
High order Fuchsian equations for the square lattice Ising model: {chi}-tilde{sup (5)}
Energy Technology Data Exchange (ETDEWEB)
Bostan, A [INRIA Paris-Rocquencourt, Domaine de Voluceau, B.P. 105 78153 Le Chesnay, Cedex (France); Boukraa, S [LPTHIRM and Departement d' Aeronautique, Universite de Blida (Algeria); Guttmann, A J; Jensen, I [ARC Centre of Excellence for Mathematics and Statistics of Complex Systems, Department of Mathematics and Statistics, University of Melbourne, Victoria 3010 (Australia); Hassani, S; Zenine, N [Centre de Recherche Nucleaire d' Alger, 2 Bd. Frantz Fanon, BP 399, 16000 Alger (Algeria); Maillard, J-M [LPTMC, UMR 7600 CNRS, Universite de Paris, Tour 24, 4eme etage, case 121, 4 Place Jussieu, 75252 Paris Cedex 05 (France)], E-mail: alin.bostan@inria.fr, E-mail: boukraa@mail.univ-blida.dz, E-mail: tonyg@ms.unimelb.edu.au, E-mail: I.Jensen@ms.unimelb.edu.au, E-mail: maillard@lptmc.jussieu.fr, E-mail: maillard@lptl.jussieu.fr, E-mail: njzenine@yahoo.com
2009-07-10
We consider the Fuchsian linear differential equation obtained (modulo a prime) for {chi}-tilde{sup (5)}, the five-particle contribution to the susceptibility of the square lattice Ising model. We show that one can understand the factorization of the corresponding linear differential operator from calculations using just a single prime. A particular linear combination of {chi}-tilde{sup (1)} and {chi}-tilde{sup (3)} can be removed from {chi}-tilde{sup (5)} and the resulting series is annihilated by a high order globally nilpotent linear ODE. The corresponding (minimal order) linear differential operator, of order 29, splits into factors of small orders. A fifth-order linear differential operator occurs as the left-most factor of the 'depleted' differential operator and it is shown to be equivalent to the symmetric fourth power of L{sub E}, the linear differential operator corresponding to the elliptic integral E. This result generalizes what we have found for the lower order terms {chi}-tilde{sup (3)} and {chi}-tilde{sup (4)}. We conjecture that a linear differential operator equivalent to a symmetric (n - 1) th power of L{sub E} occurs as a left-most factor in the minimal order linear differential operators for all {chi}-tilde{sup (n)}'s.
Trimming a hazard logic tree with a new model-order-reduction technique
Porter, Keith; Field, Edward; Milner, Kevin R
2017-01-01
The size of the logic tree within the Uniform California Earthquake Rupture Forecast Version 3, Time-Dependent (UCERF3-TD) model can challenge risk analyses of large portfolios. An insurer or catastrophe risk modeler concerned with losses to a California portfolio might have to evaluate a portfolio 57,600 times to estimate risk in light of the hazard possibility space. Which branches of the logic tree matter most, and which can one ignore? We employed two model-order-reduction techniques to simplify the model. We sought a subset of parameters that must vary, and the specific fixed values for the remaining parameters, to produce approximately the same loss distribution as the original model. The techniques are (1) a tornado-diagram approach we employed previously for UCERF2, and (2) an apparently novel probabilistic sensitivity approach that seems better suited to functions of nominal random variables. The new approach produces a reduced-order model with only 60 of the original 57,600 leaves. One can use the results to reduce computational effort in loss analyses by orders of magnitude.
The dual quark condensate in local and nonlocal NJL models: An order parameter for deconfinement?
Directory of Open Access Journals (Sweden)
Federico Marquez
2015-07-01
Full Text Available We study the behavior of the dual quark condensate Σ1 in the Nambu–Jona-Lasinio (NJL model and its nonlocal variant. In quantum chromodynamics Σ1 can be related to the breaking of the center symmetry and is therefore an (approximate order parameter of confinement. The deconfinement transition is then signaled by a strong rise of Σ1 as a function of temperature. However, a similar behavior is also seen in the NJL model, which is known to have no confinement. Indeed, it was shown that in this model the rise of Σ1 is triggered by the chiral phase transition. In order to shed more light on this issue, we calculate Σ1 for several variants of the NJL model, some of which have been suggested to be confining. Switching between “confining” and “non-confining” models and parametrizations we find no qualitative difference in the behavior of Σ1, namely, it always rises in the region of the chiral phase transition. We conclude that without having established a relation to the center symmetry in a given model, Σ1 should not blindly be regarded as an order parameter of confinement.
Using Count Data and Ordered Models in National Forest Recreation Demand Analysis
Simões, Paula; Barata, Eduardo; Cruz, Luis
2013-11-01
This research addresses the need to improve our knowledge on the demand for national forests for recreation and offers an in-depth data analysis supported by the complementary use of count data and ordered models. From a policy-making perspective, while count data models enable the estimation of monetary welfare measures, ordered models allow for the wider use of the database and provide a more flexible analysis of data. The main purpose of this article is to analyse the individual forest recreation demand and to derive a measure of its current use value. To allow a more complete analysis of the forest recreation demand structure the econometric approach supplements the use of count data models with ordered category models using data obtained by means of an on-site survey in the Bussaco National Forest (Portugal). Overall, both models reveal that travel cost and substitute prices are important explanatory variables, visits are a normal good and demographic variables seem to have no influence on demand. In particular, estimated price and income elasticities of demand are quite low. Accordingly, it is possible to argue that travel cost (price) in isolation may be expected to have a low impact on visitation levels.
A probabilistic union model with automatic order selection for noisy speech recognition.
Jancovic, P; Ming, J
2001-09-01
A critical issue in exploiting the potential of the sub-band-based approach to robust speech recognition is the method of combining the sub-band observations, for selecting the bands unaffected by noise. A new method for this purpose, i.e., the probabilistic union model, was recently introduced. This model has been shown to be capable of dealing with band-limited corruption, requiring no knowledge about the band position and statistical distribution of the noise. A parameter within the model, which we call its order, gives the best results when it equals the number of noisy bands. Since this information may not be available in practice, in this paper we introduce an automatic algorithm for selecting the order, based on the state duration pattern generated by the hidden Markov model (HMM). The algorithm has been tested on the TIDIGITS database corrupted by various types of additive band-limited noise with unknown noisy bands. The results have shown that the union model equipped with the new algorithm can achieve a recognition performance similar to that achieved when the number of noisy bands is known. The results show a very significant improvement over the traditional full-band model, without requiring prior information on either the position or the number of noisy bands. The principle of the algorithm for selecting the order based on state duration may also be applied to other sub-band combination methods.
A new order splitting model with stochastic lead times for deterioration items
Sazvar, Zeinab; Akbari Jokar, Mohammad Reza; Baboli, Armand
2014-09-01
In unreliable supply environments, the strategy of pooling lead time risks by splitting replenishment orders among multiple suppliers simultaneously is an attractive sourcing policy that has captured the attention of academic researchers and corporate managers alike. While various assumptions are considered in the models developed, researchers tend to overlook an important inventory category in order splitting models: deteriorating items. In this paper, we study an order splitting policy for a retailer that sells a deteriorating product. The inventory system is modelled as a continuous review system (s, Q) under stochastic lead time. Demand rate per unit time is assumed to be constant over an infinite planning horizon and shortages are backordered completely. We develop two inventory models. In the first model, it is assumed that all the requirements are supplied by only one source, whereas in the second, two suppliers are available. We use sensitivity analysis to determine the situations in which each sourcing policy is the most economic. We then study a real case from the European pharmaceutical industry to demonstrate the applicability and effectiveness of the proposed models. Finally, more promising directions are suggested for future research.
Averaging principle for second-order approximation of heterogeneous models with homogeneous models.
Fibich, Gadi; Gavious, Arieh; Solan, Eilon
2012-11-27
Typically, models with a heterogeneous property are considerably harder to analyze than the corresponding homogeneous models, in which the heterogeneous property is replaced by its average value. In this study we show that any outcome of a heterogeneous model that satisfies the two properties of differentiability and symmetry is O(ε(2)) equivalent to the outcome of the corresponding homogeneous model, where ε is the level of heterogeneity. We then use this averaging principle to obtain new results in queuing theory, game theory (auctions), and social networks (marketing).
Averaging principle for second-order approximation of heterogeneous models with homogeneous models
Fibich, Gadi; Gavious, Arieh; Solan, Eilon
2012-01-01
Typically, models with a heterogeneous property are considerably harder to analyze than the corresponding homogeneous models, in which the heterogeneous property is replaced by its average value. In this study we show that any outcome of a heterogeneous model that satisfies the two properties of differentiability and symmetry is O(ɛ2) equivalent to the outcome of the corresponding homogeneous model, where ɛ is the level of heterogeneity. We then use this averaging principle to obtain new results in queuing theory, game theory (auctions), and social networks (marketing). PMID:23150569
Numerical solution of two dimensional time fractional-order biological population model
Directory of Open Access Journals (Sweden)
Prakash Amit
2016-01-01
Full Text Available In this work, we provide an approximate solution of a parabolic fractional degenerate problem emerging in a spatial diffusion of biological population model using a fractional variational iteration method (FVIM. Four test illustrations are used to show the proficiency and accuracy of the projected scheme. Comparisons between exact solutions and numerical solutions are presented for different values of fractional order α.
A fractional-order model of HIV infection with drug therapy effect
Directory of Open Access Journals (Sweden)
A.A.M. Arafa
2014-10-01
Full Text Available In this paper, the fractional-order model that describes HIV infection of CD4+ T cells with therapy effect is given. Generalized Euler Method (GEM is employed to get numerical solution of such problem. The fractional derivatives are described in the Caputo sense.
Advancing investigation and physical modeling of first-order fire effects on soils
William J. Massman; John M. Frank; Sacha J. Mooney
2010-01-01
Heating soil during intense wildland fires or slash-pile burns can alter the soil irreversibly, resulting in many significant long-term biological, chemical, physical, and hydrological effects. To better understand these long-term effects, it is necessary to improve modeling capability and prediction of the more immediate, or first-order, effects that fire can have on...
Model reduction of second-order network systems using graph clustering
Cheng, Xiaodong; Scherpen, Jacquelien M.A.; Kawano, Yu
2016-01-01
A general framework is proposed for structure-preserving model reduction of a second-order network system. The method is based on graph clustering, and a recursive algorithm is proposed to find an appropriate clustering. Behaviors of nodes are interpreted by transfer functions, and the similarities
Fujimoto, Kenji; Scherpen, Jacquelien M. A.
2010-01-01
This paper discusses balanced realization and model order reduction for both continuous-time and discrete-time general nonlinear systems based on singular value analysis of the corresponding Hankel operators. Singular value analysis clarifies the gain structure of a given nonlinear operator. Here it
On the parameter estimation of first order IMA model corrupted with AR
African Journals Online (AJOL)
On the parameter estimation of first order IMA model corrupted with AR (1) errors. D Eni, S A Mahmud. Abstract. No Abstract. Global Journal of Pure and Applied Physics Vol. 14 (1) 2008 pp. 115-120. Full Text: EMAIL FULL TEXT EMAIL FULL TEXT · DOWNLOAD FULL TEXT DOWNLOAD FULL TEXT.
Rowland, David R.; Jovanoski, Zlatko
2004-01-01
A study of first-year undergraduate students' interpretational difficulties with first-order ordinary differential equations (ODEs) in modelling contexts was conducted using a diagnostic quiz, exam questions and follow-up interviews. These investigations indicate that when thinking about such ODEs, many students muddle thinking about the function…
Directory of Open Access Journals (Sweden)
Ahmad Neirameh
2018-07-01
Full Text Available In this study, we propose a new algorithm to find exact solitary wave solutions of nonlinear time- fractional order of extended biological population model. The new algorithm basically illustrates how two powerful algorithms, conformable fractional derivative and the homogeneous balance method can be combined and used to get exact solutions of fractional partial differential equations.
Compound waves in a higher order nonlinear model of thermoviscous fluids
DEFF Research Database (Denmark)
Rønne Rasmussen, Anders; Sørensen, Mads Peter; Gaididei, Yuri B.
2016-01-01
A generalized traveling wave ansatz is used to investigate compound shock waves in a higher order nonlinear model of a thermoviscous fluid. The fluid velocity potential is written as a traveling wave plus a linear function of space and time. The latter offers the possibility of predicting...
Satisfiability Solving and Model Generation for Quantified First-Order Logic Formulas
Gladisch, Christoph D.
The generation of models, i.e. interpretations, that satisfy first-order logic (FOL) formulas is an important problem in different application domains, such as, e.g., formal software verification, testing, and artificial intelligence. Satisfiability modulo theory (SMT) solvers are the state-of-the-art techniques for handling this problem. A major bottleneck is, however, the handling of quantified formulas.
Brady, Timothy F.; Tenenbaum, Joshua B.
2013-01-01
When remembering a real-world scene, people encode both detailed information about specific objects and higher order information like the overall gist of the scene. However, formal models of change detection, like those used to estimate visual working memory capacity, assume observers encode only a simple memory representation that includes no…
Interface ordering and phase competition in a model Mott-insulator--band-insulator heterostructure
Okamoto, Satoshi; Andrew J. Millis
2005-01-01
The phase diagram of model Mott-insulator--band-insulator heterostructures is studied using the semiclassical approximation to the dynamical-mean-field method as a function of thickness, coupling constant, and charge confinement. An interface-stabilized ferromagnetic phase is found, allow the study of its competition and possible coexistence with the antiferromagnetic order characteristic of the bulk Mott insulator.
Artificial regression based LM tests of mis-specification for ordered probit models
Murphy, Anthony
1994-01-01
Lagrange Multiplier (LM) tests for omitted variables, heteroscedasticity, incorrect functional form, and non-normality in the ordered probit model may be readily calculated using an artificial regression. The proposed artificial regression is both convenient and likely to have better small sample properties than the more common outer product gradient (OPG) form.
Firm-Related Training Tracks: A Random Effects Ordered Probit Model
Groot, Wim; van den Brink, Henriette Maassen
2003-01-01
A random effects ordered response model of training is estimated to analyze the existence of training tracks and time varying coefficients in training frequency. Two waves of a Dutch panel survey of workers are used covering the period 1992-1996. The amount of training received by workers increased during the period 1994-1996 compared to…
Numerical computation of the optimal control model of higher-order ...
African Journals Online (AJOL)
The paper implemented the optimal control problem of higher-order nondispersive wave. The Extended Conjugate Gradient Method [1], was used to compute the optimal values of the control and state variables of the model while the analytical expressions of the state and control variables generated the analytical values.
A Fuel-Sensitive Reduced-Order Model (ROM) for Piston Engine Scaling Analysis
2017-09-29
single-cylinder moving piston case near top dead center at diesel - engine conditions. The ROM provides a real-time engineering analytical tool for liquid...length scaling that may be used toward optimizing engine performance . 15. SUBJECT TERMS reduced-order model, ROM, engine scaling, spray... diesel engine ................................... 20 Approved for public release; distribution is unlimited. 1 1. Introduction A central
CSIR Research Space (South Africa)
Bogaers, Alfred EJ
2010-01-01
Full Text Available In this paper, we implement the method of Proper Orthogonal Decomposition (POD) to generate a reduced order model (ROM) of an optimization based mesh movement technique. In the study it is shown that POD can be used effectively to generate a ROM...
Probabilistic modelling of combined sewer overflow using the First Order Reliability Method
DEFF Research Database (Denmark)
Thorndahl, Søren; Schaarup-Jensen, Kjeld; Jensen, Jacob Birk
2007-01-01
uncertainties on an application of the commercial urban drainage model MOUSE combined with the probabilistic First Order Reliability Method (FORM). Applying statistical characteristics on several years of rainfall, it is possible to derive a parameterization of the rainfall input and the failure probability...
Probabilistic Modelling of Combined Sewer Overflow Using the First Order Reliability Method
DEFF Research Database (Denmark)
Thorndahl, Søren; Schaarup-Jensen, Kjeld; Jensen, Jacob Birk
2008-01-01
uncertainties on an application of the commercial urban drainage model MOUSE combined with the probabilistic First Order Reliability Method (FORM). Applying statistical characteristics on several years of rainfall, it is possible to derive a parameterization of the rainfall input and the failure probability...
Lattice Boltzmann model for high-order nonlinear partial differential equations
Chai, Zhenhua; He, Nanzhong; Guo, Zhaoli; Shi, Baochang
2018-01-01
In this paper, a general lattice Boltzmann (LB) model is proposed for the high-order nonlinear partial differential equation with the form ∂tϕ +∑k=1mαk∂xkΠk(ϕ ) =0 (1 ≤k ≤m ≤6 ), αk are constant coefficients, Πk(ϕ ) are some known differential functions of ϕ . As some special cases of the high-order nonlinear partial differential equation, the classical (m)KdV equation, KdV-Burgers equation, K (n ,n ) -Burgers equation, Kuramoto-Sivashinsky equation, and Kawahara equation can be solved by the present LB model. Compared to the available LB models, the most distinct characteristic of the present model is to introduce some suitable auxiliary moments such that the correct moments of equilibrium distribution function can be achieved. In addition, we also conducted a detailed Chapman-Enskog analysis, and found that the high-order nonlinear partial differential equation can be correctly recovered from the proposed LB model. Finally, a large number of simulations are performed, and it is found that the numerical results agree with the analytical solutions, and usually the present model is also more accurate than the existing LB models [H. Lai and C. Ma, Sci. China Ser. G 52, 1053 (2009), 10.1007/s11433-009-0149-3; H. Lai and C. Ma, Phys. A (Amsterdam) 388, 1405 (2009), 10.1016/j.physa.2009.01.005] for high-order nonlinear partial differential equations.
Reduced Order Model Implementation in the Risk-Informed Safety Margin Characterization Toolkit
Energy Technology Data Exchange (ETDEWEB)
Mandelli, Diego [Idaho National Lab. (INL), Idaho Falls, ID (United States); Smith, Curtis L. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Alfonsi, Andrea [Idaho National Lab. (INL), Idaho Falls, ID (United States); Rabiti, Cristian [Idaho National Lab. (INL), Idaho Falls, ID (United States); Cogliati, Joshua J. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Talbot, Paul W. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Rinaldi, Ivan [Idaho National Lab. (INL), Idaho Falls, ID (United States); Maljovec, Dan [Idaho National Lab. (INL), Idaho Falls, ID (United States); Wang, Bei [Idaho National Lab. (INL), Idaho Falls, ID (United States); Pascucci, Valerio [Idaho National Lab. (INL), Idaho Falls, ID (United States); Zhao, Haihua [Idaho National Lab. (INL), Idaho Falls, ID (United States)
2015-09-01
The RISMC project aims to develop new advanced simulation-based tools to perform Probabilistic Risk Analysis (PRA) for the existing fleet of U.S. nuclear power plants (NPPs). These tools numerically model not only the thermo-hydraulic behavior of the reactor primary and secondary systems but also external events temporal evolution and components/system ageing. Thus, this is not only a multi-physics problem but also a multi-scale problem (both spatial, µm-mm-m, and temporal, ms-s-minutes-years). As part of the RISMC PRA approach, a large amount of computationally expensive simulation runs are required. An important aspect is that even though computational power is regularly growing, the overall computational cost of a RISMC analysis may be not viable for certain cases. A solution that is being evaluated is the use of reduce order modeling techniques. During the FY2015, we investigated and applied reduced order modeling techniques to decrease the RICM analysis computational cost by decreasing the number of simulations runs to perform and employ surrogate models instead of the actual simulation codes. This report focuses on the use of reduced order modeling techniques that can be applied to any RISMC analysis to generate, analyze and visualize data. In particular, we focus on surrogate models that approximate the simulation results but in a much faster time (µs instead of hours/days). We apply reduced order and surrogate modeling techniques to several RISMC types of analyses using RAVEN and RELAP-7 and show the advantages that can be gained.
Magin, Richard L.; Li, Weiguo; Velasco, M. Pilar; Trujillo, Juan; Reiter, David A.; Morgenstern, Ashley; Spencer, Richard G.
2011-01-01
We present a fractional-order extension of the Bloch equations to describe anomalous NMR relaxation phenomena (T1 and T2). The model has solutions in the form of Mittag-Leffler and stretched exponential functions that generalize conventional exponential relaxation. Such functions have been shown by others to be useful for describing dielectric and viscoelastic relaxation in complex, heterogeneous materials. Here, we apply these fractional-order T1 and T2 relaxation models to experiments performed at 9.4 and 11.7 Tesla on type I collagen gels, chondroitin sulfate mixtures, and to bovine nasal cartilage (BNC), a largely isotropic and homogeneous form of cartilage. The results show that the fractional-order analysis captures important features of NMR relaxation that are typically described by multi-exponential decay models. We find that the T2 relaxation of BNC can be described in a unique way by a single fractional-order parameter (α), in contrast to the lack of uniqueness of multi-exponential fits in the realistic setting of a finite signal-to-noise ratio. No anomalous behavior of T1 was observed in BNC. In the single-component gels, for T2 measurements, increasing the concentration of the largest components of cartilage matrix, collagen and chondroitin sulfate, results in a decrease in α, reflecting a more restricted aqueous environment. The quality of the curve fits obtained using Mittag-Leffler and stretched exponential functions are in some cases superior to those obtained using mono- and bi-exponential models. In both gels and BNC, α appears to account for microstructural complexity in the setting of an altered distribution of relaxation times. This work suggests the utility of fractional-order models to describe T2 NMR relaxation processes in biological tissues. PMID:21498095
Higher Order, Hybrid BEM/FEM Methods Applied to Antenna Modeling
Fink, P. W.; Wilton, D. R.; Dobbins, J. A.
2002-01-01
In this presentation, the authors address topics relevant to higher order modeling using hybrid BEM/FEM formulations. The first of these is the limitation on convergence rates imposed by geometric modeling errors in the analysis of scattering by a dielectric sphere. The second topic is the application of an Incomplete LU Threshold (ILUT) preconditioner to solve the linear system resulting from the BEM/FEM formulation. The final tOpic is the application of the higher order BEM/FEM formulation to antenna modeling problems. The authors have previously presented work on the benefits of higher order modeling. To achieve these benefits, special attention is required in the integration of singular and near-singular terms arising in the surface integral equation. Several methods for handling these terms have been presented. It is also well known that achieving he high rates of convergence afforded by higher order bases may als'o require the employment of higher order geometry models. A number of publications have described the use of quadratic elements to model curved surfaces. The authors have shown in an EFIE formulation, applied to scattering by a PEC .sphere, that quadratic order elements may be insufficient to prevent the domination of modeling errors. In fact, on a PEC sphere with radius r = 0.58 Lambda(sub 0), a quartic order geometry representation was required to obtain a convergence benefi.t from quadratic bases when compared to the convergence rate achieved with linear bases. Initial trials indicate that, for a dielectric sphere of the same radius, - requirements on the geometry model are not as severe as for the PEC sphere. The authors will present convergence results for higher order bases as a function of the geometry model order in the hybrid BEM/FEM formulation applied to dielectric spheres. It is well known that the system matrix resulting from the hybrid BEM/FEM formulation is ill -conditioned. For many real applications, a good preconditioner is required
An efficient flexible-order model for coastal and ocean water waves
DEFF Research Database (Denmark)
Engsig-Karup, Allan Peter; Bingham, Harry B.; Lindberg, Ole
Current work are directed toward the development of an improved numerical 3D model for fully nonlinear potential water waves over arbitrary depths. The model is high-order accurate, robust and efficient for large-scale problems, and support will be included for flexibility in the description...... of structures. The mathemathical equations for potential waves in the physical domain is transformed through $\\sigma$-mapping(s) to a time-invariant boundary-fitted domain which then becomes a basis for an efficient solution strategy. The improved 3D numerical model is based on a finite difference method...
