EPMLR: sequence-based linear B-cell epitope prediction method using multiple linear regression.

Science.gov (United States)

Lian, Yao; Ge, Meng; Pan, Xian-Ming

2014-12-19

B-cell epitopes have been studied extensively due to their immunological applications, such as peptide-based vaccine development, antibody production, and disease diagnosis and therapy. Despite several decades of research, the accurate prediction of linear B-cell epitopes has remained a challenging task. In this work, based on the antigen's primary sequence information, a novel linear B-cell epitope prediction model was developed using the multiple linear regression (MLR). A 10-fold cross-validation test on a large non-redundant dataset was performed to evaluate the performance of our model. To alleviate the problem caused by the noise of negative dataset, 300 experiments utilizing 300 sub-datasets were performed. We achieved overall sensitivity of 81.8%, precision of 64.1% and area under the receiver operating characteristic curve (AUC) of 0.728. We have presented a reliable method for the identification of linear B cell epitope using antigen's primary sequence information. Moreover, a web server EPMLR has been developed for linear B-cell epitope prediction: http://www.bioinfo.tsinghua.edu.cn/epitope/EPMLR/ .

An online re-linearization scheme suited for Model Predictive and Linear Quadratic Control

DEFF Research Database (Denmark)

Henriksen, Lars Christian; Poulsen, Niels Kjølstad

This technical note documents the equations for primal-dual interior-point quadratic programming problem solver used for MPC. The algorithm exploits the special structure of the MPC problem and is able to reduce the computational burden such that the computational burden scales with prediction...... horizon length in a linear way rather than cubic, which would be the case if the structure was not exploited. It is also shown how models used for design of model-based controllers, e.g. linear quadratic and model predictive, can be linearized both at equilibrium and non-equilibrium points, making...

Scaling laws for e+/e- linear colliders

International Nuclear Information System (INIS)

Delahaye, J.P.; Guignard, G.; Raubenheimer, T.; Wilson, I.

1999-01-01

Design studies of a future TeV e + e - Linear Collider (TLC) are presently being made by five major laboratories within the framework of a world-wide collaboration. A figure of merit is defined which enables an objective comparison of these different designs. This figure of merit is shown to depend only on a small number of parameters. General scaling laws for the main beam parameters and linac parameters are derived and prove to be very effective when used as guidelines to optimize the linear collider design. By adopting appropriate parameters for beam stability, the figure of merit becomes nearly independent of accelerating gradient and RF frequency of the accelerating structures. In spite of the strong dependence of the wake fields with frequency, the single-bunch emittance blow-up during acceleration along the linac is also shown to be independent of the RF frequency when using equivalent trajectory correction schemes. In this situation, beam acceleration using high-frequency structures becomes very advantageous because it enables high accelerating fields to be obtained, which reduces the overall length and consequently the total cost of the linac. (Copyright (c) 1999 Elsevier Science B.V., Amsterdam. All rights reserved.)

Comparison between isotropic linear-elastic law and isotropic hyperelastic law in the finite element modeling of the brachial plexus.

Science.gov (United States)

Perruisseau-Carrier, A; Bahlouli, N; Bierry, G; Vernet, P; Facca, S; Liverneaux, P

2017-12-01

Augmented reality could help the identification of nerve structures in brachial plexus surgery. The goal of this study was to determine which law of mechanical behavior was more adapted by comparing the results of Hooke's isotropic linear elastic law to those of Ogden's isotropic hyperelastic law, applied to a biomechanical model of the brachial plexus. A model of finite elements was created using the ABAQUS ® from a 3D model of the brachial plexus acquired by segmentation and meshing of MRI images at 0°, 45° and 135° of shoulder abduction of a healthy subject. The offset between the reconstructed model and the deformed model was evaluated quantitatively by the Hausdorff distance and qualitatively by the identification of 3 anatomical landmarks. In every case the Hausdorff distance was shorter with Ogden's law compared to Hooke's law. On a qualitative aspect, the model deformed by Ogden's law followed the concavity of the reconstructed model whereas the model deformed by Hooke's law remained convex. In conclusion, the results of this study demonstrate that the behavior of Ogden's isotropic hyperelastic mechanical model was more adapted to the modeling of the deformations of the brachial plexus. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Modeling and analysis of linear hyperbolic systems of balance laws