Persistent model order reduction for complex dynamical systems using smooth orthogonal decomposition
Ilbeigi, Shahab; Chelidze, David
2017-11-01
Full-scale complex dynamic models are not effective for parametric studies due to the inherent constraints on available computational power and storage resources. A persistent reduced order model (ROM) that is robust, stable, and provides high-fidelity simulations for a relatively wide range of parameters and operating conditions can provide a solution to this problem. The fidelity of a new framework for persistent model order reduction of large and complex dynamical systems is investigated. The framework is validated using several numerical examples including a large linear system and two complex nonlinear systems with material and geometrical nonlinearities. While the framework is used for identifying the robust subspaces obtained from both proper and smooth orthogonal decompositions (POD and SOD, respectively), the results show that SOD outperforms POD in terms of stability, accuracy, and robustness.
Fourth-Order Method for Numerical Integration of Age- and Size-Structured Population Models
Energy Technology Data Exchange (ETDEWEB)
Iannelli, M; Kostova, T; Milner, F A
2008-01-08
In many applications of age- and size-structured population models, there is an interest in obtaining good approximations of total population numbers rather than of their densities. Therefore, it is reasonable in such cases to solve numerically not the PDE model equations themselves, but rather their integral equivalents. For this purpose quadrature formulae are used in place of the integrals. Because quadratures can be designed with any order of accuracy, one can obtain numerical approximations of the solutions with very fast convergence. In this article, we present a general framework and a specific example of a fourth-order method based on composite Newton-Cotes quadratures for a size-structured population model.
Energy Technology Data Exchange (ETDEWEB)
Silveira, L.M.; Kamon, M.; Elfadel, I.; White, J. [Massachusetts Inst. of Technology, Cambridge, MA (United States)
1996-12-31
Model order reduction based on Krylov subspace iterative methods has recently emerged as a major tool for compressing the number of states in linear models used for simulating very large physical systems (VLSI circuits, electromagnetic interactions). There are currently two main methods for accomplishing such a compression: one is based on the nonsymmetric look-ahead Lanczos algorithm that gives a numerically stable procedure for finding Pade approximations, while the other is based on a less well characterized Arnoldi algorithm. In this paper, we show that for certain classes of generalized state-space systems, the reduced-order models produced by a coordinate-transformed Arnoldi algorithm inherit the stability of the original system. Complete Proofs of our results will be given in the final paper.
Najhan Mohd Nagib, Ahmad; Naufal Adnan, Ahmad; Ismail, Azianti; Halim, Nurul Hayati Abdul; Syuhadah Khusaini, Nurul
2016-11-01
The inventory model had been utilized since the early 1900s. The implementation of the inventory management model is generally to ensure that an organisation is able to fulfil customer's demand at the lowest possible cost to improve profitability. This paper focuses on reviewing previous published papers regarding inventory control model mainly in the food and beverage processing industry. The author discusses four inventory models, which are the make-to-stock (MTS), make-to-order (MTO), economic order quantity (EOQ), and hybrid of MTS-MTO models. The issues raised by the researchers on the above techniques as well as the elements need to be considered upon selection have been discussed in this paper. The main objective of the study is to highlight the important role played by these inventory control models in the food and beverage processing industry.
The grounding of higher order concepts in action and language: a cognitive robotics model.
Stramandinoli, Francesca; Marocco, Davide; Cangelosi, Angelo
2012-08-01
In this paper we present a neuro-robotic model that uses artificial neural networks for investigating the relations between the development of symbol manipulation capabilities and of sensorimotor knowledge in the humanoid robot iCub. We describe a cognitive robotics model in which the linguistic input provided by the experimenter guides the autonomous organization of the robot's knowledge. In this model, sequences of linguistic inputs lead to the development of higher-order concepts grounded on basic concepts and actions. In particular, we show that higher-order symbolic representations can be indirectly grounded in action primitives directly grounded in sensorimotor experiences. The use of recurrent neural network also permits the learning of higher-order concepts based on temporal sequences of action primitives. Hence, the meaning of a higher-order concept is obtained through the combination of basic sensorimotor knowledge. We argue that such a hierarchical organization of concepts can be a possible account for the acquisition of abstract words in cognitive robots. Copyright © 2012 Elsevier Ltd. All rights reserved.
Modelling of Two-Phase Flow with Second-Order Accurate Scheme
Tiselj, Iztok; Petelin, Stojan
1997-09-01
A second-order accurate scheme based on high-resolution shock-capturing methods was used with a typical two-phase flow model which is used in the computer codes for simulation of nuclear power plant accidents. The two-fluid model, which has been taken from the computer code RELAP5, consists of six first-order partial differential equations that represent 1D mass, momentum, and energy balances for vapour and liquid. The partial differential equations are ill-posed-nonhyperbolic. The hyperbolicity required by the presented numerical scheme was obtained in the practical range of the physical parameters by minor modification of the virtual mass term. No conservative form of the applied equations exists, therefore, instead of the Riemann solver, more basic averaging was used for the evaluation of the Jacobian matrix. The equations were solved using nonconservative and conservative basic variables. Since the source terms are stiff, they were integrated with time steps which were shorter than or equal to the convection time step. The sources were treated with Strang splitting to retain the second-order accuracy of the scheme. The numerical scheme has been used for the simulations of the two-phase shock tube problem and the Edwards pipe experiment. Results show the importance of the closure laws which have a crucial impact on the accuracy of two-fluid models. Advantages of the second-order accurate schemes are evident especially in the area of fast transients dominated by acoustic phenomena.
Degenerate limit thermodynamics beyond leading order for models of dense matter
Energy Technology Data Exchange (ETDEWEB)
Constantinou, Constantinos, E-mail: c.constantinou@fz-juelich.de [Institute for Advanced Simulation, Institut für Kernphysik, and Jülich Center for Hadron Physics, Forschungszentrum Jülich, D-52425 Jülich (Germany); Muccioli, Brian, E-mail: bm956810@ohio.edu [Department of Physics and Astronomy, Ohio University, Athens, OH 45701 (United States); Prakash, Madappa, E-mail: prakash@ohio.edu [Department of Physics and Astronomy, Ohio University, Athens, OH 45701 (United States); Lattimer, James M., E-mail: james.lattimer@stonybrook.edu [Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY 11794-3800 (United States)
2015-12-15
Analytical formulas for next-to-leading order temperature corrections to the thermal state variables of interacting nucleons in bulk matter are derived in the degenerate limit. The formalism developed is applicable to a wide class of non-relativistic and relativistic models of hot and dense matter currently used in nuclear physics and astrophysics (supernovae, proto-neutron stars and neutron star mergers) as well as in condensed matter physics. We consider the general case of arbitrary dimensionality of momentum space and an arbitrary degree of relativity (for relativistic models). For non-relativistic zero-range interactions, knowledge of the Landau effective mass suffices to compute next-to-leading order effects, but for finite-range interactions, momentum derivatives of the Landau effective mass function up to second order are required. Results from our analytical formulas are compared with the exact results for zero- and finite-range potential and relativistic mean-field theoretical models. In all cases, inclusion of next-to-leading order temperature effects substantially extends the ranges of partial degeneracy for which the analytical treatment remains valid. Effects of many-body correlations that deserve further investigation are highlighted.
Simplification of the Flux Function for a Higher-order Gas-kinetic Evolution Model
Zhou, Guangzhao; Liu, Feng
2016-01-01
The higher-order gas-kinetic scheme for solving the Navier-Stokes equations has been studied in recent years. In addition to the use of higher-order reconstruction techniques, many terms are used in the Taylor expansion of the gas distribution functions. Therefore, a large number of coefficients need to be determined in the calculation of the time evolution of the gas distribution function at cell interfaces. As a consequence, the higher-order flux function takes much more computational time than that of a second-order gas-kinetic scheme. This paper aims to simplify the evolution model by two steps. Firstly, the coefficients related to the higher-order spatial and temporal derivatives of a distribution function are redefined to reduce the computational cost. Secondly, based on the physical analysis, some terms can be removed without loss of accuracy. Through the simplifications, the computational efficiency of the higher-order scheme is increased significantly. In addition, a self-adaptive numerical viscosity...
Serial recall of colors: Two models of memory for serial order applied to continuous visual stimuli.
Peteranderl, Sonja; Oberauer, Klaus
2018-01-01
This study investigated the effects of serial position and temporal distinctiveness on serial recall of simple visual stimuli. Participants observed lists of five colors presented at varying, unpredictably ordered interitem intervals, and their task was to reproduce the colors in their order of presentation by selecting colors on a continuous-response scale. To control for the possibility of verbal labeling, articulatory suppression was required in one of two experimental sessions. The predictions were derived through simulation from two computational models of serial recall: SIMPLE represents the class of temporal-distinctiveness models, whereas SOB-CS represents event-based models. According to temporal-distinctiveness models, items that are temporally isolated within a list are recalled more accurately than items that are temporally crowded. In contrast, event-based models assume that the time intervals between items do not affect recall performance per se, although free time following an item can improve memory for that item because of extended time for the encoding. The experimental and the simulated data were fit to an interference measurement model to measure the tendency to confuse items with other items nearby on the list-the locality constraint-in people as well as in the models. The continuous-reproduction performance showed a pronounced primacy effect with no recency, as well as some evidence for transpositions obeying the locality constraint. Though not entirely conclusive, this evidence favors event-based models over a role for temporal distinctiveness. There was also a strong detrimental effect of articulatory suppression, suggesting that verbal codes can be used to support serial-order memory of simple visual stimuli.
DEFF Research Database (Denmark)
Tahavori, Maryamsadat; Shaker, Hamid Reza
A method for model reduction of dynamical systems with the second order structure is proposed in this paper. The proposed technique preserves the second order structure of the system, and also preserves the stability of the original systems. The method uses the controllability and observability...... gramians within the time interval to build the appropriate Petrov-Galerkin projection for dynamical systems within the time interval of interest. The bound on approximation error is also derived. The numerical results are compared with the counterparts from other techniques. The results confirm...
Machine learning of explicit order parameters: From the Ising model to SU(2) lattice gauge theory
Wetzel, Sebastian J.; Scherzer, Manuel
2017-11-01
We present a solution to the problem of interpreting neural networks classifying phases of matter. We devise a procedure for reconstructing the decision function of an artificial neural network as a simple function of the input, provided the decision function is sufficiently symmetric. In this case one can easily deduce the quantity by which the neural network classifies the input. The method is applied to the Ising model and SU(2) lattice gauge theory. In both systems we deduce the explicit expressions of the order parameters from the decision functions of the neural networks. We assume no prior knowledge about the Hamiltonian or the order parameters except Monte Carlo-sampled configurations.
Goldberg, David A.; Katz-Rogozhnikov, Dmitriy A.; Lu, Yingdong; Sharma, Mayank; Squillante, Mark S.
2012-01-01
Lost sales inventory models with large lead times, which arise in many practical settings, are notoriously difficult to optimize due to the curse of dimensionality. In this paper we show that when lead times are large, a very simple constant-order policy, first studied by Reiman (\\cite{Reiman04}), performs nearly optimally. The main insight of our work is that when the lead time is very large, such a significant amount of randomness is injected into the system between when an order for more i...
Dynamic Hedging Based on Fractional Order Stochastic Model with Memory Effect
Directory of Open Access Journals (Sweden)
Qing Li
2016-01-01
Full Text Available Many researchers have established various hedge models to get the optimal hedge ratio. However, most of the hedge models only discuss the discrete-time processes. In this paper, we construct the minimum variance model for the estimation of the optimal hedge ratio based on the stochastic differential equation. At the same time, also by considering memory effects, we establish the continuous-time hedge model with memory based on the fractional order stochastic differential equation driven by a fractional Brownian motion to estimate the optimal dynamic hedge ratio. In addition, we carry on the empirical analysis to examine the effectiveness of our proposed hedge models from both in-sample test and out-of-sample test.
A mathematical model for order splitting in a multiple supplier single-item inventory system
DEFF Research Database (Denmark)
Abginehchi, Soheil; Farahani, Reza Zanjirani; Rezapour, Shabnam
2013-01-01
The policy of simultaneously splitting replenishment orders among several suppliers has received considerable attention in the last few years and continues to attract the attention of researchers. In this paper, we develop a mathematical model which considers multiple-supplier single-item inventory...... systems. The item acquisition lead times of suppliers are random variables. Backorder is allowed and shortage cost is charged based on not only per unit in shortage but also per time unit. Continuous review (s,Q) policy has been assumed. When the inventory level depletes to a reorder level, the total......, procurement cost, inventory holding cost, and shortage cost, is minimized. We also conduct extensive numerical experiments to show the advantages of our model compared with the models in the literature. According to our extensive experiments, the model developed in this paper is the best model...
Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network
Yao, Weigang; Liou, Meng-Sing
2012-01-01
The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis
Beaulieu, Alexandre; Bossé, Dominick; Micheau, Philippe; Avoine, Olivier; Praud, Jean-Paul; Walti, Hervé
2012-02-01
This study presents a methodology for applying the forced-oscillation technique in total liquid ventilation. It mainly consists of applying sinusoidal volumetric excitation to the respiratory system, and determining the transfer function between the delivered flow rate and resulting airway pressure. The investigated frequency range was f ∈ [0.05, 4] Hz at a constant flow amplitude of 7.5 mL/s. The five parameters of a fractional order lung model, the existing "5-parameter constant-phase model," were identified based on measured impedance spectra. The identification method was validated in silico on computer-generated datasets and the overall process was validated in vitro on a simplified single-compartment mechanical lung model. In vivo data on ten newborn lambs suggested the appropriateness of a fractional-order compliance term to the mechanical impedance to describe the low-frequency behavior of the lung, but did not demonstrate the relevance of a fractional-order inertance term. Typical respiratory system frequency response is presented together with statistical data of the measured in vivo impedance model parameters. This information will be useful for both the design of a robust pressure controller for total liquid ventilators and the monitoring of the patient's respiratory parameters during total liquid ventilation treatment. © 2011 IEEE
Freed, Alan D.; Diethelm, Kai; Gray, Hugh R. (Technical Monitor)
2002-01-01
Fraction-order viscoelastic (FOV) material models have been proposed and studied in 1D since the 1930's, and were extended into three dimensions in the 1970's under the assumption of infinitesimal straining. It was not until 1997 that Drozdov introduced the first finite-strain FOV constitutive equations. In our presentation, we shall continue in this tradition by extending the standard, FOV, fluid and solid, material models introduced in 1971 by Caputo and Mainardi into 3D constitutive formula applicable for finite-strain analyses. To achieve this, we generalize both the convected and co-rotational derivatives of tensor fields to fractional order. This is accomplished by defining them first as body tensor fields and then mapping them into space as objective Cartesian tensor fields. Constitutive equations are constructed using both variants for fractional rate, and their responses are contrasted in simple shear. After five years of research and development, we now possess a basic suite of numerical tools necessary to study finite-strain FOV constitutive equations and their iterative refinement into a mature collection of material models. Numerical methods still need to be developed for efficiently solving fraction al-order integrals, derivatives, and differential equations in a finite element setting where such constitutive formulae would need to be solved at each Gauss point in each element of a finite model, which can number into the millions in today's analysis.
Yoon, Ji Won; Roberts, Stephen J; Dyson, Mathew; Gan, John Q
2011-09-01
This paper proposes an algorithm for adaptive, sequential classification in systems with unknown labeling errors, focusing on the biomedical application of Brain Computer Interfacing (BCI). The method is shown to be robust in the presence of label and sensor noise. We focus on the inference and prediction of target labels under a nonlinear and non-Gaussian model. In order to handle missing or erroneous labeling, we model observed labels as a noisy observation of a latent label set with multiple classes (≥ 2). Whilst this paper focuses on the method's application to BCI systems, the algorithm has the potential to be applied to many application domains in which sequential missing labels are to be imputed in the presence of uncertainty. This dynamic classification algorithm combines an Ordered Probit model and an Extended Kalman Filter (EKF). The EKF estimates the parameters of the Ordered Probit model sequentially with time. We test the performance of the classification approach by processing synthetic datasets and real experimental EEG signals with multiple classes (2, 3 and 4 labels) for a Brain Computer Interfacing (BCI) experiment. Copyright © 2011 Elsevier Ltd. All rights reserved.
Modelling time-varying effects in Cox model under order restrictions
Salanti, Georgia; Ulm, Kurt
2003-01-01
The violation of the proportional hazards assumption in Cox model occurs quite often in studies concerning solid tumours or leukaemia. Then the time varying coefficients model is its most popular extension used. The function f(t) that measures the time variation of a covariate, can be assessed through several smoothing techniques, such as cubic splines. However, for practical propose, it is more convenient to assess f(t) by a step function. The main drawback of this approach is the lack of s...
Reduced-order molecular-dynamics model for polystyrene by equivalent-structure coarse graining.
Srivastava, Anand; Ghosh, Somnath
2012-02-01
This paper develops a reduced-order equivalent-structure based model for polystyrene in a rigid body molecular dynamics framework. In general, a coarse-grained model for polymers is obtained by replacing a group of chemically connected atoms by an effective particle and deriving a coarse-grained interaction potential that reproduces the structure and dynamics at the desired length and time scale. In the current model, a detailed (~16 atoms) polystyrene monomer referred to as basic structural element (BSE) is replaced by an equivalent model with spherical backbone particles and an ellipsoidal particle that represents the styrene sidegroup. The governing principals of this homogenization is based on the mass, centroid, angular momentum, and energy equivalence between the detailed and the proposed reduced-order model. The bonded interactions parameters are readily obtained in the optimization of the equivalent structure from the detailed representation. The nonbonded interactions are treated separately. In order to capture the stereochemistry of the polystyrene molecule, an anisotropic biaxial nonbonded interaction potential function known as RE-squared (RE2) interaction has been used between pairs of ellipsoidal and/or spherical particles in the system. The required calibration of the nonbonded parameters is carried out by matching with the experimental density and the local structure using radial distribution function. This homogenization process scales up the modeling system size significantly as the higher frequency motions like -C-H- vibrations and sidegroup movements are suppressed. The accuracy of the model is established by comparing fine-scale simulation with explicit representations.
Uncertainty Aware Structural Topology Optimization Via a Stochastic Reduced Order Model Approach
Aguilo, Miguel A.; Warner, James E.
2017-01-01
This work presents a stochastic reduced order modeling strategy for the quantification and propagation of uncertainties in topology optimization. Uncertainty aware optimization problems can be computationally complex due to the substantial number of model evaluations that are necessary to accurately quantify and propagate uncertainties. This computational complexity is greatly magnified if a high-fidelity, physics-based numerical model is used for the topology optimization calculations. Stochastic reduced order model (SROM) methods are applied here to effectively 1) alleviate the prohibitive computational cost associated with an uncertainty aware topology optimization problem; and 2) quantify and propagate the inherent uncertainties due to design imperfections. A generic SROM framework that transforms the uncertainty aware, stochastic topology optimization problem into a deterministic optimization problem that relies only on independent calls to a deterministic numerical model is presented. This approach facilitates the use of existing optimization and modeling tools to accurately solve the uncertainty aware topology optimization problems in a fraction of the computational demand required by Monte Carlo methods. Finally, an example in structural topology optimization is presented to demonstrate the effectiveness of the proposed uncertainty aware structural topology optimization approach.
Nearest-Neighbor Repulsion and Competing Charge and Spin Order in the Extended Hubbard Model.
Bahman, Davoudi; Tremblay, A.-M. S.
2006-03-01
We generalize the Two-Particle Self-Consistent (TPSC) approach to study the extended Hubbard model where the nearest-neighbor interaction V is present in addition to the local interaction U. Our results are in good agreement with available Quantum Monte-Carlo results over the whole range of density n up to intermediate coupling. As a function of U, V and n we observe different kinds of charge and spin orders, like commensurate/incommensurate charge and spin density wave, phase separation, and ferromagnetic order. For attractive V superconductivity could exist in the regions where the other types of charge and spin orders do not dominate. Ref.: B. Davoudi and A.-M.S. Tremblay, cond-mat/0509707
An Online Method for Interpolating Linear Parametric Reduced-Order Models
Amsallem, David
2011-01-01
A two-step online method is proposed for interpolating projection-based linear parametric reduced-order models (ROMs) in order to construct a new ROM for a new set of parameter values. The first step of this method transforms each precomputed ROM into a consistent set of generalized coordinates. The second step interpolates the associated linear operators on their appropriate matrix manifold. Real-time performance is achieved by precomputing inner products between the reduced-order bases underlying the precomputed ROMs. The proposed method is illustrated by applications in mechanical and aeronautical engineering. In particular, its robustness is demonstrated by its ability to handle the case where the sampled parameter set values exhibit a mode veering phenomenon. © 2011 Society for Industrial and Applied Mathematics.
Low-order dynamical system model of a fully developed turbulent channel flow
Hamilton, Nicholas; Tutkun, Murat; Cal, Raúl Bayoán
2017-06-01
A reduced order model of a turbulent channel flow is composed from a direct numerical simulation database hosted at the Johns Hopkins University. Snapshot proper orthogonal decomposition (POD) is used to identify the Hilbert space from which the reduced order model is obtained, as the POD basis is defined to capture the optimal energy content by mode. The reduced order model is defined by coupling the evolution of the dynamic POD mode coefficients through their respective time derivative with a least-squares polynomial fit of terms up to third order. Parameters coupling the dynamics of the POD basis are defined in analog to those produced in the classical Galerkin projection. The resulting low-order dynamical system is tested for a range of basis modes demonstrating that the non-linear mode interactions do not lead to a monotonic decrease in error propagation. A basis of five POD modes accounts for 50% of the integrated turbulence kinetic energy but captures only the largest features of the turbulence in the channel flow and is not able to reflect the anticipated flow dynamics. Using five modes, the low-order model is unable to accurately reproduce Reynolds stresses, and the root-mean-square error of the predicted stresses is as great as 30%. Increasing the basis to 28 modes accounts for 90% of the kinetic energy and adds intermediate scales to the dynamical system. The difference between the time derivatives of the random coefficients associated with individual modes and their least-squares fit is amplified in the numerical integration leading to unstable long-time solutions. Periodic recalibration of the dynamical system is undertaken by limiting the integration time to the range of the sampled data and offering the dynamical system new initial conditions. Renewed initial conditions are found by pushing the mode coefficients in the end of the integration time toward a known point along the original trajectories identified through a least-squares projection. Under
First and second order semi-Markov chains for wind speed modeling
Prattico, F.; Petroni, F.; D'Amico, G.
2012-04-01
The increasing interest in renewable energy leads scientific research to find a better way to recover most of the available energy. Particularly, the maximum energy recoverable from wind is equal to 59.3% of that available (Betz law) at a specific pitch angle and when the ratio between the wind speed in output and in input is equal to 1/3. The pitch angle is the angle formed between the airfoil of the blade of the wind turbine and the wind direction. Old turbine and a lot of that actually marketed, in fact, have always the same invariant geometry of the airfoil. This causes that wind turbines will work with an efficiency that is lower than 59.3%. New generation wind turbines, instead, have a system to variate the pitch angle by rotating the blades. This system able the wind turbines to recover, at different wind speed, always the maximum energy, working in Betz limit at different speed ratios. A powerful system control of the pitch angle allows the wind turbine to recover better the energy in transient regime. A good stochastic model for wind speed is then needed to help both the optimization of turbine design and to assist the system control to predict the value of the wind speed to positioning the blades quickly and correctly. The possibility to have synthetic data of wind speed is a powerful instrument to assist designer to verify the structures of the wind turbines or to estimate the energy recoverable from a specific site. To generate synthetic data, Markov chains of first or higher order are often used [1,2,3]. In particular in [3] is presented a comparison between a first-order Markov chain and a second-order Markov chain. A similar work, but only for the first-order Markov chain, is conduced by [2], presenting the probability transition matrix and comparing the energy spectral density and autocorrelation of real and synthetic wind speed data. A tentative to modeling and to join speed and direction of wind is presented in [1], by using two models, first-order
Nonthermal Melting of Néel Order in the Hubbard Model
Directory of Open Access Journals (Sweden)
Karsten Balzer
2015-09-01
Full Text Available We study the unitary time evolution of antiferromagnetic order in the Hubbard model after a quench starting from the perfect Néel state. In this setup, which is well suited for experiments with cold atoms, one can distinguish fundamentally different pathways for melting of long-range order at weak and strong interaction. In the Mott insulating regime, melting of long-range order occurs due to the ultrafast transfer of energy from charge excitations to the spin background, while local magnetic moments and their exchange coupling persist during the process. The latter can be demonstrated by a local spin-precession experiment. At weak interaction, local moments decay along with the long-range order. The dynamics is governed by residual quasiparticles, which are reflected in oscillations of the off-diagonal components of the momentum distribution. Such oscillations provide an alternative route to study the prethermalization phenomenon and its influence on the dynamics away from the integrable (noninteracting limit. The Hubbard model is solved within nonequilibrium dynamical mean-field theory, using the density-matrix renormalization group as an impurity solver.
Pin-wheel hexagons: a model for anthraquinone ordering on Cu(111).
Simenas, M; Tornau, E E
2013-10-21
The 4-state model of anthraquinone molecules ordering in a pin-wheel large-pore honeycomb phase on Cu(111) is proposed and solved by Monte Carlo simulation. The model is defined on a rescaled triangular lattice with the lattice constant a being equal to intermolecular distance in the honeycomb phase. The pin-wheel triangle formations are obtained taking into account the elongated shape of the molecules and anisotropic interactions for main two attractive short range (double and single dimeric) H-bond interactions. The long-range intermolecular interactions, corresponding to repulsive dipole-dipole forces, are assumed to be isotropic. Also, a very small (compared to short-range forces) isotropic attractive long-range interaction at the "characteristic" distance of a pore diameter is employed, and its effect carefully studied. This interaction is crucial for a formation of closed porous ordered systems, pin-wheel hexagons in particular. If each side of a pin-wheel hexagon is formed of n parallel molecules, the distance of this characteristic interaction is a√(3n(2)+1). The phase diagrams including different pin-wheel hexagon phases and a variety of other ordered structures are obtained. By changing the distance of characteristic interaction, different ordering routes into the experimental pin-wheel honeycomb phase are explored. The results obtained imply that classical explanation of the origin of the pin-wheel honeycomb phase in terms of some balance of attractive and repulsive forces cannot be totally discounted yet.
Low-Order Modeling of Dynamic Stall on Airfoils in Incompressible Flow
Narsipur, Shreyas
Unsteady aerodynamics has been a topic of research since the late 1930's and has increased in popularity among researchers studying dynamic stall in helicopters, insect/bird flight, micro air vehicles, wind-turbine aerodynamics, and ow-energy harvesting devices. Several experimental and computational studies have helped researchers gain a good understanding of the unsteady ow phenomena, but have proved to be expensive and time-intensive for rapid design and analysis purposes. Since the early 1970's, the push to develop low-order models to solve unsteady ow problems has resulted in several semi-empirical models capable of effectively analyzing unsteady aerodynamics in a fraction of the time required by high-order methods. However, due to the various complexities associated with time-dependent flows, several empirical constants and curve fits derived from existing experimental and computational results are required by the semi-empirical models to be an effective analysis tool. The aim of the current work is to develop a low-order model capable of simulating incompressible dynamic-stall type ow problems with a focus on accurately modeling the unsteady ow physics with the aim of reducing empirical dependencies. The lumped-vortex-element (LVE) algorithm is used as the baseline unsteady inviscid model to which augmentations are applied to model unsteady viscous effects. The current research is divided into two phases. The first phase focused on augmentations aimed at modeling pure unsteady trailing-edge boundary-layer separation and stall without leading-edge vortex (LEV) formation. The second phase is targeted at including LEV shedding capabilities to the LVE algorithm and combining with the trailing-edge separation model from phase one to realize a holistic, optimized, and robust low-order dynamic stall model. In phase one, initial augmentations to theory were focused on modeling the effects of steady trailing-edge separation by implementing a non-linear decambering
Low-order aeroelastic models of wind turbines for controller design
DEFF Research Database (Denmark)
Sønderby, Ivan Bergquist
by modal truncation by using the aeroelastic mode shapes of a fully flexible wind turbine. To capture the effect of shed vorticity and dynamic stall, a relatively large number of aerodynamically dominated modes are required, due to the assumption of independent annular flow tubes in the Blade Element....... The thesis contains a characterization of the dynamics that influence the open-loop aeroelastic frequency response of a modern wind turbine, based on a high-order aeroelastic wind turbine model. One main finding is that the transfer function from collective pitch to generator speed is affected by two low...... Momentum theory (BEM). A set of accurate reduced-order models are subsequently designed assuming quasi-steady aerodynamics, by truncation with a set of low-frequency mode shapes. In a comparison, the balanced truncation method is found to be able to capture the effect of the shed vorticity and dynamic...
Directory of Open Access Journals (Sweden)
Wolfgang Witteveen
2014-01-01
Full Text Available The mechanical response of multilayer sheet structures, such as leaf springs or car bodies, is largely determined by the nonlinear contact and friction forces between the sheets involved. Conventional computational approaches based on classical reduction techniques or the direct finite element approach have an inefficient balance between computational time and accuracy. In the present contribution, the method of trial vector derivatives is applied and extended in order to obtain a-priori trial vectors for the model reduction which are suitable for determining the nonlinearities in the joints of the reduced system. Findings show that the result quality in terms of displacements and contact forces is comparable to the direct finite element method but the computational effort is extremely low due to the model order reduction. Two numerical studies are presented to underline the method’s accuracy and efficiency. In conclusion, this approach is discussed with respect to the existing body of literature.
Coexistence of two vector order parameters: a holographic model for ferromagnetic superconductivity
Energy Technology Data Exchange (ETDEWEB)
Amoretti, Andrea [Dipartimento di Fisica, Università di Genova, and I.N.F.N. - Sezione di Genova, via Dodecaneso 33, 16146, Genova (Italy); Braggio, Alessandro [CNR-SPIN, via Dodecaneso 33, 16146, Genova (Italy); Maggiore, Nicola; Magnoli, Nicodemo [Dipartimento di Fisica, Università di Genova, and I.N.F.N. - Sezione di Genova, via Dodecaneso 33, 16146, Genova (Italy); Musso, Daniele [Physique Théorique et Mathématique, Université Libre de Bruxelles, C.P. 231, 1050 Bruxelles (Belgium)
2014-01-13
We study a generalization of the standard holographic p-wave superconductor featuring two interacting vector order parameters. Basing our argument on the symmetry and linear response properties of the model, we propose it as a holographic effective theory describing a strongly coupled ferromagnetic superconductor. We show that the two order parameters undergo concomitant condensations as a manifestation of an intrinsically interlaced charge/spin dynamics. Such intertwined dynamics is confirmed by the study of the transport properties. We characterize thoroughly the equilibrium and the linear response (i.e. optical conductivity and spin susceptibility) of the model at hand by means of a probe approximation analysis. Some insight about the effects of backreaction in the normal phase can be gained by analogy with the s-wave unbalanced holographic superconductor.
Modeling shear-induced particle ordering and deformation in a dense soft particle suspension.
Liao, Chih-Tang; Wu, Yi-Fan; Chien, Wei; Huang, Jung-Ren; Chen, Yeng-Long
2017-11-01
We apply the lattice Boltzmann method and the bead-spring network model of deformable particles (DPs) to study shear-induced particle ordering and deformation and the corresponding rheological behavior for dense DP suspensions confined in a narrow gap under steady external shear. The particle configuration is characterized with small-angle scattering intensity, the real-space 2D local order parameter, and the particle shape factors including deformation, stretching and tilt angles. We investigate how particle ordering and deformation vary with the particle volume fraction ϕ (=0.45-0.65) and the external shear rate characterized with the capillary number Ca (=0.003-0.191). The degree of particle deformation increases mildly with ϕ but significantly with Ca. Under moderate shear rate (Ca = 0.105), the inter-particle structure evolves from string-like ordering to layered hexagonal close packing (HCP) as ϕ increases. A long wavelength particle slithering motion emerges for sufficiently large ϕ. For ϕ = 0.61, the structure maintains layered HCP for Ca = 0.031-0.143 but gradually becomes disordered for larger and smaller Ca. The correlation in particle zigzag movements depends sensitively on ϕ and particle ordering. Layer-by-layer analysis reveals how the non-slippery hard walls affect particle ordering and deformation. The shear-induced reconfiguration of DPs observed in the simulation agrees qualitatively with experimental results of sheared uniform emulsions. The apparent suspension viscosity increases with ϕ but exhibits much weaker dependence compared to hard-sphere suspensions, indicating that particle deformation and unjamming under shear can significantly reduce the viscous stress. Furthermore, the suspension shear-thins, corresponding to increased inter-DP ordering and particle deformation with Ca. This work provides useful insights into the microstructure-rheology relationship of concentrated deformable particle suspensions.
Modeling shear-induced particle ordering and deformation in a dense soft particle suspension
Liao, Chih-Tang; Wu, Yi-Fan; Chien, Wei; Huang, Jung-Ren; Chen, Yeng-Long
2017-11-01
We apply the lattice Boltzmann method and the bead-spring network model of deformable particles (DPs) to study shear-induced particle ordering and deformation and the corresponding rheological behavior for dense DP suspensions confined in a narrow gap under steady external shear. The particle configuration is characterized with small-angle scattering intensity, the real-space 2D local order parameter, and the particle shape factors including deformation, stretching and tilt angles. We investigate how particle ordering and deformation vary with the particle volume fraction ϕ (=0.45-0.65) and the external shear rate characterized with the capillary number Ca (=0.003-0.191). The degree of particle deformation increases mildly with ϕ but significantly with Ca. Under moderate shear rate (Ca = 0.105), the inter-particle structure evolves from string-like ordering to layered hexagonal close packing (HCP) as ϕ increases. A long wavelength particle slithering motion emerges for sufficiently large ϕ. For ϕ = 0.61, the structure maintains layered HCP for Ca = 0.031-0.143 but gradually becomes disordered for larger and smaller Ca. The correlation in particle zigzag movements depends sensitively on ϕ and particle ordering. Layer-by-layer analysis reveals how the non-slippery hard walls affect particle ordering and deformation. The shear-induced reconfiguration of DPs observed in the simulation agrees qualitatively with experimental results of sheared uniform emulsions. The apparent suspension viscosity increases with ϕ but exhibits much weaker dependence compared to hard-sphere suspensions, indicating that particle deformation and unjamming under shear can significantly reduce the viscous stress. Furthermore, the suspension shear-thins, corresponding to increased inter-DP ordering and particle deformation with Ca. This work provides useful insights into the microstructure-rheology relationship of concentrated deformable particle suspensions.
A Novel Approach to Robust Motion Control of Electrical Drives with Model order Uncertainty
Directory of Open Access Journals (Sweden)
Stephen J Dodds
2004-01-01
Full Text Available A novel approach to the control of plants with model order uncertainty as well as parametric errors and externaldisturbances is presented, which yields a specified settling time of the step response with zero overshoot. The method is applied to amotion control system employing a permanent magnet synchronous motor. A single controller is designed to cater for mechanicalloads that may exhibit significant vibration modes. The order of the complete controlled system (i.e., the plant will therefore dependon the number of significant vibration modes. The controller is of the cascade structure, comprising an inner drive speed control loopand an outer position control loop. The main contribution of the paper is a completely new robust control strategy for plants withmodel order uncertainty, which is used in the outer position control loop. Its foundations lie in sliding mode control, but the set ofoutput derivatives fed back extend to a maximum order depending on the maximum likely rank of the plant, rather than its knownrank. In cases where the maximum order of output derivative exceeds the plant rank, in theory, virtual states are created that raise theorder of the closed-loop system while retaining the extreme robustness properties of sliding mode control.
Lagrangian perturbations and the matter bispectrum I: fourth-order model for non-linear clustering
Energy Technology Data Exchange (ETDEWEB)
Rampf, Cornelius [Institut für Theoretische Teilchenphysik und Kosmologie, RWTH Aachen, Physikzentrum RWTH-Melaten, D-52056 Aachen (Germany); Buchert, Thomas, E-mail: rampf@physik.rwth-aachen.de, E-mail: buchert@obs.univ-lyon1.fr [Université de Lyon, Observatoire de Lyon, Centre de Recherche Astrophysique de Lyon, CNRS UMR 5574: Université Lyon 1 and École Normale Supérieure de Lyon, 9 avenue Charles André, F-69230 Saint-Genis-Laval (France)
2012-06-01
We investigate the Lagrangian perturbation theory of a homogeneous and isotropic universe in the non-relativistic limit, and derive the solutions up to the fourth order. These solutions are needed for example for the next-to-leading order correction of the (resummed) Lagrangian matter bispectrum, which we study in an accompanying paper. We focus on flat cosmologies with a vanishing cosmological constant, and provide an in-depth description of two complementary approaches used in the current literature. Both approaches are solved with two different sets of initial conditions — both appropriate for modelling the large-scale structure. Afterwards we consider only the fastest growing mode solution, which is not affected by either of these choices of initial conditions. Under the reasonable approximation that the linear density contrast is evaluated at the initial Lagrangian position of the fluid particle, we obtain the nth-order displacement field in the so-called initial position limit: the nth order displacement field consists of 3(n-1) integrals over n linear density contrasts, and obeys self-similarity. Then, we find exact relations between the series in Lagrangian and Eulerian perturbation theory, leading to identical predictions for the density contrast and the peculiar-velocity divergence up to the fourth order.
Orbital nematic order and interplay with magnetism in the two-orbital Hubbard model.
Wang, Zhentao; Nevidomskyy, Andriy H
2015-06-10
Motivated by the recent angle-resolved photoemission spectroscopy (ARPES) on FeSe and iron pnictide families of iron-based superconductors, we have studied the orbital nematic order and its interplay with antiferromagnetism within the two-orbital Hubbard model. We used random phase approximation (RPA) to calculate the dependence of the orbital and magnetic susceptibilities on the strength of interactions and electron density (doping). To account for strong electron correlations not captured by RPA, we further employed non-perturbative variational cluster approximation (VCA) capable of capturing symmetry broken magnetic and orbitally ordered phases. Both approaches show that the electron and hole doping affect the two orders differently. While hole doping tends to suppress both magnetism and orbital ordering, the electron doping suppresses magnetism faster. Crucially, we find a realistic parameter regime for moderate electron doping that stabilizes orbital nematicity in the absence of long-range antiferromagnetic order. This is reminiscent of the non-magnetic orbital nematic phase observed recently in FeSe and a number of iron pnictide materials and raises the possibility that at least in some cases, the observed electronic nematicity may be primarily due to orbital rather than magnetic fluctuations.
The Nordic Model in a Global Company Situated in Norway. Challenging Institutional Orders?
Directory of Open Access Journals (Sweden)
Elin Kvande
2012-11-01
Full Text Available In this article, we explore the impact of internationalization as organizational processes where institutional actors meet in local contexts and negotiate the institutional order. The internationalization of working life implies that different traditions and practices meet and challenge each other. The focus is on how important elements of the Nordic micro model like cooperation between employees and employers and regulation of working hours are implemented in a global company situated in Norway. In general, it seems that employees and employers cooperate in line with this tradition in the Nordic micro model. Norwegian manager’s practices are described to be in accordance with Scandinavian management traditions, while managers from the United States appear to practice management consistent with the liberal working life model. The findings show a tension-filled clash between two different management practices, which indicates that the Nordic micro model in this field might be under pressure. Manager’s recommendation to the employees was not to become members of the trade union. The absence of trade unions in the organization implies that employees and employers are not cooperating on a collective level. This means that only parts of the regulatory arrangement related to participation and cooperation are implemented. Findings concerning working time and the relation to the institutional order represented by the Norwegian Work Environment Act indicate a clear tension between different institutional traditions in the organization. The company does not respect the Norwegian in working time regulations. These regulations are seen as counterproductive for a company that competes in the international market. This devaluation of the regulations in the Nordic model implies that the institutional order represented in the Nordic micro model is challenged.
Koopman Mode Decomposition Methods in Dynamic Stall: Reduced Order Modeling and Control
2015-11-10
Koopman Mode Decomposition Methods in Dynamic Stall : Reduced Order Modeling and Control During dynamic stall , large peaks in lift, pitching moment and...drag appear, and these cause an undesirable increase in the mean drag. Dynamic stall can also lead to potentially fatal structural loads due to...strong vibrations of flexible aerodynamic surfaces. Despite extensive analytical, numerical, and experimental efforts to study dynamic stall , progress is
BAYESIAN ANALYSIS FOR THE PAIRED COMPARISON MODEL WITH ORDER EFFECTS (USING NON-INFORMATIVE PRIORS
Directory of Open Access Journals (Sweden)
Ghausia Masood Gilani
2008-07-01
Full Text Available Sometimes it may be difficult for a panelist to rank or compare more than two objects or treatments at the same time. For this reason, paired comparison method is used. In this study, the Davidson and Beaver (1977 model for paired comparisons with order effects is analyzed through the Bayesian Approach. For this purpose, the posterior means and the posterior modes are compared using the noninformative priors.
Nonlinear wave-structure interactions with a high-order Boussinesq model
DEFF Research Database (Denmark)
Fuhrman, David R.; Bingham, Harry; Madsen, Per A.
2005-01-01
on a structurally divided domain, and it is shown that exterior corner points pose potential stability problems, as well as other numerical difficulties. These are mainly due to the discretization of high-order mixed-derivative terms near these points, where the flow is theoretically singular. Fortunately......, and highly nonlinear deep water wave run-up on a vertical plate. These cases demonstrate the applicability of the model over a wide range of water depth and nonlinearity....
Solution of Second-Order IVP and BVP of Matrix Differential Models Using Matrix DTM
Directory of Open Access Journals (Sweden)
Reza Abazari
2012-01-01
Full Text Available We introduce a matrix form of differential transformation method (DTM and apply for nonlinear second-order initial value problems (IVPs and boundary value problems (BVPs of matrix models which are given by (=(,(,( and subject to initial conditions (=0,(=1 and boundary conditions (=0,(=1, where 0,1∈×. Also the convergence of present method is established. Several illustrative examples are given to demonstrate the effectiveness of the present method.
Reduced order models for thermal analysis : final report : LDRD Project No. 137807.
Energy Technology Data Exchange (ETDEWEB)
Hogan, Roy E., Jr.; Gartling, David K.
2010-09-01
This LDRD Senior's Council Project is focused on the development, implementation and evaluation of Reduced Order Models (ROM) for application in the thermal analysis of complex engineering problems. Two basic approaches to developing a ROM for combined thermal conduction and enclosure radiation problems are considered. As a prerequisite to a ROM a fully coupled solution method for conduction/radiation models is required; a parallel implementation is explored for this class of problems. High-fidelity models of large, complex systems are now used routinely to verify design and performance. However, there are applications where the high-fidelity model is too large to be used repetitively in a design mode. One such application is the design of a control system that oversees the functioning of the complex, high-fidelity model. Examples include control systems for manufacturing processes such as brazing and annealing furnaces as well as control systems for the thermal management of optical systems. A reduced order model (ROM) seeks to reduce the number of degrees of freedom needed to represent the overall behavior of the large system without a significant loss in accuracy. The reduction in the number of degrees of freedom of the ROM leads to immediate increases in computational efficiency and allows many design parameters and perturbations to be quickly and effectively evaluated. Reduced order models are routinely used in solid mechanics where techniques such as modal analysis have reached a high state of refinement. Similar techniques have recently been applied in standard thermal conduction problems e.g. though the general use of ROM for heat transfer is not yet widespread. One major difficulty with the development of ROM for general thermal analysis is the need to include the very nonlinear effects of enclosure radiation in many applications. Many ROM methods have considered only linear or mildly nonlinear problems. In the present study a reduced order model is
Team Resilience as a Second-Order Emergent State: A Theoretical Model and Research Directions
Bowers, Clint; Kreutzer, Christine; Cannon-Bowers, Janis; Lamb, Jerry
2017-01-01
Resilience has been recognized as an important phenomenon for understanding how individuals overcome difficult situations. However, it is not only individuals who face difficulties; it is not uncommon for teams to experience adversity. When they do, they must be able to overcome these challenges without performance decrements.This manuscript represents a theoretical model that might be helpful in conceptualizing this important construct. Specifically, it describes team resilience as a second-order emergent state. We also include research propositions that follow from the model. PMID:28861013
Directory of Open Access Journals (Sweden)
Z. Kolka
1999-04-01
Full Text Available The elementary canonical state models of the third-order autonomous dynamical systems, topologically conjugate to Chua's circuit family, are generalized for any continuous and odd symmetrical piecewise-linear (PWL feedback function. Their state equations are in accordance with the basic form of the Lur'e systems and the corresponding circuit model contains the multiple PWL feedback. The general results are applied for the simplest three-region case defined by three sets of the equivalent eigenvalue parameters. The application of these results is demonstrated on the double-scroll chaotic attractor with global attracting properties. As an example its utilization in synchronized chaos is shown.
1st Order Modeling of a SAW Delay Line using MathCAD(Registered)
Wilson, William C.; Atkinson, Gary M.
2007-01-01
To aid in the development of SAW sensors for Integrated Vehicle Health Monitoring applications, a first order model of a SAW Delay line has been created using MathCadA. The model implements the Impulse Response method to calculate the frequency response, impedance, and insertion loss. This paper presents the model and the results from the model for a SAW delay line design. Integrated Vehicle Health Monitoring (IVHM) of aerospace vehicles requires rugged sensors having reduced volume, mass, and power that can be used to measure a variety of phenomena. Wireless systems are preferred when retro-fitting sensors onto existing vehicles [1]. Surface Acoustic Wave (SAW) devices are capable of sensing: temperature, pressure, strain, chemical species, mass loading, acceleration, and shear stress. SAW technology is low cost, rugged, lightweight, and extremely low power. Passive wireless sensors have been developed using SAW technology. For these reasons new SAW sensors are being investigated for aerospace applications.