CERN Document Server

Bartecki, Krzysztof

2016-01-01

This monograph focuses on the mathematical modeling of distributed parameter systems in which mass/energy transport or wave propagation phenomena occur and which are described by partial differential equations of hyperbolic type. The case of linear (or linearized) 2 x 2 hyperbolic systems of balance laws is considered, i.e., systems described by two coupled linear partial differential equations with two variables representing physical quantities, depending on both time and one-dimensional spatial variable. Based on practical examples of a double-pipe heat exchanger and a transportation pipeline, two typical configurations of boundary input signals are analyzed: collocated, wherein both signals affect the system at the same spatial point, and anti-collocated, in which the input signals are applied to the two different end points of the system. The results of this book emerge from the practical experience of the author gained during his studies conducted in the experimental installation of a heat exchange cente...

The Use of Linear Programming for Prediction.

Science.gov (United States)

Schnittjer, Carl J.

The purpose of the study was to develop a linear programming model to be used for prediction, test the accuracy of the predictions, and compare the accuracy with that produced by curvilinear multiple regression analysis. (Author)

On the structure on non-local conservation laws in the two-dimensional non-linear sigma-model

International Nuclear Information System (INIS)

Zamolodchikov, Al.B.

1978-01-01

The non-local conserved charges are supposed to satisfy a special multiplicative law in the space of asymptotic states of the non-linear sigma-model. This supposition leads to factorization equations for two-particle scattering matrix elements and determines to some extent the action of these charges in the asymptotic space. Their conservation turns out to be consistent with the factorized S-matrix of the non-linear sigma-model. It is shown also that the factorized sine-Gordon S-matrix is consistent with a similar family of conservation laws

Predicting birth weight with conditionally linear transformation models.

Science.gov (United States)

Möst, Lisa; Schmid, Matthias; Faschingbauer, Florian; Hothorn, Torsten

2016-12-01

Low and high birth weight (BW) are important risk factors for neonatal morbidity and mortality. Gynecologists must therefore accurately predict BW before delivery. Most prediction formulas for BW are based on prenatal ultrasound measurements carried out within one week prior to birth. Although successfully used in clinical practice, these formulas focus on point predictions of BW but do not systematically quantify uncertainty of the predictions, i.e. they result in estimates of the conditional mean of BW but do not deliver prediction intervals. To overcome this problem, we introduce conditionally linear transformation models (CLTMs) to predict BW. Instead of focusing only on the conditional mean, CLTMs model the whole conditional distribution function of BW given prenatal ultrasound parameters. Consequently, the CLTM approach delivers both point predictions of BW and fetus-specific prediction intervals. Prediction intervals constitute an easy-to-interpret measure of prediction accuracy and allow identification of fetuses subject to high prediction uncertainty. Using a data set of 8712 deliveries at the Perinatal Centre at the University Clinic Erlangen (Germany), we analyzed variants of CLTMs and compared them to standard linear regression estimation techniques used in the past and to quantile regression approaches. The best-performing CLTM variant was competitive with quantile regression and linear regression approaches in terms of conditional coverage and average length of the prediction intervals. We propose that CLTMs be used because they are able to account for possible heteroscedasticity, kurtosis, and skewness of the distribution of BWs. © The Author(s) 2014.

Neural Generalized Predictive Control of a non-linear Process

DEFF Research Database (Denmark)

Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

1998-01-01

The use of neural network in non-linear control is made difficult by the fact the stability and robustness is not guaranteed and that the implementation in real time is non-trivial. In this paper we introduce a predictive controller based on a neural network model which has promising stability qu...... detail and discuss the implementation difficulties. The neural generalized predictive controller is tested on a pneumatic servo sys-tem.......The use of neural network in non-linear control is made difficult by the fact the stability and robustness is not guaranteed and that the implementation in real time is non-trivial. In this paper we introduce a predictive controller based on a neural network model which has promising stability...... qualities. The controller is a non-linear version of the well-known generalized predictive controller developed in linear control theory. It involves minimization of a cost function which in the present case has to be done numerically. Therefore, we develop the numerical algorithms necessary in substantial...

Implementation of neural network based non-linear predictive

DEFF Research Database (Denmark)

Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

1998-01-01

The paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems including open loop unstable and non-minimum phase systems, but has also been proposed extended for the control of non......-linear systems. GPC is model-based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient Quasi......-Newton optimization algorithm. The performance is demonstrated on a pneumatic servo system....