New algorithm for mode shape estimation based on ambient signals considering model order selection
Wu, Chao; Lu, Chao; Han, Yingduo
2013-12-01
Using time-synchronized phasor measurements, a new signal processing approach for estimating the electromechanical mode shape properties from ambient signals is proposed. In this method, Bayesian information criterion and the ARMA(2 n,2 n - 1) modeling procedure are first used to automatically select the optimal model order, and the auto regressive moving averaging models are built based on ambient data, then the low-frequency oscillation modal frequency and damping ratio are identified. Next, Prony models of ambient signals are presented, and the mode shape information of multiple dominant interarea oscillation modes are simultaneously estimated. The advantages of the new ARMA-P method are demonstrated by its applications in both a simulation system and measured data from China Southern Power Grid.
Active Contour Model Coupling with Higher Order Diffusion for Medical Image Segmentation
Directory of Open Access Journals (Sweden)
Guodong Wang
2014-01-01
Full Text Available Active contour models are very popular in image segmentation. Different features such as mean gray and variance are selected for different purpose. But for image with intensity inhomogeneities, there are no features for segmentation using the active contour model. The images with intensity inhomogeneities often occurred in real world especially in medical images. To deal with the difficulties raised in image segmentation with intensity inhomogeneities, a new active contour model with higher-order diffusion method is proposed. With the addition of gradient and Laplace information, the active contour model can converge to the edge of the image even with the intensity inhomogeneities. Because of the introduction of Laplace information, the difference scheme becomes more difficult. To enhance the efficiency of the segmentation, the fast Split Bregman algorithm is designed for the segmentation implementation. The performance of our method is demonstrated through numerical experiments of some medical image segmentations with intensity inhomogeneities.
Low-Order Modeling for Unsteady Separated Compressible Flows by POD-Galerkin Approach
Bourguet, R.; Braza, M.; Harran, G.; Dervieux, A.
A low-dimensional model is developed on the basis of the unsteady compressible Navier-Stokes equations by means of POD-Galerkin methodology in the perspective of physical analysis and computational savings. This approach consists in projecting the complex physical model onto a subspace determined to reach an optimal statistical content conservation. This leads to a drastic reduction of the number of degrees of freedom while preserving the main flow dynamics. The high-order system formulation is modified and an inner product which couples the contributions of both kinematic and thermodynamic state variables is selected. The associated reduced order model is a quadratic polynomial ordinary differential equation system which presents an inherent sensitivity to POD basis truncation for long-term prediction. A calibration process based on the minimisation of the prediction error with respect to reference dynamics is implemented. The predictive capacities of the low-order approach are evaluated by comparison with results issued from the 2D Navier-Stokes simulation of a transonic flow around a NACA0012 airfoil, at zero angle of incidence. This configuration is characterised by a complex unsteadiness caused by a von Kármán instability mode induced by shock/vortex interaction, and a low frequency buffeting mode.
Directory of Open Access Journals (Sweden)
I.Stasyuk
2003-01-01
Full Text Available A microscopic model based on the consideration of the proton ordering is proposed for describing the H-bonded ferroelectric crystalline systems with a complex structure of the hydrogen bond network. The model has been used for the investigation of thermodynamics and dielectric properties of the GPI crystal. The symmetry analysis of the order parameters responsible for the mixed (ferro- and antiferroelectric nature of ordering is performed within the model. The phase transition into the ferroelectric state is described. Changes in the dielectric susceptibility of the crystal are studied in the presence of the transverse external electric field acting along the c-axis. The results of measurements of temperature and field dependences of dielectric permittivity εc' in the paraelectric phase are presented. The microscopic mechanism of the observed effects is discussed based on the comparison of theoretical results and experimental data. A conclusion is made about the significant role of the ionic groups connected by hydrogen bonds in the charge transfer. So they make an important contribution into the polarizability of the GPI crystal along the direction of H-bonded chains.
On the Entropy Based Associative Memory Model with Higher-Order Correlations
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Masahiro Nakagawa
2010-01-01
Full Text Available In this paper, an entropy based associative memory model will be proposed and applied to memory retrievals with an orthogonal learning model so as to compare with the conventional model based on the quadratic Lyapunov functional to be minimized during the retrieval process. In the present approach, the updating dynamics will be constructed on the basis of the entropy minimization strategy which may be reduced asymptotically to the above-mentioned conventional dynamics as a special case ignoring the higher-order correlations. According to the introduction of the entropy functional, one may involve higer-order correlation effects between neurons in a self-contained manner without any heuristic coupling coefficients as in the conventional manner. In fact we shall show such higher order coupling tensors are to be uniquely determined in the framework of the entropy based approach. From numerical results, it will be found that the presently proposed novel approach realizes much larger memory capacity than that of the quadratic Lyapunov functional approach, e.g., associatron.
Coarse-graining for fast dynamics of order parameters in the phase-field model
Jou, D.; Galenko, P. K.
2018-01-01
In standard descriptions, the master equation can be obtained by coarse-graining with the application of the hypothesis of full local thermalization that is equivalent to the local thermodynamic equilibrium. By contrast, fast transformations proceed in the absence of local equilibrium and the master equation must be obtained with the absence of thermalization. In the present work, a non-Markovian master equation leading, in specific cases of relaxation to local thermodynamic equilibrium, to hyperbolic evolution equations for a binary alloy, is derived for a system with two order parameters. One of them is a conserved order parameter related to the atomistic composition, and the other one is a non-conserved order parameter, which is related to phase field. A microscopic basis for phenomenological phase-field models of fast phase transitions, when the transition is so fast that there is not sufficient time to achieve local thermalization between two successive elementary processes in the system, is provided. In a particular case, when the relaxation to local thermalization proceeds by the exponential law, the obtained coarse-grained equations are related to the hyperbolic phase-field model. The solution of the model equations is obtained to demonstrate non-equilibrium phenomenon of solute trapping which appears in rapid growth of dendritic crystals. This article is part of the theme issue `From atomistic interfaces to dendritic patterns'.
HIGHLY-ACCURATE MODEL ORDER REDUCTION TECHNIQUE ON A DISCRETE DOMAIN
Directory of Open Access Journals (Sweden)
L. D. Ribeiro
2015-09-01
Full Text Available AbstractIn this work, we present a highly-accurate technique of model order reduction applied to staged processes. The proposed method reduces the dimension of the original system based on null values of moment-weighted sums of heat and mass balance residuals on real stages. To compute these sums of weighted residuals, a discrete form of Gauss-Lobatto quadrature was developed, allowing a high degree of accuracy in these calculations. The locations where the residuals are cancelled vary with time and operating conditions, characterizing a desirable adaptive nature of this technique. Balances related to upstream and downstream devices (such as condenser, reboiler, and feed tray of a distillation column are considered as boundary conditions of the corresponding difference-differential equations system. The chosen number of moments is the dimension of the reduced model being much lower than the dimension of the complete model and does not depend on the size of the original model. Scaling of the discrete independent variable related with the stages was crucial for the computational implementation of the proposed method, avoiding accumulation of round-off errors present even in low-degree polynomial approximations in the original discrete variable. Dynamical simulations of distillation columns were carried out to check the performance of the proposed model order reduction technique. The obtained results show the superiority of the proposed procedure in comparison with the orthogonal collocation method.
Factors Influencing the Incidence of Obesity in Australia: A Generalized Ordered Probit Model
Directory of Open Access Journals (Sweden)
Gulay Avsar
2017-02-01
Full Text Available The increasing health costs of and the risks factors associated with obesity are well documented. From this perspective, it is important that the propensity of individuals towards obesity is analyzed. This paper uses longitudinal data from the Household Income and Labour Dynamics in Australia (HILDA Survey for 2005 to 2010 to model those variables which condition the probability of being obese. The model estimated is a random effects generalized ordered probit, which exploits two sources of heterogeneity; the individual heterogeneity of panel data models and heterogeneity across body mass index (BMI categories. The latter is associated with non-parallel thresholds in the generalized ordered model, where the thresholds are functions of the conditioning variables, which comprise economic, social, and demographic and lifestyle variables. To control for potential predisposition to obesity, personality traits augment the empirical model. The results support the view that the probability of obesity is significantly determined by the conditioning variables. Particularly, personality is found to be important and these outcomes reinforce other work examining personality and obesity.
Factors Influencing the Incidence of Obesity in Australia: A Generalized Ordered Probit Model.
Avsar, Gulay; Ham, Roger; Tannous, W Kathy
2017-02-10
The increasing health costs of and the risks factors associated with obesity are well documented. From this perspective, it is important that the propensity of individuals towards obesity is analyzed. This paper uses longitudinal data from the Household Income and Labour Dynamics in Australia (HILDA) Survey for 2005 to 2010 to model those variables which condition the probability of being obese. The model estimated is a random effects generalized ordered probit, which exploits two sources of heterogeneity; the individual heterogeneity of panel data models and heterogeneity across body mass index (BMI) categories. The latter is associated with non-parallel thresholds in the generalized ordered model, where the thresholds are functions of the conditioning variables, which comprise economic, social, and demographic and lifestyle variables. To control for potential predisposition to obesity, personality traits augment the empirical model. The results support the view that the probability of obesity is significantly determined by the conditioning variables. Particularly, personality is found to be important and these outcomes reinforce other work examining personality and obesity.
Numerical study of Potts models with aperiodic modulations: influence on first-order transitions
Branco, Nilton; Girardi, Daniel
2012-02-01
We perform a numerical study of Potts models on a rectangular lattice with aperiodic interactions along one spatial direction. The number of states q is such that the transition is a first-order one for the uniform model. The Wolff algorithm is employed, for many lattice sizes, allowing for a finite-size scaling analyses to be carried out. Three different self-dual aperiodic sequences are employed, such that the exact critical temperature is known: this leads to precise results for the exponents. We analyze models with q=6 and 15 and show that the Harris-Luck criterion, originally introduced in the study of continuous transitions, is obeyed also for first-order ones. The new universality class that emerges for relevant aperiodic modulations depends on the number of states of the Potts model, as obtained elsewhere for random disorder, and on the aperiodic sequence. We determine the occurrence of log-periodic behavior, as expected for models with aperiodic modulated interactions.
Renormalization-group theory for cooling first-order phase transitions in Potts models.
Liang, Ning; Zhong, Fan
2017-03-01
We develop a dynamic field-theoretic renormalization-group (RG) theory for cooling first-order phase transitions in the Potts model. It is suggested that the well-known imaginary fixed points of the q-state Potts model for q>10/3 in the RG theory are the origin of the dynamic scaling found recently from numerical simulations, apart from logarithmic corrections. This indicates that the real and imaginary fixed points of the Potts model are both physical and control the scalings of the continuous and discontinuous phase transitions, respectively, of the model. Our one-loop results for the scaling exponents are already not far away from the numerical results. Further, the scaling exponents depend on q only slightly, consistent with the numerical results. Therefore, the theory is believed to provide a natural explanation of the dynamic scaling including the scaling exponents and their scaling laws for various observables in the cooling first-order phase transition of the Potts model.
Calibration of aero-structural reduced order models using full-field experimental measurements
Perez, R.; Bartram, G.; Beberniss, T.; Wiebe, R.; Spottswood, S. M.
2017-03-01
The structural response of hypersonic aircraft panels is a multi-disciplinary problem, where the nonlinear structural dynamics, aerodynamics, and heat transfer models are coupled. A clear understanding of the impact of high-speed flow effects on the structural response, and the potential influence of the structure on the local environment, is needed in order to prevent the design of overly-conservative structures, a common problem in past hypersonic programs. The current work investigates these challenges from a structures perspective. To this end, the first part of this investigation looks at the modeling of the response of a rectangular panel to an external heating source (thermo-structural coupling) where the temperature effect on the structure is obtained from forward looking infrared (FLIR) measurements and the displacement via 3D-digital image correlation (DIC). The second part of the study uses data from a previous series of wind-tunnel experiments, performed to investigate the response of a compliant panel to the effects of high-speed flow, to train a pressure surrogate model. In this case, the panel aero-loading is obtained from fast-response pressure sensitive paint (PSP) measurements, both directly and from the pressure surrogate model. The result of this investigation is the use of full-field experimental measurements to update the structural model and train a computational efficient model of the loading environment. The use of reduced order models, informed by these full-field physical measurements, is a significant step toward the development of accurate simulation models of complex structures that are computationally tractable.
A Polarimetric First-Order Model of Soil Moisture Effects on the DInSAR Coherence
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Simon Zwieback
2015-06-01
Full Text Available Changes in soil moisture between two radar acquisitions can impact the observed coherence in differential interferometry: both coherence magnitude |Υ| and phase Φ are affected. The influence on the latter potentially biases the estimation of deformations. These effects have been found to be variable in magnitude and sign, as well as dependent on polarization, as opposed to predictions by existing models. Such diversity can be explained when the soil is modelled as a half-space with spatially varying dielectric properties and a rough interface. The first-order perturbative solution achieves–upon calibration with airborne L band data–median correlations ρ at HH polarization of 0.77 for the phase Φ, of 0.50 for |Υ|, and for the phase triplets ≡ of 0.56. The predictions are sensitive to the choice of dielectric mixing model, in particular the absorptive properties; the differences between the mixing models are found to be partially compensatable by varying the relative importance of surface and volume scattering. However, for half of the agricultural fields the Hallikainen mixing model cannot reproduce the observed sensitivities of the phase to soil moisture. In addition, the first-order expansion does not predict any impact on the HV coherence, which is however empirically found to display similar sensitivities to soil moisture as the co-pol channels HH and VV. These results indicate that the first-order solution, while not able to reproduce all observed phenomena, can capture some of the more salient patterns of the effect of soil moisture changes on the HH and VV DInSAR signals. Hence it may prove useful in separating the deformations from the moisture signals, thus yielding improved displacement estimates or new ways for inferring soil moisture.
A semi-implicit, second-order-accurate numerical model for multiphase underexpanded volcanic jets
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S. Carcano
2013-11-01
Full Text Available An improved version of the PDAC (Pyroclastic Dispersal Analysis Code, Esposti Ongaro et al., 2007 numerical model for the simulation of multiphase volcanic flows is presented and validated for the simulation of multiphase volcanic jets in supersonic regimes. The present version of PDAC includes second-order time- and space discretizations and fully multidimensional advection discretizations in order to reduce numerical diffusion and enhance the accuracy of the original model. The model is tested on the problem of jet decompression in both two and three dimensions. For homogeneous jets, numerical results are consistent with experimental results at the laboratory scale (Lewis and Carlson, 1964. For nonequilibrium gas–particle jets, we consider monodisperse and bidisperse mixtures, and we quantify nonequilibrium effects in terms of the ratio between the particle relaxation time and a characteristic jet timescale. For coarse particles and low particle load, numerical simulations well reproduce laboratory experiments and numerical simulations carried out with an Eulerian–Lagrangian model (Sommerfeld, 1993. At the volcanic scale, we consider steady-state conditions associated with the development of Vulcanian and sub-Plinian eruptions. For the finest particles produced in these regimes, we demonstrate that the solid phase is in mechanical and thermal equilibrium with the gas phase and that the jet decompression structure is well described by a pseudogas model (Ogden et al., 2008. Coarse particles, on the other hand, display significant nonequilibrium effects, which associated with their larger relaxation time. Deviations from the equilibrium regime, with maximum velocity and temperature differences on the order of 150 m s−1 and 80 K across shock waves, occur especially during the rapid acceleration phases, and are able to modify substantially the jet dynamics with respect to the homogeneous case.
Jin, Lihua; Zeng, Zhi; Huo, Yongzhong
2010-11-01
Liquid crystal elastomer is a kind of anisotropic polymeric material, with complicated micro-structures and thermo-order-mechanical coupling behaviors. In this paper, we propose a method to systematically model these coupling behaviors. We derive the constitutive model in full tensor structure according to the Clausius-Duhem inequality. Two of the constitutive equations represent the mechanical equilibrium and the other two represent the phase equilibrium. Choosing the total free energy as the combination of the neo-classical free energy and the Landau-de Gennes nematic free energy, we obtain the Cauchy stress-deformation gradient relation and the order-mechanical coupling equations. We find the analytical homogeneous solutions of the deformation for the typical mechanical loadings, such as uniaxial stretch, and simple shear in any directions. We also compare the compression behavior of prolate liquid crystal elastomers with the stretch behavior of oblate liquid crystal elastomers. As a result, the stress, strain, temperature, order parameter, biaxiality and the direction of the director of liquid crystal elastomers couple with each other. When the prolate liquid crystal elastomer sample is stretched in the direction parallel to its director, the deviatoric stress makes the mesogens more order and increase the transition temperature. When the sample is sheared or stretched in the direction non-parallel to the director, the director of the liquid crystal elastomer will rotate, and the biaxiality will be induced. Because of the order-mechanical coupling, under infinitesimal deformation, liquid crystal elastomer has anisotropic Young's modulus and zero shear modulus in the direction parallel or perpendicular to the director. While for the oblate liquid crystal elastomers, the stretch parallel to the director will cause the rotation of the director and induce the biaxiality.
Automatic Black-Box Model Order Reduction using Radial Basis Functions
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Stephanson, M B; Lee, J F; White, D A
2011-07-15
Finite elements methods have long made use of model order reduction (MOR), particularly in the context of fast freqeucny sweeps. In this paper, we discuss a black-box MOR technique, applicable to a many solution methods and not restricted only to spectral responses. We also discuss automated methods for generating a reduced order model that meets a given error tolerance. Numerical examples demonstrate the effectiveness and wide applicability of the method. With the advent of improved computing hardware and numerous fast solution techniques, the field of computational electromagnetics are progressed rapidly in terms of the size and complexity of problems that can be solved. Numerous applications, however, require the solution of a problem for many different configurations, including optimization, parameter exploration, and uncertainly quantification, where the parameters that may be changed include frequency, material properties, geometric dimensions, etc. In such cases, thousands of solutions may be needed, so solve times of even a few minutes can be burdensome. Model order reduction (MOR) may alleviate this difficulty by creating a small model that can be evaluated quickly. Many MOR techniques have been applied to electromagnetic problems over the past few decades, particularly in the context of fast frequency sweeps. Recent works have extended these methods to allow more than one parameter and to allow the parameters to represent material and geometric properties. There are still limitations with these methods, however. First, they almost always assume that the finite element method is used to solve the problem, so that the system matrix is a known function of the parameters. Second, although some authors have presented adaptive methods (e.g., [2]), the order of the model is often determined before the MOR process begins, with little insight about what order is actually needed to reach the desired accuracy. Finally, it not clear how to efficiently extend most
Rostamian, Rahele; Behnejad, Hassan
2018-01-01
The adsorption behavior of tetracycline (TCN), doxycycline (DCN) as the most common antibiotics in veterinary and ciprofloxacin (CPN) onto graphene oxide nanosheets (GOS) in aqueous solution was evaluated. The four factors influencing the adsorption of antibiotics (initial concentration, pH, temperature and contact time) were studied. The results showed that initial pH ∼ 6 to 7 and contact time ∼ 100 - 200min are optimum for each drug. The monolayer adsorption capacity was reduced with the increasing temperature from 25°C to 45°C. Non-linear regressions were carried out in order to define the best fit model for every system. To do this, eight error functions were applied to predict the optimum model. Among various models, Hill and Toth isotherm models represented the equilibrium adsorption data of antibiotics while the kinetic data were well fitted by pseudo second-order (PSO) kinetic model (DCN and TCN) and Elovich (CPN) models. The maximum adsorption capacity (qmax) is found to be in the following order: CPN > DCN > TCN, obtained from sips equation at the same temperature. The GOS shows highest adsorption capacity towards CPN up to 173.4mgg-1. The study showed that GOS can be removed more efficiently from water solution. Copyright © 2017 Elsevier Inc. All rights reserved.
A Lagrangian photoresponse model coupled with 2nd-order turbulence closure
Nagai, T.; Yamazaki, H.; Kamykowski, D.
2004-12-01
Vertical mixing can transport nutrients from deep layer to euphotic zone. Therefore, it plays a very important role for biological productivity. Also, it can transport phytoplankton vertically and varies light exposure history of individual phytoplankton. When phytoplankton_fs response to the ambient light intensity is slow compare to the time scale of vertical mixing, production averaged in space can be varied caused by vertical mixing. In the past, such effects of vertical mixing on the photoresponse of phytoplankton have been considered with constant eddy diffusivity. However, vertical mixing is not independent of time, and variable vertical mixing in time should be examined for the Lagrangian photoresponse. In present study, a 2nd-order turbulence closure approach was coupled with a Lagrangian phytoplankton model, in order to examine the effect of time-dependent vertical eddy diffusion on the photoresponse of phytoplankton in a wind-driven upper mixing layer. In general, stronger wind mixing in a lower-transparency water column contributes to greater phytoplankton production. According to our study, vertical mixing is insignificant for photoinhibition in relatively clear open ocean water, while it can be more important in relatively turbid coastal water. A simple Ekman layer model provided surprisingly similar production to that observed with the 2nd-order closure scheme when the starting distribution of the phytoplankton cells was normalized. Two factors involved in the process are the change in the background stratification and the time dependence of the diffusivity coefficient. The influences of these 2 factors cancel each other to reduce the apparent difference between the total production estimated by the Ekman model compared to that estimated by the 2nd-order closure scheme.
Renormalization group theory for temperature-driven first-order phase transitions in scalar models
Liang, Ning; Zhong, Fan
2017-12-01
We study the scaling and universal behavior of temperature-driven first-order phase transitions in scalar models. These transitions are found to exhibit rich phenomena, though they are controlled by a single complex-conjugate pair of imaginary fixed points of ϕ 3 theory. Scaling theories and renormalization group theories are developed to account for the phenomena, and three universality classes with their own hysteresis exponents are found: a field-like thermal class, a partly thermal class, and a purely thermal class, designated, respectively, as Thermal Classes I, II, and III. The first two classes arise from the opposite limits of the scaling forms proposed and may cross over to each other depending on the temperature sweep rate. They are both described by a massless model and a purely massive model, both of which are equivalent and are derived from ϕ 3 theory via symmetry. Thermal Class III characterizes the cooling transitions in the absence of applied external fields and is described by purely thermal models, which include cases in which the order parameters possess different symmetries and thus exhibit different universality classes. For the purely thermal models whose free energies contain odd-symmetry terms, Thermal Class III emerges only at the mean-field level and is identical to Thermal Class II. Fluctuations change the model into the other two models. Using the extant three- and two-loop results for the static and dynamic exponents for the Yang-Lee edge singularity, respectively, which falls into the same universality class as ϕ 3 theory, we estimate the thermal hysteresis exponents of the various classes to the same precision. Comparisons with numerical results and experiments are briefly discussed.
Reduced-Order Biogeochemical Flux Model for High-Resolution Multi-Scale Biophysical Simulations
Smith, Katherine; Hamlington, Peter; Pinardi, Nadia; Zavatarelli, Marco
2017-04-01
Biogeochemical tracers and their interactions with upper ocean physical processes such as submesoscale circulations and small-scale turbulence are critical for understanding the role of the ocean in the global carbon cycle. These interactions can cause small-scale spatial and temporal heterogeneity in tracer distributions that can, in turn, greatly affect carbon exchange rates between the atmosphere and interior ocean. For this reason, it is important to take into account small-scale biophysical interactions when modeling the global carbon cycle. However, explicitly resolving these interactions in an earth system model (ESM) is currently infeasible due to the enormous associated computational cost. As a result, understanding and subsequently parameterizing how these small-scale heterogeneous distributions develop and how they relate to larger resolved scales is critical for obtaining improved predictions of carbon exchange rates in ESMs. In order to address this need, we have developed the reduced-order, 17 state variable Biogeochemical Flux Model (BFM-17) that follows the chemical functional group approach, which allows for non-Redfield stoichiometric ratios and the exchange of matter through units of carbon, nitrate, and phosphate. This model captures the behavior of open-ocean biogeochemical systems without substantially increasing computational cost, thus allowing the model to be combined with computationally-intensive, fully three-dimensional, non-hydrostatic large eddy simulations (LES). In this talk, we couple BFM-17 with the Princeton Ocean Model and show good agreement between predicted monthly-averaged results and Bermuda testbed area field data (including the Bermuda-Atlantic Time-series Study and Bermuda Testbed Mooring). Through these tests, we demonstrate the capability of BFM-17 to accurately model open-ocean biochemistry. Additionally, we discuss the use of BFM-17 within a multi-scale LES framework and outline how this will further our understanding
Marchesseau, S; Delingette, H; Sermesant, M; Cabrera-Lozoya, R; Tobon-Gomez, C; Moireau, P; Figueras i Ventura, R M; Lekadir, K; Hernandez, A; Garreau, M; Donal, E; Leclercq, C; Duckett, S G; Rhode, K; Rinaldi, C A; Frangi, A F; Razavi, R; Chapelle, D; Ayache, N
2013-10-01
Patient-specific cardiac modeling can help in understanding pathophysiology and therapy planning. However it requires to combine functional and anatomical data in order to build accurate models and to personalize the model geometry, kinematics, electrophysiology and mechanics. Personalizing the electromechanical coupling from medical images is a challenging task. We use the Bestel-Clément-Sorine (BCS) electromechanical model of the heart, which provides reasonable accuracy with a reasonable number of parameters (14 for each ventricle) compared to the available clinical data at the organ level. We propose a personalization strategy from cine MRI data in two steps. We first estimate global parameters with an automatic calibration algorithm based on the Unscented Transform which allows to initialize the parameters while matching the volume and pressure curves. In a second step we locally personalize the contractilities of all AHA (American Heart Association) zones of the left ventricle using the reduced order unscented Kalman filtering on Regional Volumes. This personalization strategy was validated synthetically and tested successfully on eight healthy and three pathological cases. Copyright © 2013 Elsevier B.V. All rights reserved.
A New Model of the Fractional Order Dynamics of the Planetary Gears
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Vera Nikolic-Stanojevic
2013-01-01
Full Text Available A theoretical model of planetary gears dynamics is presented. Planetary gears are parametrically excited by the time-varying mesh stiffness that fluctuates as the number of gear tooth pairs in contact changes during gear rotation. In the paper, it has been indicated that even the small disturbance in design realizations of this gear cause nonlinear properties of dynamics which are the source of vibrations and noise in the gear transmission. Dynamic model of the planetary gears with four degrees of freedom is used. Applying the basic principles of analytical mechanics and taking the initial and boundary conditions into consideration, it is possible to obtain the system of equations representing physical meshing process between the two or more gears. This investigation was focused to a new model of the fractional order dynamics of the planetary gear. For this model analytical expressions for the corresponding fractional order modes like one frequency eigen vibrational modes are obtained. For one planetary gear, eigen fractional modes are obtained, and a visualization is presented. By using MathCAD the solution is obtained.
First Versus Second Order Latent Growth Curve Models: Some Insights From Latent State-Trait Theory.
Geiser, Christian; Keller, Brian; Lockhart, Ginger
2013-07-01
First order latent growth curve models (FGMs) estimate change based on a single observed variable and are widely used in longitudinal research. Despite significant advantages, second order latent growth curve models (SGMs), which use multiple indicators, are rarely used in practice, and not all aspects of these models are widely understood. In this article, our goal is to contribute to a deeper understanding of theoretical and practical differences between FGMs and SGMs. We define the latent variables in FGMs and SGMs explicitly on the basis of latent state-trait (LST) theory and discuss insights that arise from this approach. We show that FGMs imply a strict trait-like conception of the construct under study, whereas SGMs allow for both trait and state components. Based on a simulation study and empirical applications to the CES-D depression scale (Radloff, 1977) we illustrate that, as an important practical consequence, FGMs yield biased reliability estimates whenever constructs contain state components, whereas reliability estimates based on SGMs were found to be accurate. Implications of the state-trait distinction for the measurement of change via latent growth curve models are discussed.
Diederich, Adele; Oswald, Peter
2014-01-01
A sequential sampling model for multiattribute binary choice options, called multiattribute attention switching (MAAS) model, assumes a separate sampling process for each attribute. During the deliberation process attention switches from one attribute consideration to the next. The order in which attributes are considered as well for how long each attribute is considered-the attention time-influences the predicted choice probabilities and choice response times. Several probability distributions for the attention time with different variances are investigated. Depending on the time and order schedule the model predicts a rich choice probability/choice response time pattern including preference reversals and fast errors. Furthermore, the difference between finite and infinite decision horizons for the attribute considered last is investigated. For the former case the model predicts a probability p 0 > 0 of not deciding within the available time. The underlying stochastic process for each attribute is an Ornstein-Uhlenbeck process approximated by a discrete birth-death process. All predictions are also true for the widely applied Wiener process.
New higher-order Godunov code for modelling performance of two-stage light gas guns
Bogdanoff, D. W.; Miller, R. J.
1995-01-01
A new quasi-one-dimensional Godunov code for modeling two-stage light gas guns is described. The code is third-order accurate in space and second-order accurate in time. A very accurate Riemann solver is used. Friction and heat transfer to the tube wall for gases and dense media are modeled and a simple nonequilibrium turbulence model is used for gas flows. The code also models gunpowder burn in the first-stage breech. Realistic equations of state (EOS) are used for all media. The code was validated against exact solutions of Riemann's shock-tube problem, impact of dense media slabs at velocities up to 20 km/sec, flow through a supersonic convergent-divergent nozzle and burning of gunpowder in a closed bomb. Excellent validation results were obtained. The code was then used to predict the performance of two light gas guns (1.5 in. and 0.28 in.) in service at the Ames Research Center. The code predictions were compared with measured pressure histories in the powder chamber and pump tube and with measured piston and projectile velocities. Very good agreement between computational fluid dynamics (CFD) predictions and measurements was obtained. Actual powder-burn rates in the gun were found to be considerably higher (60-90 percent) than predicted by the manufacturer and the behavior of the piston upon yielding appears to differ greatly from that suggested by low-strain rate tests.
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Adele eDiederich
2014-09-01
Full Text Available A sequential sampling model for multiattribute binary choice options, called Multiattribute attention switching (MAAS model, assumes a separate sampling process for each attribute. During the deliberation process attention switches from one attribute consideration to the next. The order in which attributes are considered as well for how long each attribute is considered - the attention time - influences the predicted choice probabilities and choice response times. Several probability distributions for the attention time including deterministic, Poisson, binomial, geometric, and uniform with different variances are investigated. Depending on the time and order schedule the model predicts a rich choice probability/choice response time pattern including preference reversals and fast errors. Furthermore, the difference between a finite and infinite decision horizons for the attribute considered last is investigated. For the former case the model predicts a probability $p_0> 0$ of not deciding within the available time. The underlying stochastic process for each attribute is an Ornstein-Uhlenbeck process approximated by a discrete birth-death process. All predictions are also true for the widely applied Wiener process.
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Monalisha Pattnaik
2014-09-01
Full Text Available Background: This model presents the effect of deteriorating items in fuzzy optimal instantaneous replenishment for finite planning horizon. Accounting for holding cost per unit per unit time and ordering cost per order have traditionally been the case of modeling inventory systems in fuzzy environment. These imprecise parameters defined on a bounded interval on the axis of real numbers and the physical characteristics of stocked items dictate the nature of inventory policies implemented to manage and control in the production system. Methods: The modified fuzzy EOQ (FEOQ model is introduced, it assumes that a percentage of the on-hand inventory is wasted due to deterioration and considered as an enhancement to EOQ model to determine the optimal replenishment quantity so that the net profit is maximized. In theoretical analysis, the necessary and sufficient conditions of the existence and uniqueness of the optimal solutions are proved and further the concavity of the fuzzy net profit function is established. Computational algorithm using the software LINGO 13.0 version is developed to find the optimal solution. Results and conclusions: The results of the numerical analysis enable decision-makers to quantify the effect of units lost due to deterioration on optimizing the fuzzy net profit for the retailer. Finally, sensitivity analyses of the optimal solution with respect the major parameters are also carried out. Furthermore fuzzy decision making is shown to be superior then crisp decision making in terms of profit maximization.
A note on inventory model for ameliorating items with time dependent second order demand rate
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Gobinda Chandra Panda
2013-03-01
Full Text Available Background: This paper is concerned with the development of ameliorating inventory models. The ameliorating inventory is the inventory of goods whose utility increases over the time by ameliorating activation. Material and Methods: This study is performed according to two areas: one is an economic order quantity (EOQ model for the items whose utility is ameliorating in accordance with Weibull distribution, and the other is a partial selling quantity (PSQ model developed for selling the surplus inventory accumulated by ameliorating activation with linear demand. The aim of this paper was to develop a mathematical model for inventory type concerned in the paper. Numerical examples were presented show the effect of ameliorating rate on inventory polices. Results and Conclusions: The inventory model for items with Weibull ameliorating is developed. For the case of small ameliorating rate (less than linear demand rate, EOQ model is developed, and for the case where ameliorating rate is greater than linear demand rate, PSQ model is developed. .
Pinning the Order: The Nature of Quantum Criticality in the Hubbard Model on Honeycomb Lattice
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Fakher F. Assaad
2013-08-01
Full Text Available In numerical simulations, spontaneously broken symmetry is often detected by computing two-point correlation functions of the appropriate local order parameter. This approach, however, computes the square of the local order parameter, and so when it is small, very large system sizes at high precisions are required to obtain reliable results. Alternatively, one can pin the order by introducing a local symmetry-breaking field and then measure the induced local order parameter infinitely far from the pinning center. The method is tested here at length for the Hubbard model on honeycomb lattice, within the realm of the projective auxiliary-field quantum Monte Carlo algorithm. With our enhanced resolution, we find a direct and continuous quantum phase transition between the semimetallic and the insulating antiferromagnetic states with increase of the interaction. The single-particle gap, measured in units of Hubbard U, tracks the staggered magnetization. An excellent data collapse is obtained by finite-size scaling, with the values of the critical exponents in accord with the Gross-Neveu universality class of the transition.
Compositional modeling of three-phase flow with gravity using higher-order finite element methods
Moortgat, Joachim
2011-05-11
A wide range of applications in subsurface flow involve water, a nonaqueous phase liquid (NAPL) or oil, and a gas phase, such as air or CO2. The numerical simulation of such processes is computationally challenging and requires accurate compositional modeling of three-phase flow in porous media. In this work, we simulate for the first time three-phase compositional flow using higher-order finite element methods. Gravity poses complications in modeling multiphase processes because it drives countercurrent flow among phases. To resolve this issue, we propose a new method for the upwinding of three-phase mobilities. Numerical examples, related to enhanced oil recovery and carbon sequestration, are presented to illustrate the capabilities of the proposed algorithm. We pay special attention to challenges associated with gravitational instabilities and take into account compressibility and various phase behavior effects, including swelling, viscosity changes, and vaporization. We find that the proposed higher-order method can capture sharp solution discontinuities, yielding accurate predictions of phase boundaries arising in computational three-phase flow. This work sets the stage for a broad extension of the higher-order methods for numerical simulation of three-phase flow for complex geometries and processes.
Application of a system dynamics model to improve the performance of make-to-order production
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Yi-Lun Elaine Ho
2015-08-01
Full Text Available This study provides a system dynamics (SD model of make-to-order (MTO production and discusses the key factors of production improvement. The proposed system can be divided into three subsystems: income/cost, order/production, and human resources (HR. The time delay between customer demand, production demand, order quantity, material demand, and inventory is considered in a practical application. In addition, this paper considers how the cycle time is affected by the total input of HR; how unit transportation cost is influenced by the delivery quantity; and how unit penalty (shortage cost is affected by the amount of shortage. The production capacity, yield, and holding cost needed to satisfy practical demands are all considered. A simulation approach to MTO production for meeting contract requests is presented in this study. Simulation results reveal that the amount of shortage will be the most important factor affecting the policy for the replenishment of material. Although the rise in production capacity leads to a reduced amount of shortage, it does not play a significant role. A sensitivity analysis of the replenishment of material policy is conducted to find out the best suggested policy. The SD model is also shown to quickly simulate changes in system behaviour, which allows an organisation enough time to respond to and conquer any unpredictable situation that might occur.
A new model order reduction strategy adapted to nonlinear problems in earthquake engineering.
Bamer, Franz; Amiri, Abbas Kazemi; Bucher, Christian
2017-04-10
Earthquake dynamic response analysis of large complex structures, especially in the presence of nonlinearities, usually turns out to be computationally expensive. In this paper, the methodical developments of a new model order reduction strategy (MOR) based on the proper orthogonal decomposition (POD) method as well as its practical applicability to a realistic building structure are presented. The seismic performance of the building structure, a medical complex, is to be improved by means of base isolation realized by frictional pendulum bearings. According to the new introduced MOR strategy, a set of deterministic POD modes (transformation matrix) is assembled, which is derived based on the information of parts of the response history, so-called snapshots, of the structure under a representative earthquake excitation. Subsequently, this transformation matrix is utilized to create reduced-order models of the structure subjected to different earthquake excitations. These sets of nonlinear low-order representations are now solved in a fractional amount of time in comparison with the computations of the full (non-reduced) systems. The results demonstrate accurate approximations of the physical (full) responses by means of this new MOR strategy if the probable behavior of the structure has already been captured in the POD snapshots. Copyright © 2016 The Authors. Earthquake Engineering & Structural Dynamics Published by John Wiley & Sons Ltd.
Effective bilinear-biquadratic model for noncoplanar ordering in itinerant magnets
Hayami, Satoru; Ozawa, Ryo; Motome, Yukitoshi
2017-06-01
Noncollinear and noncoplanar magnetic textures including Skyrmions and vortices act as emergent electromagnetic fields and give rise to novel electronic and transport properties. We here report a unified understanding of noncoplanar magnetic orderings emergent from the spin-charge coupling in itinerant magnets. The mechanism has its roots in effective multiple spin interactions beyond the conventional Ruderman-Kittel-Kasuya-Yosida (RKKY) mechanism, which are ubiquitously generated in itinerant electron systems with local magnetic moments. By carefully examining the higher-order perturbations in terms of the spin-charge coupling, we construct a minimal effective spin model composed of the bilinear and biquadratic interactions with particular wave numbers dictated by the Fermi surface. Taking two-dimensional systems as examples, we find that our effective model captures the underlying physics of the instability toward noncoplanar multiple-Q states discovered recently: a single-Q helical state expected from the RKKY theory is replaced by a double-Q vortex crystal with chirality density waves even for an infinitely small spin-charge coupling on generic lattices [R. Ozawa et al., J. Phys. Soc. Jpn. 85, 103703 (2016), 10.7566/JPSJ.85.103703], and a triple-Q Skyrmion crystal with a high topological number of two appears while increasing the spin-charge coupling on a triangular lattice [R. Ozawa, S. Hayami, and Y. Motome, Phys. Rev. Lett. 118, 147205 (2017), 10.1103/PhysRevLett.118.147205]. We find that by introducing an external magnetic field, our effective model exhibits a plethora of multiple-Q states. Our effective model will serve as a guide for exploring further exotic magnetic orderings in itinerant magnets, not only in two dimensions but also in three dimensions.
Kim, S.; Jung, B.-J.; Jo, Y.
2014-06-01
We describe development and validation of a tangent linear model for the High-Order Method Modeling Environment, the default dynamical core in the Community Atmosphere Model and the Community Earth System Model that solves a primitive hydrostatic equation using a spectral element method. A tangent linear model is primarily intended to approximate the evolution of perturbations generated by a nonlinear model, provides a computationally efficient way to calculate a nonlinear model trajectory for a short time range, and serves as an intermediate step to write and test adjoint models, as the forward model in the incremental approach to four-dimensional variational data assimilation, and as a tool for stability analysis. Each module in the tangent linear model (version 1.0) is linearized by hands-on derivations, and is validated by the Taylor-Lagrange formula. The linearity checks confirm all modules correctly developed, and the field results of the tangent linear modules converge to the difference field of two nonlinear modules as the magnitude of the initial perturbation is sequentially reduced. Also, experiments for stable integration of the tangent linear model (version 1.0) show that the linear model is also suitable with an extended time step size compared to the time step of the nonlinear model without reducing spatial resolution, or increasing further computational cost. Although the scope of the current implementation leaves room for a set of natural extensions, the results and diagnostic tools presented here should provide guidance for further development of the next generation of the tangent linear model, the corresponding adjoint model, and four-dimensional variational data assimilation, with respect to resolution changes and improvements in linearized physics and dynamics.
Single Top Production at Next-to-Leading Order in the Standard Model Effective Field Theory.
Zhang, Cen
2016-04-22
Single top production processes at hadron colliders provide information on the relation between the top quark and the electroweak sector of the standard model. We compute the next-to-leading order QCD corrections to the three main production channels: t-channel, s-channel, and tW associated production, in the standard model including operators up to dimension six. The calculation can be matched to parton shower programs and can therefore be directly used in experimental analyses. The QCD corrections are found to significantly impact the extraction of the current limits on the operators, because both of an improved accuracy and a better precision of the theoretical predictions. In addition, the distributions of some of the key discriminating observables are modified in a nontrivial way, which could change the interpretation of measurements in terms of UV complete models.
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Zhihong Wang
2015-01-01
Full Text Available Considering the varying inertia and load torque in high speed and high accuracy servo systems, a novel discrete second-order sliding mode adaptive controller (DSSMAC based on characteristic model is proposed, and a command observer is also designed. Firstly, the discrete characteristic model of servo systems is established. Secondly, the recursive least square algorithm is adopted to identify time-varying parameters in characteristic model, and the observer is applied to predict the command value of next sample time. Furthermore, the stability of the closed-loop system and the convergence of the observer are analyzed. The experimental results show that the proposed method not only can adapt to varying inertia and load torque, but also has good disturbance rejection ability and robustness to uncertainties.
Rotaru, Ionela Magdalena
2015-09-01
Knowledge management is a powerful instrument. Areas where knowledge - based modelling can be applied are different from business, industry, government to education area. Companies engage in efforts to restructure the database held based on knowledge management principles as they recognize in it a guarantee of models characterized by the fact that they consist only from relevant and sustainable knowledge that can bring value to the companies. The proposed paper presents a theoretical model of what it means optimizing polyethylene pipes, thus bringing to attention two important engineering fields, the one of the metal cutting process and gas industry, who meet in order to optimize the butt fusion welding process - the polyethylene cutting part - of the polyethylene pipes. All approach is shaped on the principles of knowledge management. The study was made in collaboration with companies operating in the field.
Directory of Open Access Journals (Sweden)
Milad Elyasi
2014-04-01
Full Text Available In the recent decade, studying the economic order quantity (EOQ models with imperfect quality has appealed to many researchers. Only few papers are published discussing EOQ models with imperfect items in a supply chain. In this paper, a two-echelon decentralized supply chain consisting of a manufacture and a supplier that both face just in time (JIT inventory problem is considered. It is sought to find the optimal number of the shipments and the quantity of each shipment in a way that minimizes the both manufacturer’s and the supplier’s cost functions. To the authors’ best knowledge, this is the first paper that deals with imperfect items in a decentralized supply chain. Thereby, three different game theoretical solution approaches consisting of two non-cooperative games and a cooperative game are proposed. Comparing the results of three different scenarios with those of the centralized model, the conclusions are drawn to obtain the best approach.
Exact out-of-time-ordered correlation functions for an interacting lattice fermion model
Tsuji, Naoto; Ueda, Masahito
2016-01-01
An exact solution for local equilibrium and nonequilibrium out-of-time-ordered correlation (OTOC) functions is obtained for a lattice fermion model with on-site interactions, namely the Falicov-Kimball (FK) model, in the large dimensional and thermodynamic limit. Our approach is based on the nonequilibrium dynamical mean-field theory generalized to an extended Kadanoff-Baym contour. We find that the OTOC is enhanced at intermediate coupling around the metal-insulator phase transition, implying that the system is most scrambled in that regime. In the high-temperature limit, the OTOC remains nontrivially finite, even though dynamical charge correlations probed by an ordinary response function are suppressed. We propose an experiment to measure OTOCs of fermionic lattice systems including the FK and Hubbard models in ultracold atomic systems.
Physically-Based Reduced Order Modelling of a Uni-Axial Polysilicon MEMS Accelerometer
Ghisi, Aldo; Mariani, Stefano; Corigliano, Alberto; Zerbini, Sarah
2012-01-01
In this paper, the mechanical response of a commercial off-the-shelf, uni-axial polysilicon MEMS accelerometer subject to drops is numerically investigated. To speed up the calculations, a simplified physically-based (beams and plate), two degrees of freedom model of the movable parts of the sensor is adopted. The capability and the accuracy of the model are assessed against three-dimensional finite element simulations, and against outcomes of experiments on instrumented samples. It is shown that the reduced order model provides accurate outcomes as for the system dynamics. To also get rather accurate results in terms of stress fields within regions that are prone to fail upon high-g shocks, a correction factor is proposed by accounting for the local stress amplification induced by re-entrant corners. PMID:23202031
A Fractional-Order Epidemic Model for Bovine Babesiosis Disease and Tick Populations
Directory of Open Access Journals (Sweden)
José Paulo Carvalho dos Santos
2015-01-01
Full Text Available This paper shows that the epidemic model, previously proposed under ordinary differential equation theory, can be generalized to fractional order on a consistent framework of biological behavior. The domain set for the model in which all variables are restricted is established. Moreover, the existence and stability of equilibrium points are studied. We present the proof that endemic equilibrium point when reproduction number R0>1 is locally asymptotically stable. This result is achieved using the linearization theorem for fractional differential equations. The global asymptotic stability of disease-free point, when R0<1, is also proven by comparison theory for fractional differential equations. The numeric simulations for different scenarios are carried out and data obtained are in good agreement with theoretical results, showing important insight about the use of the fractional coupled differential equations set to model babesiosis disease and tick populations.
A Study of Enhanced, Higher Order Boussinesq-Type Equations and Their Numerical Modelling
DEFF Research Database (Denmark)
Banijamali, Babak
potential for applications to the realm of numerical modelling in coastal engineering. The derivation and analysis of several forms of higher-order in dispersion and non-linearity Boussinesq-type equations have been undertaken, obtaining and investigating the properties of a new and generalised class...... discretisation methods. The analysis categorises the errors of the semidiscretised and the fully-discretised equations into the categories of spurious dispersion and spurious diffusion. In particular, issues of numerical wave refraction and numerical wave blocking are introduced and addressed in the contexts......This project has encompassed efforts in two separate veins: on the one hand, the acquiring of highly accurate model equations of the Boussinesq-type, and on the other hand, the theoretical and practical work in implementing such equations in the form of conventional numerical models, with obvious...