Non-linear aeroelastic prediction for aircraft applications

Science.gov (United States)

de C. Henshaw, M. J.; Badcock, K. J.; Vio, G. A.; Allen, C. B.; Chamberlain, J.; Kaynes, I.; Dimitriadis, G.; Cooper, J. E.; Woodgate, M. A.; Rampurawala, A. M.; Jones, D.; Fenwick, C.; Gaitonde, A. L.; Taylor, N. V.; Amor, D. S.; Eccles, T. A.; Denley, C. J.

2007-05-01

Current industrial practice for the prediction and analysis of flutter relies heavily on linear methods and this has led to overly conservative design and envelope restrictions for aircraft. Although the methods have served the industry well, it is clear that for a number of reasons the inclusion of non-linearity in the mathematical and computational aeroelastic prediction tools is highly desirable. The increase in available and affordable computational resources, together with major advances in algorithms, mean that non-linear aeroelastic tools are now viable within the aircraft design and qualification environment. The Partnership for Unsteady Methods in Aerodynamics (PUMA) Defence and Aerospace Research Partnership (DARP) was sponsored in 2002 to conduct research into non-linear aeroelastic prediction methods and an academic, industry, and government consortium collaborated to address the following objectives: To develop useable methodologies to model and predict non-linear aeroelastic behaviour of complete aircraft. To evaluate the methodologies on real aircraft problems. To investigate the effect of non-linearities on aeroelastic behaviour and to determine which have the greatest effect on the flutter qualification process. These aims have been very effectively met during the course of the programme and the research outputs include: New methods available to industry for use in the flutter prediction process, together with the appropriate coaching of industry engineers. Interesting results in both linear and non-linear aeroelastics, with comprehensive comparison of methods and approaches for challenging problems. Additional embryonic techniques that, with further research, will further improve aeroelastics capability. This paper describes the methods that have been developed and how they are deployable within the industrial environment. We present a thorough review of the PUMA aeroelastics programme together with a comprehensive review of the relevant research

Four-dimensional Hooke's law can encompass linear elasticity and inertia

International Nuclear Information System (INIS)

Antoci, S.; Mihich, L.

1999-01-01

The question is examined whether the formally straightforward extension of Hooke's time-honoured stress-strain relation to the four dimensions of special and of general relativity can make physical sense. The four-dimensional Hooke law is found able to account for the inertia of matter; in the flat-space, slow-motion approximation the field equations for the displacement four-vector field ξ i can encompass both linear elasticity and inertia. In this limit one just recovers the equations of motion of the classical theory of elasticity

LINEAR2007, Linear-Linear Interpolation of ENDF Format Cross-Sections

International Nuclear Information System (INIS)

2007-01-01

1 - Description of program or function: LINEAR converts evaluated cross sections in the ENDF/B format into a tabular form that is subject to linear-linear interpolation in energy and cross section. The code also thins tables of cross sections already in that form. Codes used subsequently need thus to consider only linear-linear data. IAEA1311/15: This version include the updates up to January 30, 2007. Changes in ENDF/B-VII Format and procedures, as well as the evaluations themselves, make it impossible for versions of the ENDF/B pre-processing codes earlier than PREPRO 2007 (2007 Version) to accurately process current ENDF/B-VII evaluations. The present code can handle all existing ENDF/B-VI evaluations through release 8, which will be the last release of ENDF/B-VI. Modifications from previous versions: - Linear VERS. 2007-1 (JAN. 2007): checked against all ENDF/B-VII; increased page size from 60,000 to 600,000 points 2 - Method of solution: Each section of data is considered separately. Each section of File 3, 23, and 27 data consists of a table of cross section versus energy with any of five interpolation laws. LINEAR will replace each section with a new table of energy versus cross section data in which the interpolation law is always linear in energy and cross section. The histogram (constant cross section between two energies) interpolation law is converted to linear-linear by substituting two points for each initial point. The linear-linear is not altered. For the log-linear, linear-log and log- log laws, the cross section data are converted to linear by an interval halving algorithm. Each interval is divided in half until the value at the middle of the interval can be approximated by linear-linear interpolation to within a given accuracy. The LINEAR program uses a multipoint fractional error thinning algorithm to minimize the size of each cross section table

Predicting law enforcement officer job performance with the Personality Assessment Inventory.