Physically-Based Reduced Order Modelling of a Uni-Axial Polysilicon MEMS Accelerometer
Directory of Open Access Journals (Sweden)
Sarah Zerbini
2012-10-01
Full Text Available In this paper, the mechanical response of a commercial off-the-shelf, uni-axial polysilicon MEMS accelerometer subject to drops is numerically investigated. To speed up the calculations, a simplified physically-based (beams and plate, two degrees of freedom model of the movable parts of the sensor is adopted. The capability and the accuracy of the model are assessed against three-dimensional finite element simulations, and against outcomes of experiments on instrumented samples. It is shown that the reduced order model provides accurate outcomes as for the system dynamics. To also get rather accurate results in terms of stress fields within regions that are prone to fail upon high-g shocks, a correction factor is proposed by accounting for the local stress amplification induced by re-entrant corners.
Modelling of an EGSB treating sugarcane vinasse using first-order variable kinetics.
López, Iván; Borzacconi, Liliana
2011-01-01
An expanded granular sludge bed (EGSB) anaerobic reactor treating sugar cane vinasse was modelled using a simple model with two steps (acidogenesis and methanogenesis), two populations, two substrates and completely mixed conditions. A first-order kinetic equation for both steps with time-variant kinetic coefficients was used. An observer system was used to estimate the evolution of kinetic constants over time. The model was validated by comparing methane flow predictions with experimental values. An estimation of evolution of populations of microorganisms was also performed. This approach allows calculation of specific kinetic constants that reflect biological activity of microorganisms. Variation of specific kinetic constants reflects the influence of the fraction of raw vinasse in the feed. High salt concentrations in the reactor may have inhibited the process.
Generalized Gramian Framework for Model/Controller Order Reduction of Switched Systems
DEFF Research Database (Denmark)
Shaker, Hamid Reza; Wisniewski, Rafal
2011-01-01
In this article, a general method for model/controller order reduction of switched linear dynamical systems is presented. The proposed technique is based on the generalised gramian framework for model reduction. It is shown that different classical reduction methods can be developed...... transform approach for reduction. The framework is developed for switched controller reduction. To the best of our knowledge, there is no other reported result on switched controller reduction in the literature. The method preserves the stability under an arbitrary switching signal for both model...... and controller reduction. Furthermore, it is applicable to both continuous and discrete time systems for different classical gramian-based reduction methods. The performance of the proposed method is illustrated by numerical examples....
Low-order models of wave interactions in the transition to baroclinic chaos
Directory of Open Access Journals (Sweden)
W.-G. Früh
1996-01-01
Full Text Available A hierarchy of low-order models, based on the quasi-geostrophic two-layer model, is used to investigate complex multi-mode flows. The different models were used to study distinct types of nonlinear interactions, namely wave- wave interactions through resonant triads, and zonal flow-wave interactions. The coupling strength of individual triads is estimated using a phase locking probability density function. The flow of primary interest is a strongly modulated amplitude vacillation, whose modulation is coupled to intermittent bursts of weaker wave modes. This flow was found to emerge in a discontinuous bifurcation directly from a steady wave solution. Two mechanism were found to result in this flow, one involving resonant triads, and the other involving zonal flow-wave interactions together with a strong β-effect. The results will be compared with recent laboratory experiments of multi-mode baroclinic waves in a rotating annulus of fluid subjected to a horizontal temperature gradient.
Kontrol Optimal pada Model Economic Order Quantity (EOQ dengan Inisiatif Tim Penjualan
Directory of Open Access Journals (Sweden)
Abdul Latif Al Fauzi
2017-06-01
Full Text Available Pengendalian tingkat persediaan stok suatu produk/barang merupakan salah satu kegiatan yang penting dalam kelancaran penjualan, yang juga berakibat pada keuntungan yang akan diperoleh dari penjualan suatu produk/barang. Model Economic Order Quantity (EOQ merupakan salah satu model persediaan barang yang sering digunakan untuk pengendalian persedian barang. Pada artikel ini dibahas kontrol optimal pada model EOQ dengan inisiatif tim penjualan tidak hanya pada saat kondisi setimbang saja, tetapi kontrol optimal dengan usaha tim penjualan pada setiap saat. Strategi kontrol optimal dilakukan dengan meminimumkan biaya persediaan, biaya pembelian, biaya penjualan dan biaya usaha tim penjualan. Masalah kontrol optimal diselesaikan menggunakan prinsip maksimum Pontryagin. Solusi optimal yang diperoleh disimulasikan secara numerik menggunakan metode Sweep Maju-Mundur. Berdasarkan hasil simulasi numerik dapat diketahui bahwa semakin besar koefisien tingkat permintaan barang, maka proses persediaan barang akan lebih cepat berkurang. Selain itu semakin besar usaha tim penjualan, maka proses persediaan barang akan lebih sedikit bahkan lebih cepat habis.
Directory of Open Access Journals (Sweden)
Szymszal J.
2013-09-01
Full Text Available It has been found that the area where one can look for significant reserves in the procurement logistics is a rational management of the stock of raw materials. Currently, the main purpose of projects which increase the efficiency of inventory management is to rationalise all the activities in this area, taking into account and minimising at the same time the total inventory costs. The paper presents a method for optimising the inventory level of raw materials under a foundry plant conditions using two different control models. The first model is based on the estimate of an optimal level of the minimum emergency stock of raw materials, giving information about the need for an order to be placed immediately and about the optimal size of consignments ordered after the minimum emergency level has occurred. The second model is based on the estimate of a maximum inventory level of raw materials and an optimal order cycle. Optimisation of the presented models has been based on the previously done selection and use of rational methods for forecasting the time series of the delivery of a chosen auxiliary material (ceramic filters to a casting plant, including forecasting a mean size of the delivered batch of products and its standard deviation.
Linear and nonlinear stability analysis in BWRs applying a reduced order model
Energy Technology Data Exchange (ETDEWEB)
Olvera G, O. A.; Espinosa P, G.; Prieto G, A., E-mail: omar_olverag@hotmail.com [Universidad Autonoma Metropolitana, Unidad Iztapalapa, San Rafael Atlixco No. 186, Col. Vicentina, 09340 Ciudad de Mexico (Mexico)
2016-09-15
Boiling Water Reactor (BWR) stability studies are generally conducted through nonlinear reduced order models (Rom) employing various techniques such as bifurcation analysis and time domain numerical integration. One of those models used for these studies is the March-Leuba Rom. Such model represents qualitatively the dynamic behavior of a BWR through a one-point reactor kinetics, a one node representation of the heat transfer process in fuel, and a two node representation of the channel Thermal hydraulics to account for the void reactivity feedback. Here, we study the effect of this higher order model on the overall stability of the BWR. The change in the stability boundaries is determined by evaluating the eigenvalues of the Jacobian matrix. The nonlinear model is also integrated numerically to show that in the nonlinear region, the system evolves to stable limit cycles when operating close to the stability boundary. We also applied a new technique based on the Empirical Mode Decomposition (Emd) to estimate a parameter linked with stability in a BWR. This instability parameter is not exactly the classical Decay Ratio (Dr), but it will be linked with it. The proposed method allows decomposing the analyzed signal in different levels or mono-component functions known as intrinsic mode functions (Imf). One or more of these different modes can be associated to the instability problem in BWRs. By tracking the instantaneous frequencies (calculated through Hilbert Huang Transform (HHT) and the autocorrelation function (Acf) of the Imf linked to instability. The estimation of the proposed parameter can be achieved. The current methodology was validated with simulated signals of the studied model. (Author)
Quantum criticality and first-order transitions in the extended periodic Anderson model
Hagymási, I.; Itai, K.; Sólyom, J.
2013-03-01
We investigate the behavior of the periodic Anderson model in the presence of d-f Coulomb interaction (Udf) using mean-field theory, variational calculation, and exact diagonalization of finite chains. The variational approach based on the Gutzwiller trial wave function gives a critical value of Udf and two quantum critical points (QCPs), where the valence susceptibility diverges. We derive the critical exponent for the valence susceptibility and investigate how the position of the QCP depends on the other parameters of the Hamiltonian. For larger values of Udf, the Kondo regime is bounded by two first-order transitions. These first-order transitions merge into a triple point at a certain value of Udf. For even larger Udf valence skipping occurs. Although the other methods do not give a critical point, they support this scenario.
Singlet exciton condensation and bond-order-wave phase in the extended Hubbard model
Hafez-Torbati, Mohsen; Uhrig, Götz S.
2017-09-01
The competition of interactions implies the compensation of standard mechanisms, which leads to the emergence of exotic phases between conventional phases. The extended Hubbard model (EHM) is a fundamental example for the competition of the local Hubbard interaction and the nearest-neighbor density-density interaction, which at half-filling and in one dimension leads to a bond-order wave (BOW) between a charge-density wave (CDW) and a quasi-long-range order Mott insulator. We study the full momentum-resolved excitation spectrum of the one-dimensional EHM in the CDW phase, and we clarify the relation between different elementary energy gaps. We show that the CDW-to-BOW transition is driven by the softening of a singlet exciton at momentum π . The BOW is realized as the condensate of this singlet exciton.
Quantum phase transition between antiferromagnetic and charge order in the Hubbard-Holstein model
Energy Technology Data Exchange (ETDEWEB)
Bauer, Johannes [Max-Planck Institute for Solid State Research, Heisenbergstr.1, 70569 Stuttgart (Germany); Hewson, Alex C. [Department of Mathematics, Imperial College, London SW7 2AZ (United Kingdom)
2010-03-15
We explore the quantum phase transitions between two ordered states in the infinite dimensional Hubbard-Holstein model at half filling. Our study is based on the dynamical mean field theory (DMFT) combined with the numerical renormalization group (NRG), which allows us to handle both strong electron-electron and strong electron-phonon interactions. The transition line is characterized by an effective electron-electron interaction. Depending on this effective interaction and the phonon frequency {omega}{sub 0} one finds either a continuous transition or discontinuous transition. Here, the analysis focuses on the behavior of the system when the electron-electron repulsion U and the phonon-mediated attraction {lambda} are equal. We first discuss the adiabatic and antiadiabatic limiting cases. For finite {omega}{sub 0} we study the differences between the antiferromagnetic (AFM) and charge order, and find that when present the AFM state has a lower energy on the line. (Abstract Copyright [2010], Wiley Periodicals, Inc.)
Magnetic relaxation in a spin-1 Ising model near the second-order phase transition point
Energy Technology Data Exchange (ETDEWEB)
Erdem, Riza [Department of Physics, Gaziosmanpasa University, Tasliciftlik Campus, Tokat 60250 (Turkey)], E-mail: rerdem29@hotmail.com
2008-09-15
The magnetic relaxation of a spin-1 Ising model with bilinear and biquadratic interactions is formulated within the framework of statistical equilibrium theory and the thermodynamics of irreversible processes. Using a molecular-field expression for the magnetic Gibbs energy, the magnetic Gibbs energy produced in the irreversible process is calculated and time derivatives of the dipolar and quadrupolar order parameters are treated as fluxes conjugate to their appropriate generalized forces in the sense of Onsager theory. The kinetic equations are obtained by introducing kinetic coefficients that satisfy the Onsager relation. By solving these equations an expression is derived for the dynamic or complex magnetic susceptibility. From the real and imaginary parts of this expression, magnetic dispersion and absorption factor are calculated and analyzed near the second-order phase transition.
Magnetic relaxation in a spin-1 Ising model near the second-order phase transition point
Erdem, Rıza
The magnetic relaxation of a spin-1 Ising model with bilinear and biquadratic interactions is formulated within the framework of statistical equilibrium theory and the thermodynamics of irreversible processes. Using a molecular-field expression for the magnetic Gibbs energy, the magnetic Gibbs energy produced in the irreversible process is calculated and time derivatives of the dipolar and quadrupolar order parameters are treated as fluxes conjugate to their appropriate generalized forces in the sense of Onsager theory. The kinetic equations are obtained by introducing kinetic coefficients that satisfy the Onsager relation. By solving these equations an expression is derived for the dynamic or complex magnetic susceptibility. From the real and imaginary parts of this expression, magnetic dispersion and absorption factor are calculated and analyzed near the second-order phase transition.
A reduced-order, single-bubble cavitation model with applications to therapeutic ultrasound
Kreider, Wayne; Crum, Lawrence A.; Bailey, Michael R.; Sapozhnikov, Oleg A.
2011-01-01
Cavitation often occurs in therapeutic applications of medical ultrasound such as shock-wave lithotripsy (SWL) and high-intensity focused ultrasound (HIFU). Because cavitation bubbles can affect an intended treatment, it is important to understand the dynamics of bubbles in this context. The relevant context includes very high acoustic pressures and frequencies as well as elevated temperatures. Relative to much of the prior research on cavitation and bubble dynamics, such conditions are unique. To address the relevant physics, a reduced-order model of a single, spherical bubble is proposed that incorporates phase change at the liquid-gas interface as well as heat and mass transport in both phases. Based on the energy lost during the inertial collapse and rebound of a millimeter-sized bubble, experimental observations were used to tune and test model predictions. In addition, benchmarks from the published literature were used to assess various aspects of model performance. Benchmark comparisons demonstrate that the model captures the basic physics of phase change and diffusive transport, while it is quantitatively sensitive to specific model assumptions and implementation details. Given its performance and numerical stability, the model can be used to explore bubble behaviors across a broad parameter space relevant to therapeutic ultrasound. PMID:22088026
Navarro, Rafael; Palos, Fernando; Lanchares, Elena; Calvo, Begoña; Cristóbal, José A
2009-01-01
To develop a realistic model of the optomechanical behavior of the cornea after curved relaxing incisions to simulate the induced astigmatic change and predict the optical aberrations produced by the incisions. ICMA Consejo Superior de Investigaciones Científicas and Universidad de Zaragoza, Zaragoza, Spain. A 3-dimensional finite element model of the anterior hemisphere of the ocular surface was used. The corneal tissue was modeled as a quasi-incompressible, anisotropic hyperelastic constitutive behavior strongly dependent on the physiological collagen fibril distribution. Similar behaviors were assigned to the limbus and sclera. With this model, some corneal incisions were computer simulated after the Lindstrom nomogram. The resulting geometry of the biomechanical simulation was analyzed in the optical zone, and finite ray tracing was performed to compute refractive power and higher-order aberrations (HOAs). The finite-element simulation provided new geometry of the corneal surfaces, from which elevation topographies were obtained. The surgically induced astigmatism (SIA) of the simulated incisions according to the Lindstrom nomogram was computed by finite ray tracing. However, paraxial computations would yield slightly different results (undercorrection of astigmatism). In addition, arcuate incisions would induce significant amounts of HOAs. Finite-element models, together with finite ray-tracing computations, yielded realistic simulations of the biomechanical and optical changes induced by relaxing incisions. The model reproduced the SIA indicated by the Lindstrom nomogram for the simulated incisions and predicted a significant increase in optical aberrations induced by arcuate keratotomy.
A porous flow model of flank eruptions on Mt. Etna: second-order perturbation theory
Directory of Open Access Journals (Sweden)
N. Cenni
1997-06-01
Full Text Available A porous flow model for magma migration from a deep source within a volcanic edifice is developed. The model is based on the assumption that an isotropic and homogeneous system of fractures allows magma migration from one localized feeding dyke up to the surface of the volcano. The maximum level that magma can reach within the volcano (i.e., the «free surface» of magma, where fluid pressure equals the atmospheric pressure is reproduced through a second-order perturbation approach to the non-linear equations governing the migration of incompressible fluids through a porous medium. The perturbation parameter is found to depend on the ratio of the volumic discharge rate at the source (m3/s divided by the product of the hydraulic conductivity of the medium (m1/s times the square of the source depth. The second-order corrections for the free surface of Mt. Etna are found to be small but not negligible; from the comparison between first-order and second-order free surfaces it appears that the former is higher near the summit, slightly lower at intermediate altitudes and slightly higher far away from the axis of the volcano. Flank eruptions in the southern sector are found to be located in regions where the topography is actually lower than the theoretical free surface of magma. In this sector, modulations in the eruption site density correlate well with even minor differences between free surface and topography. In the northern and western sectors similar good fits are found, while the NE rift and the eastern sector seem to require mechanisms or structures respectively favouring and inhibiting magma migration.
Assessment of First- and Second-Order Wave-Excitation Load Models for Cylindrical Substructures
Energy Technology Data Exchange (ETDEWEB)
Pereyra, Brandon; Wendt, Fabian; Robertson, Amy; Jonkman, Jason
2016-07-01
The hydrodynamic loads on an offshore wind turbine's support structure present unique engineering challenges for offshore wind. Two typical approaches used for modeling these hydrodynamic loads are potential flow (PF) and strip theory (ST), the latter via Morison's equation. This study examines the first- and second-order wave-excitation surge forces on a fixed cylinder in regular waves computed by the PF and ST approaches to (1) verify their numerical implementations in HydroDyn and (2) understand when the ST approach breaks down. The numerical implementation of PF and ST in HydroDyn, a hydrodynamic time-domain solver implemented as a module in the FAST wind turbine engineering tool, was verified by showing the consistency in the first- and second-order force output between the two methods across a range of wave frequencies. ST is known to be invalid at high frequencies, and this study investigates where the ST solution diverges from the PF solution. Regular waves across a range of frequencies were run in HydroDyn for a monopile substructure. As expected, the solutions for the first-order (linear) wave-excitation loads resulting from these regular waves are similar for PF and ST when the diameter of the cylinder is small compared to the length of the waves (generally when the diameter-to-wavelength ratio is less than 0.2). The same finding applies to the solutions for second-order wave-excitation loads, but for much smaller diameter-to-wavelength ratios (based on wavelengths of first-order waves).
Energy Technology Data Exchange (ETDEWEB)
Pereyra, Brandon; Wendt, Fabian; Robertson, Amy; Jonkman, Jason
2017-03-09
The hydrodynamic loads on an offshore wind turbine's support structure present unique engineering challenges for offshore wind. Two typical approaches used for modeling these hydrodynamic loads are potential flow (PF) and strip theory (ST), the latter via Morison's equation. This study examines the first- and second-order wave-excitation surge forces on a fixed cylinder in regular waves computed by the PF and ST approaches to (1) verify their numerical implementations in HydroDyn and (2) understand when the ST approach breaks down. The numerical implementation of PF and ST in HydroDyn, a hydrodynamic time-domain solver implemented as a module in the FAST wind turbine engineering tool, was verified by showing the consistency in the first- and second-order force output between the two methods across a range of wave frequencies. ST is known to be invalid at high frequencies, and this study investigates where the ST solution diverges from the PF solution. Regular waves across a range of frequencies were run in HydroDyn for a monopile substructure. As expected, the solutions for the first-order (linear) wave-excitation loads resulting from these regular waves are similar for PF and ST when the diameter of the cylinder is small compared to the length of the waves (generally when the diameter-to-wavelength ratio is less than 0.2). The same finding applies to the solutions for second-order wave-excitation loads, but for much smaller diameter-to-wavelength ratios (based on wavelengths of first-order waves).
Energy Technology Data Exchange (ETDEWEB)
Liu, Youshan, E-mail: ysliu@mail.iggcas.ac.cn [State Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, 100029 (China); Teng, Jiwen, E-mail: jwteng@mail.iggcas.ac.cn [State Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, 100029 (China); Xu, Tao, E-mail: xutao@mail.iggcas.ac.cn [State Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, 100029 (China); CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, 100101 (China); Badal, José, E-mail: badal@unizar.es [Physics of the Earth, Sciences B, University of Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza (Spain)
2017-05-01
The mass-lumped method avoids the cost of inverting the mass matrix and simultaneously maintains spatial accuracy by adopting additional interior integration points, known as cubature points. To date, such points are only known analytically in tensor domains, such as quadrilateral or hexahedral elements. Thus, the diagonal-mass-matrix spectral element method (SEM) in non-tensor domains always relies on numerically computed interpolation points or quadrature points. However, only the cubature points for degrees 1 to 6 are known, which is the reason that we have developed a p-norm-based optimization algorithm to obtain higher-order cubature points. In this way, we obtain and tabulate new cubature points with all positive integration weights for degrees 7 to 9. The dispersion analysis illustrates that the dispersion relation determined from the new optimized cubature points is comparable to that of the mass and stiffness matrices obtained by exact integration. Simultaneously, the Lebesgue constant for the new optimized cubature points indicates its surprisingly good interpolation properties. As a result, such points provide both good interpolation properties and integration accuracy. The Courant–Friedrichs–Lewy (CFL) numbers are tabulated for the conventional Fekete-based triangular spectral element (TSEM), the TSEM with exact integration, and the optimized cubature-based TSEM (OTSEM). A complementary study demonstrates the spectral convergence of the OTSEM. A numerical example conducted on a half-space model demonstrates that the OTSEM improves the accuracy by approximately one order of magnitude compared to the conventional Fekete-based TSEM. In particular, the accuracy of the 7th-order OTSEM is even higher than that of the 14th-order Fekete-based TSEM. Furthermore, the OTSEM produces a result that can compete in accuracy with the quadrilateral SEM (QSEM). The high accuracy of the OTSEM is also tested with a non-flat topography model. In terms of computational
Albert, Lena; Rottensteiner, Franz; Heipke, Christian
2017-08-01
We propose a new approach for the simultaneous classification of land cover and land use considering spatial as well as semantic context. We apply a Conditional Random Fields (CRF) consisting of a land cover and a land use layer. In the land cover layer of the CRF, the nodes represent super-pixels; in the land use layer, the nodes correspond to objects from a geospatial database. Intra-layer edges of the CRF model spatial dependencies between neighbouring image sites. All spatially overlapping sites in both layers are connected by inter-layer edges, which leads to higher order cliques modelling the semantic relation between all land cover and land use sites in the clique. A generic formulation of the higher order potential is proposed. In order to enable efficient inference in the two-layer higher order CRF, we propose an iterative inference procedure in which the two classification tasks mutually influence each other. We integrate contextual relations between land cover and land use in the classification process by using contextual features describing the complex dependencies of all nodes in a higher order clique. These features are incorporated in a discriminative classifier, which approximates the higher order potentials during the inference procedure. The approach is designed for input data based on aerial images. Experiments are carried out on two test sites to evaluate the performance of the proposed method. The experiments show that the classification results are improved compared to the results of a non-contextual classifier. For land cover classification, the result is much more homogeneous and the delineation of land cover segments is improved. For the land use classification, an improvement is mainly achieved for land use objects showing non-typical characteristics or similarities to other land use classes. Furthermore, we have shown that the size of the super-pixels has an influence on the level of detail of the classification result, but also on the
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments.
Tünnermann, Jan; Krüger, Alexander; Scharlau, Ingrid
2017-01-23
This protocol describes how to conduct temporal-order experiments to measure visual processing speed and the attentional resource distribution. The proposed method is based on a new and synergistic combination of three components: the temporal-order judgments (TOJ) paradigm, Bundesen's Theory of Visual Attention (TVA), and a hierarchical Bayesian estimation framework. The method provides readily interpretable parameters, which are supported by the theoretical and neurophysiological underpinnings of TVA. Using TOJs, TVA-based estimates can be obtained for a broad range of stimuli, whereas traditional paradigms used with TVA are mainly limited to letters and digits. Finally, the meaningful parameters of the proposed model allow for the establishment of a hierarchical Bayesian model. Such a statistical model allows assessing results in one coherent analysis both on the subject and the group level. To demonstrate the feasibility and versatility of this new approach, three experiments are reported with attention manipulations in synthetic pop-out displays, natural images, and a cued letter-report paradigm.
Modeling Multi-Agent Self-Organization through the Lens of Higher Order Attractor Dynamics.
Butner, Jonathan E; Wiltshire, Travis J; Munion, A K
2017-01-01
Social interaction occurs across many time scales and varying numbers of agents; from one-on-one to large-scale coordination in organizations, crowds, cities, and colonies. These contexts, are characterized by emergent self-organization that implies higher order coordinated patterns occurring over time that are not due to the actions of any particular agents, but rather due to the collective ordering that occurs from the interactions of the agents. Extant research to understand these social coordination dynamics (SCD) has primarily examined dyadic contexts performing rhythmic tasks. To advance this area of study, we elaborate on attractor dynamics, our ability to depict them visually, and quantitatively model them. Primarily, we combine difference/differential equation modeling with mixture modeling as a way to infer the underlying topological features of the data, which can be described in terms of attractor dynamic patterns. The advantage of this approach is that we are able to quantify the self-organized dynamics that agents exhibit, link these dynamics back to activity from individual agents, and relate it to other variables central to understanding the coordinative functionality of a system's behavior. We present four examples that differ in the number of variables used to depict the attractor dynamics (1, 2, and 6) and range from simulated to non-simulated data sources. We demonstrate that this is a flexible method that advances scientific study of SCD in a variety of multi-agent systems.
HOMOR: higher order model outlier rejection for high b-value MR diffusion data.
Pannek, Kerstin; Raffelt, David; Bell, Christopher; Mathias, Jane L; Rose, Stephen E
2012-11-01
Diffusion MR images are prone to artefacts caused by head movement and cardiac pulsation. Previous techniques for the automated voxel-wise detection of signal intensity outliers have relied on the fit of the diffusion tensor to the data (RESTORE). However, the diffusion tensor cannot appropriately model more than a single fibre population, which may lead to inaccuracies when identifying outlier voxels in crossing fibre regions, particularly when high b-values are used to obtain increased angular contrast. HOMOR (higher order model outlier rejection) was developed to overcome this limitation and is introduced in this study. HOMOR is closely related to RESTORE, but employs a higher order model capable of resolving multiple fibre populations within a voxel. Using high b-value (b=3000 s/mm2) diffusion data from a population of 90 healthy participants, as well as simulations, HOMOR was found to identify a decreased number of outlier voxels compared to RESTORE primarily within areas of crossing, bending and fanning fibres. At lower b-values, however, RESTORE and HOMOR give similar results, which is demonstrated using diffusion data acquired at b=1000 s/mm2 in a mixed cohort. This study demonstrates that, although RESTORE is suitable for low b-value data, HOMOR is better suited for high b-value data. Copyright © 2012 Elsevier Inc. All rights reserved.
An analysis of motorcycle injury and vehicle damage severity using ordered probit models.
Quddus, Mohammed A; Noland, Robert B; Chin, Hoong Chor
2002-01-01
Motorcycles constitute about 19% of all motorized vehicles in Singapore and are generally overrepresented in traffic accidents, accounting for 40% of total fatalities. In this paper, an ordered probit model is used to examine factors that affect the injury severity of motorcycle accidents and the severity of damage to the vehicle for those crashes. Nine years of motorcycle accident data were obtained for Singapore through police reports. These data included categorical assessments of the severity of accidents based on three levels. Damage severity to the vehicle was also assessed and categorized into four levels. Categorical data of this type are best analyzed using ordered probit models because they require no assumptions regarding the ordinality of the dependent variable, which in this case is the severity score. Various models are examined to determine what factors are related to increased injury and damage severity of motorcycle accidents. Factors found to lead to increases in the probability of severe injuries include the motorcyclist having non-Singaporean nationality, increased engine capacity, headlight not turned on during daytime, collisions with pedestrians and stationary objects, driving during early morning hours, having a pillion passenger, and when the motorcyclist is determined to be at fault for the accident. Factors leading to increased probability of vehicle damage include some similar factors but also show some differences, such as less damage associated with pedestrian collisions and with female drivers. In addition, it was also found that both injury severity and vehicle damage severity levels are decreasing over time.
Reduced-Order Models for Load Management in the Power Grid
Alizadeh, Mahnoosh
In recent years, considerable research efforts have been directed towards designing control schemes that can leverage the inherent flexibility of electricity demand that is not tapped into in today's electricity markets. It is expected that these control schemes will be carried out by for-profit entities referred to as aggregators that operate at the edge of the power grid network. While the aggregator control problem is receiving much attention, more high-level questions of how these aggregators should plan their market participation, interact with the main grid and with each other, remain rather understudied. Answering these questions requires a large-scale model for the aggregate flexibility that can be harnessed from the a population of customers, particularly for residences and small businesses. The contribution of this thesis towards this goal is divided into three parts: In Chapter 3, a reduced-order model for a large population of heterogeneous appliances is provided by clustering load profiles that share similar degrees of freedom together. The use of such reduced-order model for system planning and optimal market decision making requires a foresighted approximation of the number of appliances that will join each cluster. Thus, Chapter 4 provides a systematic framework to generate such forecasts for the case of Electric Vehicles, based on real-world battery charging data. While these two chapters set aside the economic side that is naturally involved with participation in demand response programs and mainly focus on the control problem, Chapter 5 is dedicated to the study of optimal pricing mechanisms in order to recruit heterogeneous customers in a demand response program in which an aggregator can directly manage their appliances' load under their specified preferences. Prices are proportional to the wholesale market savings that can result from each recruitment event.
Ruzziconi, Laura
2013-06-10
We present a study of the dynamic behavior of a microelectromechanical systems (MEMS) device consisting of an imperfect clamped-clamped microbeam subjected to electrostatic and electrodynamic actuation. Our objective is to develop a theoretical analysis, which is able to describe and predict all the main relevant aspects of the experimental response. Extensive experimental investigation is conducted, where the main imperfections coming from microfabrication are detected, the first four experimental natural frequencies are identified and the nonlinear dynamics are explored at increasing values of electrodynamic excitation, in a neighborhood of the first symmetric resonance. Several backward and forward frequency sweeps are acquired. The nonlinear behavior is highlighted, which includes ranges of multistability, where the nonresonant and the resonant branch coexist, and intervals where superharmonic resonances are clearly visible. Numerical simulations are performed. Initially, two single mode reduced-order models are considered. One is generated via the Galerkin technique, and the other one via the combined use of the Ritz method and the Padé approximation. Both of them are able to provide a satisfactory agreement with the experimental data. This occurs not only at low values of electrodynamic excitation, but also at higher ones. Their computational efficiency is discussed in detail, since this is an essential aspect for systematic local and global simulations. Finally, the theoretical analysis is further improved and a two-degree-of-freedom reduced-order model is developed, which is also capable of capturing the measured second symmetric superharmonic resonance. Despite the apparent simplicity, it is shown that all the proposed reduced-order models are able to describe the experimental complex nonlinear dynamics of the device accurately and properly, which validates the proposed theoretical approach. © 2013 IOP Publishing Ltd.
Modeling of geogenic radon in Switzerland based on ordered logistic regression.
Kropat, Georg; Bochud, François; Murith, Christophe; Palacios Gruson, Martha; Baechler, Sébastien
2017-01-01
The estimation of the radon hazard of a future construction site should ideally be based on the geogenic radon potential (GRP), since this estimate is free of anthropogenic influences and building characteristics. The goal of this study was to evaluate terrestrial gamma dose rate (TGD), geology, fault lines and topsoil permeability as predictors for the creation of a GRP map based on logistic regression. Soil gas radon measurements (SRC) are more suited for the estimation of GRP than indoor radon measurements (IRC) since the former do not depend on ventilation and heating habits or building characteristics. However, SRC have only been measured at a few locations in Switzerland. In former studies a good correlation between spatial aggregates of IRC and SRC has been observed. That's why we used IRC measurements aggregated on a 10 km × 10 km grid to calibrate an ordered logistic regression model for geogenic radon potential (GRP). As predictors we took into account terrestrial gamma doserate, regrouped geological units, fault line density and the permeability of the soil. The classification success rate of the model results to 56% in case of the inclusion of all 4 predictor variables. Our results suggest that terrestrial gamma doserate and regrouped geological units are more suited to model GRP than fault line density and soil permeability. Ordered logistic regression is a promising tool for the modeling of GRP maps due to its simplicity and fast computation time. Future studies should account for additional variables to improve the modeling of high radon hazard in the Jura Mountains of Switzerland. Copyright Â© 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.
National Aeronautics and Space Administration — The overall goal of the project is to develop reliable reduced order modeling technologies to automatically generate nonlinear, parameter-varying (PV),...
Multi-Level Reduced Order Modeling Equipped with Probabilistic Error Bounds
Abdo, Mohammad Gamal Mohammad Mostafa
This thesis develops robust reduced order modeling (ROM) techniques to achieve the needed efficiency to render feasible the use of high fidelity tools for routine engineering analyses. Markedly different from the state-of-the-art ROM techniques, our work focuses only on techniques which can quantify the credibility of the reduction which can be measured with the reduction errors upper-bounded for the envisaged range of ROM model application. Our objective is two-fold. First, further developments of ROM techniques are proposed when conventional ROM techniques are too taxing to be computationally practical. This is achieved via a multi-level ROM methodology designed to take advantage of the multi-scale modeling strategy typically employed for computationally taxing models such as those associated with the modeling of nuclear reactor behavior. Second, the discrepancies between the original model and ROM model predictions over the full range of model application conditions are upper-bounded in a probabilistic sense with high probability. ROM techniques may be classified into two broad categories: surrogate construction techniques and dimensionality reduction techniques, with the latter being the primary focus of this work. We focus on dimensionality reduction, because it offers a rigorous approach by which reduction errors can be quantified via upper-bounds that are met in a probabilistic sense. Surrogate techniques typically rely on fitting a parametric model form to the original model at a number of training points, with the residual of the fit taken as a measure of the prediction accuracy of the surrogate. This approach, however, does not generally guarantee that the surrogate model predictions at points not included in the training process will be bound by the error estimated from the fitting residual. Dimensionality reduction techniques however employ a different philosophy to render the reduction, wherein randomized snapshots of the model variables, such as the
Sutrisno
2012-01-01
The Analysis of the Inventory Control System of Probabilistic Models with Back Order at Indah Traso Company Medan. Supervised by Prof. Dr. Ir. A. Rahim Matondang, MSIE.and Ir. Rosnani Ginting, MT. The problem which is usually faced by an industrial company is the problem of inventory management; for example, in the inappropriate procurement of goods by the company. The stock out of goods when there is a demand from consumer will cause the company to miss an opportunity to gain profit or t...
Regarding on the prototype solutions for the nonlinear fractional-order biological population model
Energy Technology Data Exchange (ETDEWEB)
Baskonus, Haci Mehmet, E-mail: hmbaskonus@gmail.com [Department of Computer Engineering, Tunceli University, Tunceli (Turkey); Bulut, Hasan [Department of Mathematics, Fzrat University, Elazig, Turkey, email: hbulut@firat.edu.tr (Turkey)
2016-06-08
In this study, we have submitted to literature a method newly extended which is called as Improved Bernoulli sub-equation function method based on the Bernoulli Sub-ODE method. The proposed analytical scheme has been expressed with steps. We have obtained some new analytical solutions to the nonlinear fractional-order biological population model by using this technique. Two and three dimensional surfaces of analytical solutions have been drawn by wolfram Mathematica 9. Finally, a conclusion has been submitted by mentioning important acquisitions founded in this study.
A new simple model for composite fading channels: Second order statistics and channel capacity
Yilmaz, Ferkan
2010-09-01
In this paper, we introduce the most general composite fading distribution to model the envelope and the power of the received signal in such fading channels as millimeter wave (60 GHz or above) fading channels and free-space optical channels, which we term extended generalized-K (EGK) composite fading distribution. We obtain the second-order statistics of the received signal envelope characterized by the EGK composite fading distribution. Expressions for probability density function, cumulative distribution function, level crossing rate and average fade duration, moments, amount of fading and average capacity are derived. Numerical and computer simulation examples validate the accuracy of the presented mathematical analysis. © 2010 IEEE.
High-order Boussinesq-type modelling of nonlinear wave phenomena in deep and shallow water
DEFF Research Database (Denmark)
Madsen, Per A.; Fuhrman, David R.
2010-01-01
In this work, we start with a review of the development of Boussinesq theory for water waves covering the period from 1872 to date. Previous reviews have been given by Dingemans,1 Kirby,2,3 and Madsen & Schäffer.4 Next, we present our most recent high-order Boussinesq-type formulation valid...... for fully nonlinear and highly dispersive waves traveling over a rapidly varying bathymetry. Finally, we cover applications of this Boussinesq model, and we study a number of nonlinear wave phenomena in deep and shallow water. These include (1) Kinematics in highly nonlinear progressive deep-water waves; (2...
A Simple Model of Separation Logic for Higher-order Store
DEFF Research Database (Denmark)
Birkedal, Lars; Reus, Bernhard; Schwinghammer, Jan
2008-01-01
Separation logic is a Hoare-style logic for reasoning about pointer-manipulating programs. Its core ideas have recently been extended from low-level to richer, high-level languages. In this paper we develop a new semantics of the logic for a programming language where code can be stored (i.......e., with higher-order store). The main improvement on previous work is the simplicity of the model. As a consequence, several restrictions imposed by the semantics are removed, leading to a considerably more natural assertion language with a powerful specification logic....
Directory of Open Access Journals (Sweden)
Alvaro Ruíz-Baltazar
2015-12-01
Full Text Available In this research, the adsorption capacity of Ag nanoparticles on natural zeolite from Oaxaca is presented. In order to describe the adsorption mechanism of silver nanoparticles on zeolite, experimental adsorption models for Ag ions and Ag nanoparticles were carried out. These experimental data obtained by the atomic absorption spectrophotometry technique were compared with theoretical models such as Lagergren first-order, pseudo-second-order, Elovich, and intraparticle diffusion. Correlation factors R2 of the order of 0.99 were observed. Analysis by transmission electron microscopy describes the distribution of the silver nanoparticles on the zeolite outer surface. Additionally, a chemical characterization of the material was carried out through a dilution process with lithium metaborate. An average value of 9.3 in the Si/Al ratio was observed. Factors such as the adsorption behavior of the silver ions and the Si/Al ratio of the zeolite are very important to support the theoretical models and establish the adsorption mechanism of Ag nanoparticles on natural zeolite.
SyntEyes KTC: higher order statistical eye model for developing keratoconus.
Rozema, Jos J; Rodriguez, Pablo; Ruiz Hidalgo, Irene; Navarro, Rafael; Tassignon, Marie-José; Koppen, Carina
2017-05-01
To present and validate a stochastic eye model for developing keratoconus to e.g. improve optical corrective strategies. This could be particularly useful for researchers that do not have access to original keratoconic data. The Scheimpflug tomography, ocular biometry and wavefront of 145 keratoconic right eyes were collected. These data were processed using principal component analysis for parameter reduction, followed by a multivariate Gaussian fit that produces a stochastic model for keratoconus (SyntEyes KTC). The output of this model is filtered to remove the occasional incorrect topography patterns by either an automatic or manual procedure. Finally, the output of this keratoconus model is matched to that of the original model for normal eyes using the non-corneal biometry to obtain a description of keratoconus development. The synthetic data generated by the model were found to be significantly equal to the original data (non-parametric Mann-Whitney equivalence test; 145/154 passed). The variability of the synthetic data, however, was often significantly less than that of the original data, especially for the higher order Zernike terms of corneal elevation (non-parametric Levene test; p < 0.05/154). These results remained generally the same after applying either filter procedure to remove the synthetic eyes with incorrect topographies. Interpolation between matched pairs of normal and keratoconic SyntEyes appears to provide an adequate model for keratoconus progression. The synthetic data provided by the proposed keratoconus model closely resembles actual clinical data and may be used for a range of research applications when (sufficient) real data is not available. © 2017 The Authors Ophthalmic & Physiological Optics © 2017 The College of Optometrists.
Ito, Shin-Ichi; Nagao, Hiromichi; Yamanaka, Akinori; Tsukada, Yuhki; Koyama, Toshiyuki; Inoue, Junya
Phase field (PF) method, which phenomenologically describes dynamics of microstructure evolutions during solidification and phase transformation, has progressed in the fields of hydromechanics and materials engineering. How to determine, based on observation data, an initial state and model parameters involved in a PF model is one of important issues since previous estimation methods require too much computational cost. We propose data assimilation (DA), which enables us to estimate the parameters and states by integrating the PF model and observation data on the basis of the Bayesian statistics. The adjoint method implemented on DA not only finds an optimum solution by maximizing a posterior distribution but also evaluates the uncertainty in the estimations by utilizing the second order information of the posterior distribution. We carried out an estimation test using synthetic data generated by the two-dimensional Kobayashi's PF model. The proposed method is confirmed to reproduce the true initial state and model parameters we assume in advance, and simultaneously estimate their uncertainties due to quality and quantity of the data. This result indicates that the proposed method is capable of suggesting the experimental design to achieve the required accuracy.
A generative spike train model with time-structured higher order correlations
Directory of Open Access Journals (Sweden)
James eTrousdale
2013-07-01
Full Text Available Emerging technologies are revealing the spiking activity in ever larger neural ensembles. Frequently, this spiking is far from independent, with correlations in the spike times of different cells. Understanding how such correlations impact the dynamics and function of neural ensembles remains an important open problem.Here we describe a new, generative model for correlated spike trains that can exhibit many of the features observed in data. Extending prior work in mathematical finance, this generalized thinning and shift (GTaS model creates marginally Poisson spike trains with diverse temporal correlation structures.We give several examples which highlight the model's flexibility and utility. For instance, we use it to examine how a neural network responds to highly structured patterns of inputs.We then show that the GTaS model is analytically tractable, and derive cumulant densities of all orders in terms of model parameters. The GTaS framework can therefore be an important tool in the experimental and theoretical exploration of neural dynamics.
First-order uncertainty analysis using Algorithmic Differentiation of morphodynamic models
Villaret, Catherine; Kopmann, Rebekka; Wyncoll, David; Riehme, Jan; Merkel, Uwe; Naumann, Uwe
2016-05-01
We present here an efficient first-order second moment method using Algorithmic Differentiation (FOSM/AD) which can be applied to quantify uncertainty/sensitivities in morphodynamic models. Changes with respect to variable flow and sediment input parameters are estimated with machine accuracy using the technique of Algorithmic Differentiation (AD). This method is particularly attractive for process-based morphodynamic models like the Telemac-2D/Sisyphe model considering the large number of input parameters and CPU time associated to each simulation. The FOSM/AD method is applied to identify the relevant processes in a trench migration experiment (van Rijn, 1987). A Tangent Linear Model (TLM) of the Telemac-2D/Sisyphe morphodynamic model (release 6.2) was generated using the AD-enabled NAG Fortran compiler. One single run of the TLM is required per variable input parameter and results are then combined to calculate the total uncertainty. The limits of the FOSM/AD method have been assessed by comparison with Monte Carlo (MC) simulations. Similar results were obtained assuming small standard deviation of the variable input parameters. Both settling velocity and grain size have been identified as the most sensitive input parameters and the uncertainty as measured by the standard deviation of the calculated bed evolution increases with time.
Energy Technology Data Exchange (ETDEWEB)
Becherif, M. [University of Technology of Belfort-Montbeliard, SeT-FCLab, UTBM, 90010 Belfort Cedex (France); Hissel, D. [University of Franche Comte, FEMTO-ST/FCLab, UMR CNRS 6174, 90010 Belfort Cedex (France); Gaagat, S. [Department of Chemical Engineering, IIT Guwahati, Assam (India); Wack, M. [SeT, UTBM, 90010 Belfort Cedex (France)
2010-10-01
The fuel cell is a complex system which is the centre of a lot of multidisciplinary research activities since it involves intricate application of various fields of study. The operation of a fuel cell depends on a wide range of parameters. The effect of one cannot be studied in isolation without disturbing the system which makes it very difficult to comprehend, analyze and predict various phenomena occurring in the fuel cell. In the current work, we present an equivalent electrical circuit of the pneumatics and fluidics in a fuel cell stack. The proposed model is based on the physical phenomena occurring inside fuel cell stack where we define the fluidic-electrical and pneumatic-electrical analogy. The effect of variation in temperature and relative humidity on the cell are considered in this model. The proposed model, according to the considered hypothesis, is a simple three order state space model which is suitable for the control purpose where a desired control structure can be formulated for high-end applications of the fuel cell as a subpart of a larger system, for instance, in hybrid propulsion of vehicles coupled with batteries and supercapacitors. Another key point of our work is the definition of the natural fuel cell stack energy function. The circuit analysis equations are presented and the simulated model is validated using the experimental data obtained using the fuel cell test bench available in Fuel Cell Laboratory, France. (author)
A role for low-order system dynamics models in urban health policy making.
Newell, Barry; Siri, José
2016-10-01
Cities are complex adaptive systems whose responses to policy initiatives emerge from feedback interactions between their parts. Urban policy makers must routinely deal with both detail and dynamic complexity, coupled with high levels of diversity, uncertainty and contingency. In such circumstances, it is difficult to generate reliable predictions of health-policy outcomes. In this paper we explore the potential for low-order system dynamics (LOSD) models to make a contribution towards meeting this challenge. By definition, LOSD models have few state variables (≤5), illustrate the non-linear effects caused by feedback and accumulation, and focus on endogenous dynamics generated within well-defined boundaries. We suggest that experience with LOSD models can help practitioners to develop an understanding of basic principles of system dynamics, giving them the ability to 'see with new eyes'. Because efforts to build a set of LOSD models can help a transdisciplinary group to develop a shared, coherent view of the problems that they seek to tackle, such models can also become the foundations of 'powerful ideas'. Powerful ideas are conceptual metaphors that provide the members of a policy-making group with the a priori shared context required for effective communication, the co-production of knowledge, and the collaborative development of effective public health policies. Copyright © 2016 Elsevier Ltd. All rights reserved.
Analysis of driver injury severity levels at multiple locations using ordered probit models.
Abdel-Aty, Mohamed
2003-01-01
The occurrence and outcome of traffic crashes have long been recognized as complex events involving interactions between many factors, including the roadway, driver, traffic characteristics, and the environment. This study is concerned with the outcome of the crash. Driver injury severity levels are analyzed using the ordered probit modeling methodology. Models were developed for roadway sections, signalized intersections, and toll plazas in Central Florida. All models showed the significance of driver's age, gender, seat belt use, point of impact, speed, and vehicle type on the injury severity level. Other variables were found significant only in specific cases. A driver's violation was significant in the case of signalized intersections. Alcohol, lighting conditions, and the existence of a horizontal curve affected the likelihood of injuries in the roadway sections' model. A variable specific to toll plazas, vehicles equipped with Electronic Toll Collection (ETC), had a positive effect on the probability of higher injury severity at toll plazas. Other variables that entered into some of the models were weather condition, area type, and some interaction factors. This study illustrates the similarities and the differences in the factors that affect injury severity between different locations.