Science.gov (United States)

Lowmaster, Sara E; Morey, Leslie C

2012-01-01

This study examined the descriptive and predictive characteristics of the Personality Assessment Inventory (PAI; Morey, 1991) in a sample of 85 law enforcement officer candidates. Descriptive results indicate that mean PAI full-scale and subscale scores are consistently lower than normative community sample scores, with some exceptions noted typically associated with defensive responding. Predictive validity was examined by relating PAI full-scale and subscale scores to supervisor ratings in the areas of job performance, integrity problems, and abuse of disability status. Modest correlations were observed for all domains; however, predictive validity was moderated by defensive response style, with greater predictive validity observed among less defensive responders. These results suggest that the PAI's full scales and subscales are able to predict law enforcement officers' performance, but their utility is appreciably improved when taken in the context of indicators of defensive responding.

Fall with linear drag and Wien's displacement law: approximate solution and Lambert function

International Nuclear Information System (INIS)

Vial, Alexandre

2012-01-01

We present an approximate solution for the downward time of travel in the case of a mass falling with a linear drag force. We show how a quasi-analytical solution implying the Lambert function can be found. We also show that solving the previous problem is equivalent to the search for Wien's displacement law. These results can be of interest for undergraduate students, as they show that some transcendental equations found in physics may be solved without purely numerical methods. Moreover, as will be seen in the case of Wien's displacement law, solutions based on series expansion can be very accurate even with few terms. (paper)

Deviations from Vegard’s law in ternary III-V alloys

KAUST Repository

Murphy, S. T.

2010-08-03

Vegard’s law states that, at a constant temperature, the volume of an alloy can be determined from a linear interpolation of its constituent’s volumes. Deviations from this description occur such that volumes are both greater and smaller than the linear relationship would predict. Here we use special quasirandom structures and density functional theory to investigate such deviations for MxN1−xAs ternary alloys, where M and N are group III species (B, Al, Ga, and In). Our simulations predict a tendency, with the exception of AlxGa1−xAs, for the volume of the ternary alloys to be smaller than that determined from the linear interpolation of the volumes of the MAs and BAs binary alloys. Importantly, we establish a simple relationship linking the relative size of the group III atoms in the alloy and the predicted magnitude of the deviation from Vegard’s law.

Implementation of neural network based non-linear predictive control

DEFF Research Database (Denmark)

Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

1999-01-01

This paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems, including open-loop unstable and non-minimum phase systems, but has also been proposed to be extended for the control...... of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis...... on an efficient quasi-Newton algorithm. The performance is demonstrated on a pneumatic servo system....

Linear regression crash prediction models : issues and proposed solutions.

Science.gov (United States)

2010-05-01

The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...

A Review of Darcy's Law: Limitations and Alternatives for Predicting Solute Transport

Science.gov (United States)

Steenhuis, Tammo; Kung, K.-J. Sam; Jaynes, Dan; Helling, Charles S.; Gish, Tim; Kladivko, Eileen

2016-04-01

Darcy's Law that was derived originally empirically 160 years ago, has been used successfully in calculating the (Darcy) flux in porous media throughout the world. However, field and laboratory experiments have demonstrated that the Darcy flux employed in the convective disperse equation could only successfully predict solute transport under two conditions: (1) uniformly or densely packed porous media; and (2) field soils under relatively dry condition. Employing the Darcy flux for solute transport in porous media with preferential flow pathways was problematic. In this paper we examine the theoretical background behind these field and laboratory observations and then provide an alternative to predict solute movement. By examining the characteristics of the momentum conservation principles on which Darcy's law is based, we show under what conditions Darcy flux can predict solute transport in porous media of various complexity. We find that, based on several case studies with capillary pores, Darcy's Law inherently merges momentum and in that way erases information on pore-scale velocities. For that reason the Darcy flux cannot predict flow in media with preferential flow conduits where individual pore velocities are essential in predicting the shape of the breakthrough curve and especially "the early arrival" of solutes. To overcome the limitations of the assumption in Darcy's law, we use Jury's conceptualization and employ the measured chemical breakthrough curve as input to characterize the impact of individual preferential flow pathways on chemical transport. Specifically, we discuss how best to take advantage of Jury's conceptualization to extract the pore-scale flow velocity to accurately predict chemical transport through soils with preferential flow pathways.

Second Law of Thermodynamics Applied to Metabolic Networks

Science.gov (United States)

Nigam, R.; Liang, S.

2003-01-01

We present a simple algorithm based on linear programming, that combines Kirchoff's flux and potential laws and applies them to metabolic networks to predict thermodynamically feasible reaction fluxes. These law's represent mass conservation and energy feasibility that are widely used in electrical circuit analysis. Formulating the Kirchoff's potential law around a reaction loop in terms of the null space of the stoichiometric matrix leads to a simple representation of the law of entropy that can be readily incorporated into the traditional flux balance analysis without resorting to non-linear optimization. Our technique is new as it can easily check the fluxes got by applying flux balance analysis for thermodynamic feasibility and modify them if they are infeasible so that they satisfy the law of entropy. We illustrate our method by applying it to the network dealing with the central metabolism of Escherichia coli. Due to its simplicity this algorithm will be useful in studying large scale complex metabolic networks in the cell of different organisms.

Kinematic Hardening: Characterization, Modeling and Impact on Springback Prediction

International Nuclear Information System (INIS)

Alves, J. L.; Bouvier, S.; Jomaa, M.; Billardon, R.; Oliveira, M. C.; Menezes, L. F.

2007-01-01

The constitutive modeling of the materials' mechanical behavior, usually carried out using a phenomenological constitutive model, i.e., a yield criterion associated to the isotropic and kinematic hardening laws, is of paramount importance in the FEM simulation of the sheet metal forming processes, as well as in the springback prediction. Among others, the kinematic behavior of the yield surface plays an essential role, since it is indispensable to describe the Bauschinger effect, i.e., the materials' answer to the multiple tension-compression cycles to which material points are submitted during the forming process. Several laws are usually used to model and describe the kinematic hardening, namely: a) the Prager's law, which describes a linear evolution of the kinematic hardening with the plastic strain rate tensor b) the Frederick-Armstrong non-linear kinematic hardening, basically a non-linear law with saturation; and c) a more advanced physically-based law, similar to the previous one but sensitive to the strain path changes. In the present paper a mixed kinematic hardening law (linear + non-linear behavior) is proposed and its implementation into a static fully-implicit FE code is described. The material parameters identification for sheet metals using different strategies, and the classical Bauschinger loading tests (i.e. in-plane forward and reverse monotonic loading), are addressed, and their impact on springback prediction evaluated. Some numerical results concerning the springback prediction of the Numisheet'05 Benchmark no. 3 are briefly presented to emphasize the importance of a correct modeling and identification of the kinematic hardening behavior

Friction laws at the nanoscale.

Science.gov (United States)

Mo, Yifei; Turner, Kevin T; Szlufarska, Izabela

2009-02-26

Macroscopic laws of friction do not generally apply to nanoscale contacts. Although continuum mechanics models have been predicted to break down at the nanoscale, they continue to be applied for lack of a better theory. An understanding of how friction force depends on applied load and contact area at these scales is essential for the design of miniaturized devices with optimal mechanical performance. Here we use large-scale molecular dynamics simulations with realistic force fields to establish friction laws in dry nanoscale contacts. We show that friction force depends linearly on the number of atoms that chemically interact across the contact. By defining the contact area as being proportional to this number of interacting atoms, we show that the macroscopically observed linear relationship between friction force and contact area can be extended to the nanoscale. Our model predicts that as the adhesion between the contacting surfaces is reduced, a transition takes place from nonlinear to linear dependence of friction force on load. This transition is consistent with the results of several nanoscale friction experiments. We demonstrate that the breakdown of continuum mechanics can be understood as a result of the rough (multi-asperity) nature of the contact, and show that roughness theories of friction can be applied at the nanoscale.

A uniform law for convergence to the local times of linear fractional stable motions

OpenAIRE

Duffy, James A.

2016-01-01

We provide a uniform law for the weak convergence of additive functionals of partial sum processes to the local times of linear fractional stable motions, in a setting sufficiently general for statistical applications. Our results are fundamental to the analysis of the global properties of nonparametric estimators of nonlinear statistical models that involve such processes as covariates.