Dynamical system analysis of a low-order tropical cyclone model
Directory of Open Access Journals (Sweden)
Daria Schönemann
2012-02-01
Full Text Available Tropical cyclone dynamics is investigated by means of a conceptual box model. The tropical cyclone (TC is divided into three regions, the eye, eyewall and ambient region. The model forms a low-order dynamical system of three ordinary differential equations. These are based on entropy budget equations comprising processes of surface enthalpy transfer, entropy advection, convection and radiative cooling. For tropical ocean parameter settings, the system possesses four non-trivial steady state solutions when the sea surface temperature (SST is above a critical value. Two steady states are unstable while the two remaining states are stable. Bifurcation diagrams provide an explanation why only finite-amplitude perturbations above a critical SST can transform into TCs. Besides SST, relative humidity of the ambient region forms an important model parameter. The surfaces that describe equilibria as a function of SST and relative humidity reveal a cusp-catastrophe where the two non-trivial equilibria split into four. Within the model regime of four equilibria, cyclogenesis becomes very unlikely due to the repelling and attracting effects of the two additional equilibria. The results are in qualitative agreement with observations and evince the relevance of the simple model approach to the dynamics of TC formation and its maximum potential intensity.
Becherif, M.; Hissel, D.; Gaagat, S.; Wack, M.
The fuel cell is a complex system which is the centre of a lot of multidisciplinary research activities since it involves intricate application of various fields of study. The operation of a fuel cell depends on a wide range of parameters. The effect of one cannot be studied in isolation without disturbing the system which makes it very difficult to comprehend, analyze and predict various phenomena occurring in the fuel cell. In the current work, we present an equivalent electrical circuit of the pneumatics and fluidics in a fuel cell stack. The proposed model is based on the physical phenomena occurring inside fuel cell stack where we define the fluidic-electrical and pneumatic-electrical analogy. The effect of variation in temperature and relative humidity on the cell are considered in this model. The proposed model, according to the considered hypothesis, is a simple three order state space model which is suitable for the control purpose where a desired control structure can be formulated for high-end applications of the fuel cell as a subpart of a larger system, for instance, in hybrid propulsion of vehicles coupled with batteries and supercapacitors. Another key point of our work is the definition of the natural fuel cell stack energy function. The circuit analysis equations are presented and the simulated model is validated using the experimental data obtained using the fuel cell test bench available in Fuel Cell Laboratory, France.
Directory of Open Access Journals (Sweden)
Jiyuan Zhang
2014-09-01
Full Text Available The application of headspace-solid phase microextraction (HS-SPME has been widely used in various fields as a simple and versatile method, yet challenging in quantification. In order to improve the reproducibility in quantification, a mathematical model with its root in psychological modeling and chemical reactor modeling was developed, describing the kinetic behavior of aroma active compounds extracted by SPME from two different food model systems, i.e., a semi-solid food and a liquid food. The model accounted for both adsorption and release of the analytes from SPME fiber, which occurred simultaneously but were counter-directed. The model had four parameters and their estimated values were found to be more reproducible than the direct measurement of the compounds themselves by instrumental analysis. With the relative standard deviations (RSD of each parameter less than 5% and root mean square error (RMSE less than 0.15, the model was proved to be a robust one in estimating the release of a wide range of low molecular weight acetates at three environmental temperatures i.e., 30, 40 and 60 °C. More insights of SPME behavior regarding the small molecule analytes were also obtained through the kinetic parameters and the model itself.
Effect of Adaptation Gain in Model Reference Adaptive Controlled Second Order System
Directory of Open Access Journals (Sweden)
R. K. Nema
2011-06-01
Full Text Available Adaptive control involves modifying the control law used by the controller to cope with the fact that the parameters of the system being controlled change drastically due to change in environmental conditions or in system itself. This technique is based on the fundamental characteristic of adaptation of living organism. The adaptive control process is one that continuously and automatically measures the dynamic behavior of plant, compares it with the desired output and uses the difference to vary adjustable system parameters or to generate an actuating signal in such a way so that optimal performance can be maintained regardless of system changes. Nature of adaptation mechanism for controlling the system performance is greatly affected by the value of adaptation gain. It is observed that for the lower order system wide range of adaptation gain can be used to study the performance of the system. As the order of the system increases the applicable range of adaptation gain becomes narrow. This paper deals with application of model reference adaptive control scheme to second order system with different values of adaptation gain. The rule which is used for this application is MIT rule. Simulation is done in MATLAB and simulink and the results are compared for varying adaptation mechanism due to variation in adaptation gain.
Nutrikinetic modeling reveals order of genistein phase II metabolites appearance in human plasma.
Smit, Suzanne; Szymańska, Ewa; Kunz, Iris; Gomez Roldan, Victoria; van Tilborg, Marcel W E M; Weber, Peter; Prudence, Kevin; van der Kloet, Frans M; van Duynhoven, John P M; Smilde, Age K; de Vos, Ric C H; Bendik, Igor
2014-11-01
Genistein from foods or supplements is metabolized by the gut microbiota and the human body, thereby releasing many different metabolites into systemic circulation. The order of their appearance in plasma and the possible influence of food format are still unknown. This study compared the nutrikinetic profiles of genistein metabolites. In a randomized cross-over trial, 12 healthy young volunteers were administered a single dose of 30 mg genistein provided as a genistein tablet, a genistein tablet in low fat milk, and soy milk containing genistein glycosides. A high mass resolution LC-LTQ-Orbitrap FTMS platform detected and quantified in human plasma: free genistein, seven of its phase-II metabolites and 15 gut-derived metabolites. Interestingly, a novel metabolite, genistein-4'-glucuronide-7-sulfate (G-4'G-7S) was identified. Nutrikinetic analysis using population-based modeling revealed the order of appearance of five genistein phase II metabolites in plasma: (1) genistein-4',7-diglucuronide, (2) genistein-7-sulfate, (3) genistein-4'-sulfate-7-glucuronide, (4) genistein-4'-glucuronide, and (5) genistein-7-glucuronide, independent of the food matrix. The conjugated genistein metabolites appear in a distinct order in human plasma. The specific early appearance of G-4',7-diG suggests a multistep formation process for the mono and hetero genistein conjugates, involving one or two deglucuronidation steps. © 2014 The Authors. Molecular Nutrition & Food Research published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Effect of third- and fourth-order moments on the modeling of unresolved transition arrays
Pain, J.-Ch.; Gilleron, F.; Bauche, J.; Bauche-Arnoult, C.
2009-12-01
The impact of the third (skewness) and fourth (kurtosis) reduced centered moments on the statistical modeling of E1 lines in complex atomic spectra is investigated through the use of Gram-Charlier, Normal Inverse Gaussian and Generalized Gaussian distributions. It is shown that the modeling of unresolved transition arrays with non-Gaussian distributions may reveal more detailed structures, due essentially to the large value of the kurtosis. In the present work, focus is put essentially on the Generalized Gaussian, the power of the argument in the exponential being constrained by the kurtosis value. The relevance of the new statistical line distribution is checked by comparisons with smoothed detailed line-by-line calculations and through the analysis of 2 p → 3 d transitions of recent laser or Z-pinch absorption measurements. The issue of calculating high-order moments is also discussed (Racah algebra, Jucys graphical method, semi-empirical approach…).
Vendor selection and order allocation using an integrated fuzzy mathematical programming model
Directory of Open Access Journals (Sweden)
Farzaneh Talebi
2015-09-01
Full Text Available In the context of supply chain management, supplier selection plays a key role in reaching desirable production planning. In today's competitive world, many enterprises have focused on selecting the appropriate suppliers in an attempt to reduce purchasing costs and improve quality products and services. Supplier selection is a multi-criteria decision problem, which includes different qualitative and quantitative criteria such as purchase cost, on time delivery, quality of service, etc. In this study, a fuzzy multi-objective mathematical programming model is presented to select appropriate supplier and assign desirable order to different supplies. The proposed model was implemented for an organization by considering 16 different scenarios and the results are compared with two other existing methods.
Exploring competing density order in the ionic Hubbard model with ultracold fermions.
Messer, Michael; Desbuquois, Rémi; Uehlinger, Thomas; Jotzu, Gregor; Huber, Sebastian; Greif, Daniel; Esslinger, Tilman
2015-09-11
We realize and study the ionic Hubbard model using an interacting two-component gas of fermionic atoms loaded into an optical lattice. The bipartite lattice has a honeycomb geometry with a staggered energy offset that explicitly breaks the inversion symmetry. Distinct density-ordered phases are identified using noise correlation measurements of the atomic momentum distribution. For weak interactions the geometry induces a charge density wave. For strong repulsive interactions we detect a strong suppression of doubly occupied sites, as expected for a Mott insulating state, and the externally broken inversion symmetry is not visible anymore in the density distribution. The local density distributions in different configurations are characterized by measuring the number of doubly occupied lattice sites as a function of interaction and energy offset. We further probe the excitations of the system using direction dependent modulation spectroscopy and discover a complex spectrum, which we compare with a theoretical model.
Energy Technology Data Exchange (ETDEWEB)
Degrande, Celine [CERN, Theory Division, Geneva 23 (Switzerland); Fuks, Benjamin [Sorbonne Universites, UPMC Univ. Paris 06, Paris (France); CNRS, Paris (France); Mawatari, Kentarou [Universite Grenoble-Alpes, Laboratoire de Physique Subatomique et de Cosmologie, Grenoble (France); Vrije Universiteit Brussel, Theoretische Natuurkunde and IIHE/ELEM, International Solvay Institutes, Brussels (Belgium); Mimasu, Ken [University of Sussex, Department of Physics and Astronomy, Brighton (United Kingdom); Universite catholique de Louvain, Centre for Cosmology, Particle Physics and Phenomenology (CP3), Louvain-la-Neuve (Belgium); Sanz, Veronica [University of Sussex, Department of Physics and Astronomy, Brighton (United Kingdom)
2017-04-15
We study the impact of dimension-six operators of the standard model effective field theory relevant for vector-boson fusion and associated Higgs boson production at the LHC. We present predictions at the next-to-leading order accuracy in QCD that include matching to parton showers and that rely on fully automated simulations. We show the importance of the subsequent reduction of the theoretical uncertainties in improving the possible discrimination between effective field theory and standard model results, and we demonstrate that the range of the Wilson coefficient values allowed by a global fit to LEP and LHC Run I data can be further constrained by LHC Run II future results. (orig.)
Degrande, Celine; Mawatari, Kentarou; Mimasu, Ken; Sanz, Veronica
2017-04-25
We study the impact of dimension-six operators of the standard model effective field theory relevant for vector-boson fusion and associated Higgs boson production at the LHC. We present predictions at the next-to-leading order accuracy in QCD that include matching to parton showers and that rely on fully automated simulations. We show the importance of the subsequent reduction of the theoretical uncertainties in improving the possible discrimination between effective field theory and standard model results, and we demonstrate that the range of the Wilson coefficient values allowed by a global fit to LEP and LHC Run I data can be further constrained by LHC Run II future results.
Doping dependence of ordered phases in the Hubbard-Holstein model
Mendl, Christian; Nowadnick, Elizabeth; Kung, Yvonne; Moritz, Brian; Johnston, Steven; Devereaux, Thomas
Complex phase diagrams of strongly correlated materials are often accessed by the addition or removal of carriers, for example the emergence of high-temperature superconductivity from a charge transfer insulating state in the cuprates, and the metal-insulator transition in the nickelates. In many cases, these doping-dependent transitions are closely linked to the competition between multiple phases of similar energy scales, e.g., charge-stripe and superconducting states in the cuprates. The Hubbard-Holstein model, which includes electron-electron and electron-phonon interactions, provides a framework to study competing phases. In this talk I will present determinant quantum Monte Carlo (DQMC) simulations of the Hubbard-Holstein model and use spin and charge susceptibilities and single-particle spectral functions to elucidate the doping evolution of the competition between spin and charge order.
Directory of Open Access Journals (Sweden)
M. Imron Mustajib
2010-01-01
Full Text Available This paper discusses the development of simultaneous optimization model to determine component tolerance of assembly product and plant for manufacturing processes by considering quality tolerance limits, and delivery time constraint to minimize total cost in collaboration environment of make-to-order manufacturing systems. Total cost of the system consists of manufacturing costs and quality loss costs as the tolerance function, operational costs for multi-plant manufacturing collaboration which includes: setup costs, material handling costs, operating costs of assembly, manual operations costs, and transportation costs. Formulation of the model developed uses mixed integer non linear programming as a method of solution search. In the numerical examples presented, the optimization process results an optimal solution. Optimal solution is not sensitive if the changes in quality tolerance constraint and delivery time constraint is not large. While the addition of an alternative plant for producing a component can changes the alternative plant selected
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Najeeb Alam Khan
2012-01-01
Full Text Available This paper suggests two component homotopy method to solve nonlinear fractional integrodifferential equations, namely, Volterra's population model. Padé approximation was effectively used in this method to capture the essential behavior of solutions for the mathematical model of accumulated effect of toxins on a population living in a closed system. The behavior of the solutions and the effects of different values of fractional-order α are indicated graphically. The study outlines significant features of this method as well as sheds some light on advantages of the method over the other. The results show that this method is very efficient, convenient, and can be adapted to fit a larger class of problems.
Reduced-Order Monte Carlo Modeling of Radiation Transport in Random Media
Olson, Aaron
The ability to perform radiation transport computations in stochastic media is essential for predictive capabilities in applications such as weather modeling, radiation shielding involving non-homogeneous materials, atmospheric radiation transport computations, and transport in plasma-air structures. Due to the random nature of such media, it is often not clear how to model or otherwise compute on many forms of stochastic media. Several approaches to evaluation of transport quantities for some stochastic media exist, though such approaches often either yield considerable error or are quite computationally expensive. We model stochastic media using the Karhunen-Loeve (KL) expansion, seek to improve efficiency through use of stochastic collocation (SC), and provide higher-order information of output values using the polynomial chaos expansion (PCE). We study and demonstrate method convergence and apply the new methods to both spatially continuous and spatially discontinuous stochastic media. New methods are shown to produce accurate solutions for reasonable computational cost for several problem when compared with existing solution methods. Spatially random media are modeled using transformations of the Gaussian-distributed KL expansion-continuous random media with a lognormal transformation and discontinuous random media with a Nataf transformation. Each transformation preserves second-order statistics for the quantity-atom density or material index, respectively-being modeled. The Nystrom method facilitates numerical solution of the KL eigenvalues and eigenvectors, and a variety of methods are investigated for sampling KL eigenfunctions as a function of solved eigenvectors. The infinite KL expansion is truncated to a finite number of terms each containing a random variable, and material realizations are created by either randomly or deterministically sampling from the random variables. Deterministic sampling is performed with either isotropic or anisotropic
First-order phase transition in the bosonic Kondo-Hubbard model
Foss-Feig, Michael; Rey, Ana Maria
2011-05-01
Recent experimental progress in populating the excited bands of an optical lattice gives rise to the exciting possibility of simulating multi-band condensed matter Hamiltonians. The Kondo lattice model (KLM), in which tightly bound electrons act as spinful scattering centers for electrons in a conduction band, is a typical example of the type of model one would like to simulate. In the KLM, the orbital (band) degree of freedom gives rise to a complex phase diagram, which includes magnetically ordered states, a heavy Fermi liquid, and unconventional superconductors. Here we consider a version of the KLM first proposed in, in which the electrons are replaced by spin-1/2 bosons, which in turn are realized physically by bosonic alkali atoms in an optical lattice. As we demonstrate, the interplay between spin, charge, and orbital degrees of freedom can drive the Mott insulator to superfluid transition to be first order, without explicit breaking of SU(2) symmetry. The observability of such behavior in the context of current experiments will also be discussed.
Wind farm density and harvested power in very large wind farms: A low-order model
Cortina, G.; Sharma, V.; Calaf, M.
2017-07-01
In this work we create new understanding of wind turbine wakes recovery process as a function of wind farm density using large-eddy simulations of an atmospheric boundary layer diurnal cycle. Simulations are forced with a constant geostrophic wind and a time varying surface temperature extracted from a selected period of the Cooperative Atmospheric Surface Exchange Study field experiment. Wind turbines are represented using the actuator disk model with rotation and yaw alignment. A control volume analysis around each turbine has been used to evaluate wind turbine wake recovery and corresponding harvested power. Results confirm the existence of two dominant recovery mechanisms, advection and flux of mean kinetic energy, which are modulated by the background thermal stratification. For the low-density arrangements advection dominates, while for the highly loaded wind farms the mean kinetic energy recovers through fluxes of mean kinetic energy. For those cases in between, a smooth balance of both mechanisms exists. From the results, a low-order model for the wind farms' harvested power as a function of thermal stratification and wind farm density has been developed, which has the potential to be used as an order-of-magnitude assessment tool.
Pototzky, Anthony S.
2010-01-01
A methodology is described for generating first-order plant equations of motion for aeroelastic and aeroservoelastic applications. The description begins with the process of generating data files representing specialized mode-shapes, such as rigid-body and control surface modes, using both PATRAN and NASTRAN analysis. NASTRAN executes the 146 solution sequence using numerous Direct Matrix Abstraction Program (DMAP) calls to import the mode-shape files and to perform the aeroelastic response analysis. The aeroelastic response analysis calculates and extracts structural frequencies, generalized masses, frequency-dependent generalized aerodynamic force (GAF) coefficients, sensor deflections and load coefficients data as text-formatted data files. The data files are then re-sequenced and re-formatted using a custom written FORTRAN program. The text-formatted data files are stored and coefficients for s-plane equations are fitted to the frequency-dependent GAF coefficients using two Interactions of Structures, Aerodynamics and Controls (ISAC) programs. With tabular files from stored data created by ISAC, MATLAB generates the first-order aeroservoelastic plant equations of motion. These equations include control-surface actuator, turbulence, sensor and load modeling. Altitude varying root-locus plot and PSD plot results for a model of the F-18 aircraft are presented to demonstrate the capability.
Spatial and model-order based reactor signal analysis methodology for BWR core stability evaluation
Energy Technology Data Exchange (ETDEWEB)
Dokhane, A. [Paul Scherrer Institute, Laboratory for Reactor Physics and Systems Behavior, CH-5232 Villigen PSI (Switzerland) and Ecole Polytechnique Federale de Lausanne, CH-1015 Lausanne (Switzerland)]. E-mail: adokhane@ksu.edu.sa; Ferroukhi, H. [Paul Scherrer Institute, Laboratory for Reactor Physics and Systems Behavior, CH-5232 Villigen PSI (Switzerland)]. E-mail: hakim.ferroukhi@psi.ch; Zimmermann, M.A. [Paul Scherrer Institute, Laboratory for Reactor Physics and Systems Behavior, CH-5232 Villigen PSI (Switzerland); Aguirre, C. [Kernkraftwerk Leibstadt, CH-5325 Leibstadt (Switzerland)
2006-11-15
A new methodology for the boiling water reactor core stability evaluation from measured noise signals has been recently developed and adopted at the Paul Scherrer Institut (PSI). This methodology consists in a general reactor noise analysis where as much as possible information recorded during the tests is investigated prior to determining core representative stability parameters, i.e. the decay ratio (DR) and the resonance frequency, along with an associated estimate of the uncertainty range. A central part in this approach is that the evaluation of the core stability parameters is performed not only for a few but for ALL recorded neutron flux signals, allowing thereby the assessment of signal-related uncertainties. In addition, for each signal, three different model-order optimization methods are systematically employed to take into account the sensitivity upon the model-order. The current methodology is then applied to the evaluation of the core stability measurements performed at the Leibstadt NPP, Switzerland, during cycles 10, 13 and 19. The results show that as the core becomes very stable, the method-related uncertainty becomes the major contributor to the overall uncertainty range while for intermediate DR values, the signal-related uncertainty becomes dominant. However, as the core stability deteriorates, the method-related and signal-related spreads have similar contributions to the overall uncertainty, and both are found to be small. The PSI methodology identifies the origin of the different contributions to the uncertainty. Furthermore, in order to assess the results obtained with the current methodology, a comparative study is for completeness carried out with respect to results from previously developed and applied procedures. The results show a good agreement between the current method and the other methods.
A Deep Learning based Approach to Reduced Order Modeling of Fluids using LSTM Neural Networks
Mohan, Arvind; Gaitonde, Datta
2017-11-01
Reduced Order Modeling (ROM) can be used as surrogates to prohibitively expensive simulations to model flow behavior for long time periods. ROM is predicated on extracting dominant spatio-temporal features of the flow from CFD or experimental datasets. We explore ROM development with a deep learning approach, which comprises of learning functional relationships between different variables in large datasets for predictive modeling. Although deep learning and related artificial intelligence based predictive modeling techniques have shown varied success in other fields, such approaches are in their initial stages of application to fluid dynamics. Here, we explore the application of the Long Short Term Memory (LSTM) neural network to sequential data, specifically to predict the time coefficients of Proper Orthogonal Decomposition (POD) modes of the flow for future timesteps, by training it on data at previous timesteps. The approach is demonstrated by constructing ROMs of several canonical flows. Additionally, we show that statistical estimates of stationarity in the training data can indicate a priori how amenable a given flow-field is to this approach. Finally, the potential and limitations of deep learning based ROM approaches will be elucidated and further developments discussed.
A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint
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Xixing Li
2017-01-01
Full Text Available The mode of production in the modern manufacturing enterprise mainly prefers to MTO (Make-to-Order; how to reasonably arrange the production plan has become a very common and urgent problem for enterprises’ managers to improve inner production reformation in the competitive market environment. In this paper, a mathematical model of production planning is proposed to maximize the profit with capacity constraint. Four kinds of cost factors (material cost, process cost, delay cost, and facility occupy cost are considered in the proposed model. Different factors not only result in different profit but also result in different satisfaction degrees of customers. Particularly, the delay cost and facility occupy cost cannot reach the minimum at the same time; the two objectives are interactional. This paper presents a mathematical model based on the actual production process of a foundry flow shop. An improved genetic algorithm (IGA is proposed to solve the biobjective problem of the model. Also, the gene encoding and decoding, the definition of fitness function, and genetic operators have been illustrated. In addition, the proposed algorithm is used to solve the production planning problem of a foundry flow shop in a casting enterprise. And comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.
Static aeroelastic analysis including geometric nonlinearities based on reduced order model
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Changchuan Xie
2017-04-01
Full Text Available This paper describes a method proposed for modeling large deflection of aircraft in nonlinear aeroelastic analysis by developing reduced order model (ROM. The method is applied for solving the static aeroelastic and static aeroelastic trim problems of flexible aircraft containing geometric nonlinearities; meanwhile, the non-planar effects of aerodynamics and follower force effect have been considered. ROMs are computational inexpensive mathematical representations compared to traditional nonlinear finite element method (FEM especially in aeroelastic solutions. The approach for structure modeling presented here is on the basis of combined modal/finite element (MFE method that characterizes the stiffness nonlinearities and we apply that structure modeling method as ROM to aeroelastic analysis. Moreover, the non-planar aerodynamic force is computed by the non-planar vortex lattice method (VLM. Structure and aerodynamics can be coupled with the surface spline method. The results show that both of the static aeroelastic analysis and trim analysis of aircraft based on structure ROM can achieve a good agreement compared to analysis based on the FEM and experimental result.