Fast Algorithms for High-Order Sparse Linear Prediction with Applications to Speech Processing

DEFF Research Database (Denmark)

Jensen, Tobias Lindstrøm; Giacobello, Daniele; van Waterschoot, Toon

2016-01-01

In speech processing applications, imposing sparsity constraints on high-order linear prediction coefficients and prediction residuals has proven successful in overcoming some of the limitation of conventional linear predictive modeling. However, this modeling scheme, named sparse linear prediction...... problem with lower accuracy than in previous work. In the experimental analysis, we clearly show that a solution with lower accuracy can achieve approximately the same performance as a high accuracy solution both objectively, in terms of prediction gain, as well as with perceptual relevant measures, when...... evaluated in a speech reconstruction application....

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

Science.gov (United States)

Wang, Hsin-Wei; Pai, Tun-Wen

2014-01-01

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

Validation of Individual Non-Linear Predictive Pharmacokinetic ...

African Journals Online (AJOL)

3Department of Veterinary Medicine, Faculty of Agriculture, University of Novi Sad, Novi Sad, Republic of Serbia ... Purpose: To evaluate the predictive performance of phenytoin multiple dosing non-linear pharmacokinetic ... status epilepticus affects an estimated 152,000 ..... causal factors, i.e., infection, inflammation, tissue.

Linear Prediction Using Refined Autocorrelation Function

Directory of Open Access Journals (Sweden)

M. Shahidur Rahman

2007-07-01

Full Text Available This paper proposes a new technique for improving the performance of linear prediction analysis by utilizing a refined version of the autocorrelation function. Problems in analyzing voiced speech using linear prediction occur often due to the harmonic structure of the excitation source, which causes the autocorrelation function to be an aliased version of that of the vocal tract impulse response. To estimate the vocal tract characteristics accurately, however, the effect of aliasing must be eliminated. In this paper, we employ homomorphic deconvolution technique in the autocorrelation domain to eliminate the aliasing effect occurred due to periodicity. The resulted autocorrelation function of the vocal tract impulse response is found to produce significant improvement in estimating formant frequencies. The accuracy of formant estimation is verified on synthetic vowels for a wide range of pitch frequencies typical for male and female speakers. The validity of the proposed method is also illustrated by inspecting the spectral envelopes of natural speech spoken by high-pitched female speaker. The synthesis filter obtained by the current method is guaranteed to be stable, which makes the method superior to many of its alternatives.

Non-linear flow law of rockglacier creep determined from geomorphological observations: A case study from the Murtèl rockglacier (Engadin, SE Switzerland)

Science.gov (United States)

Frehner, Marcel; Amschwand, Dominik; Gärtner-Roer, Isabelle

2016-04-01

Rockglaciers consist of unconsolidated rock fragments (silt/sand-rock boulders) with interstitial ice; hence their creep behavior (i.e., rheology) may deviate from the simple and well-known flow-laws for pure ice. Here we constrain the non-linear viscous flow law that governs rockglacier creep based on geomorphological observations. We use the Murtèl rockglacier (upper Engadin valley, SE Switzerland) as a case study, for which high-resolution digital elevation models (DEM), time-lapse borehole deformation data, and geophysical soundings exist that reveal the exterior and interior architecture and dynamics of the landform. Rockglaciers often feature a prominent furrow-and-ridge topography. For the Murtèl rockglacier, Frehner et al. (2015) reproduced the wavelength, amplitude, and distribution of the furrow-and-ridge morphology using a linear viscous (Newtonian) flow model. Arenson et al. (2002) presented borehole deformation data, which highlight the basal shear zone at about 30 m depth and a curved deformation profile above the shear zone. Similarly, the furrow-and-ridge morphology also exhibits a curved geometry in map view. Hence, the surface morphology and the borehole deformation data together describe a curved 3D geometry, which is close to, but not quite parabolic. We use a high-resolution DEM to quantify the curved geometry of the Murtèl furrow-and-ridge morphology. We then calculate theoretical 3D flow geometries using different non-linear viscous flow laws. By comparing them to the measured curved 3D geometry (i.e., both surface morphology and borehole deformation data), we can determine the most adequate flow-law that fits the natural data best. Linear viscous models result in perfectly parabolic flow geometries; non-linear creep leads to localized deformation at the sides and bottom of the rockglacier while the deformation in the interior and top are less intense. In other words, non-linear creep results in non-parabolic flow geometries. Both the

Genomic prediction based on data from three layer lines: a comparison between linear methods

NARCIS (Netherlands)

Calus, M.P.L.; Huang, H.; Vereijken, J.; Visscher, J.; Napel, ten J.; Windig, J.J.

2014-01-01

Background The prediction accuracy of several linear genomic prediction models, which have previously been used for within-line genomic prediction, was evaluated for multi-line genomic prediction. Methods Compared to a conventional BLUP (best linear unbiased prediction) model using pedigree data, we

Genomic prediction based on data from three layer lines using non-linear regression models.

Science.gov (United States)

Huang, Heyun; Windig, Jack J; Vereijken, Addie; Calus, Mario P L

2014-11-06

Most studies on genomic prediction with reference populations that include multiple lines or breeds have used linear models. Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods. In an attempt to alleviate potential discrepancies between assumptions of linear models and multi-population data, two types of alternative models were used: (1) a multi-trait genomic best linear unbiased prediction (GBLUP) model that modelled trait by line combinations as separate but correlated traits and (2) non-linear models based on kernel learning. These models were compared to conventional linear models for genomic prediction for two lines of brown layer hens (B1 and B2) and one line of white hens (W1). The three lines each had 1004 to 1023 training and 238 to 240 validation animals. Prediction accuracy was evaluated by estimating the correlation between observed phenotypes and predicted breeding values. When the training dataset included only data from the evaluated line, non-linear models yielded at best a similar accuracy as linear models. In some cases, when adding a distantly related line, the linear models showed a slight decrease in performance, while non-linear models generally showed no change in accuracy. When only information from a closely related line was used for training, linear models and non-linear radial basis function (RBF) kernel models performed similarly. The multi-trait GBLUP model took advantage of the estimated genetic correlations between the lines. Combining linear and non-linear models improved the accuracy of multi-line genomic prediction. Linear models and non-linear RBF models performed very similarly for genomic prediction, despite the expectation that non-linear models could deal better with the heterogeneous multi-population data. This heterogeneity of the data can be overcome by modelling trait by line combinations as separate but correlated traits, which avoids the occasional

An Offline Formulation of MPC for LPV Systems Using Linear Matrix Inequalities

Directory of Open Access Journals (Sweden)

P. Bumroongsri

2014-01-01

Full Text Available An offline model predictive control (MPC algorithm for linear parameter varying (LPV systems is presented. The main contribution is to develop an offline MPC algorithm for LPV systems that can deal with both time-varying scheduling parameter and persistent disturbance. The norm-bounding technique is used to derive an offline MPC algorithm based on the parameter-dependent state feedback control law and the parameter-dependent Lyapunov functions. The online computational time is reduced by solving offline the linear matrix inequality (LMI optimization problems to find the sequences of explicit state feedback control laws. At each sampling instant, a parameter-dependent state feedback control law is computed by linear interpolation between the precomputed state feedback control laws. The algorithm is illustrated with two examples. The results show that robust stability can be ensured in the presence of both time-varying scheduling parameter and persistent disturbance.

Biochemical methane potential prediction of plant biomasses: Comparing chemical composition versus near infrared methods and linear versus non-linear models.

Science.gov (United States)

Godin, Bruno; Mayer, Frédéric; Agneessens, Richard; Gerin, Patrick; Dardenne, Pierre; Delfosse, Philippe; Delcarte, Jérôme

2015-01-01

The reliability of different models to predict the biochemical methane potential (BMP) of various plant biomasses using a multispecies dataset was compared. The most reliable prediction models of the BMP were those based on the near infrared (NIR) spectrum compared to those based on the chemical composition. The NIR predictions of local (specific regression and non-linear) models were able to estimate quantitatively, rapidly, cheaply and easily the BMP. Such a model could be further used for biomethanation plant management and optimization. The predictions of non-linear models were more reliable compared to those of linear models. The presentation form (green-dried, silage-dried and silage-wet form) of biomasses to the NIR spectrometer did not influence the performances of the NIR prediction models. The accuracy of the BMP method should be improved to enhance further the BMP prediction models. Copyright © 2014 Elsevier Ltd. All rights reserved.

On the classical theory of ordinary linear differential equations of the second order and the Schroedinger equation for power law potentials

International Nuclear Information System (INIS)

Lima, M.L.; Mignaco, J.A.

1983-01-01

The power law potentials in the Schroedinger equation solved recently are shown to come from the classical treatment of the singularities of a linear, second order differential equation. This allows to enlarge the class of solvable power law potentials. (Author) [pt

Large-scale linear programs in planning and prediction.

Science.gov (United States)

2017-06-01

Large-scale linear programs are at the core of many traffic-related optimization problems in both planning and prediction. Moreover, many of these involve significant uncertainty, and hence are modeled using either chance constraints, or robust optim...

Nonlinear Dynamic Inversion Baseline Control Law: Architecture and Performance Predictions

Science.gov (United States)

Miller, Christopher J.

2011-01-01

A model reference dynamic inversion control law has been developed to provide a baseline control law for research into adaptive elements and other advanced flight control law components. This controller has been implemented and tested in a hardware-in-the-loop simulation; the simulation results show excellent handling qualities throughout the limited flight envelope. A simple angular momentum formulation was chosen because it can be included in the stability proofs for many basic adaptive theories, such as model reference adaptive control. Many design choices and implementation details reflect the requirements placed on the system by the nonlinear flight environment and the desire to keep the system as basic as possible to simplify the addition of the adaptive elements. Those design choices are explained, along with their predicted impact on the handling qualities.

The Application Law of Large Numbers That Predicts The Amount of Actual Loss in Insurance of Life

Science.gov (United States)

Tinungki, Georgina Maria

2018-03-01

The law of large numbers is a statistical concept that calculates the average number of events or risks in a sample or population to predict something. The larger the population is calculated, the more accurate predictions. In the field of insurance, the Law of Large Numbers is used to predict the risk of loss or claims of some participants so that the premium can be calculated appropriately. For example there is an average that of every 100 insurance participants, there is one participant who filed an accident claim, then the premium of 100 participants should be able to provide Sum Assured to at least 1 accident claim. The larger the insurance participant is calculated, the more precise the prediction of the calendar and the calculation of the premium. Life insurance, as a tool for risk spread, can only work if a life insurance company is able to bear the same risk in large numbers. Here apply what is called the law of large number. The law of large numbers states that if the amount of exposure to losses increases, then the predicted loss will be closer to the actual loss. The use of the law of large numbers allows the number of losses to be predicted better.

Who Will Win?: Predicting the Presidential Election Using Linear Regression

Science.gov (United States)

Lamb, John H.

2007-01-01

This article outlines a linear regression activity that engages learners, uses technology, and fosters cooperation. Students generated least-squares linear regression equations using TI-83 Plus[TM] graphing calculators, Microsoft[C] Excel, and paper-and-pencil calculations using derived normal equations to predict the 2004 presidential election.…

Linear and nonlinear dynamic systems in financial time series prediction

Directory of Open Access Journals (Sweden)

Salim Lahmiri

2012-10-01

Full Text Available Autoregressive moving average (ARMA process and dynamic neural networks namely the nonlinear autoregressive moving average with exogenous inputs (NARX are compared by evaluating their ability to predict financial time series; for instance the S&P500 returns. Two classes of ARMA are considered. The first one is the standard ARMA model which is a linear static system. The second one uses Kalman filter (KF to estimate and predict ARMA coefficients. This model is a linear dynamic system. The forecasting ability of each system is evaluated by means of mean absolute error (MAE and mean absolute deviation (MAD statistics. Simulation results indicate that the ARMA-KF system performs better than the standard ARMA alone. Thus, introducing dynamics into the ARMA process improves the forecasting accuracy. In addition, the ARMA-KF outperformed the NARX. This result may suggest that the linear component found in the S&P500 return series is more dominant than the nonlinear part. In sum, we conclude that introducing dynamics into the ARMA process provides an effective system for S&P500 time series prediction.

Convergence Guaranteed Nonlinear Constraint Model Predictive Control via I/O Linearization

Directory of Open Access Journals (Sweden)

Xiaobing Kong

2013-01-01

Full Text Available Constituting reliable optimal solution is a key issue for the nonlinear constrained model predictive control. Input-output feedback linearization is a popular method in nonlinear control. By using an input-output feedback linearizing controller, the original linear input constraints will change to nonlinear constraints and sometimes the constraints are state dependent. This paper presents an iterative quadratic program (IQP routine on the continuous-time system. To guarantee its convergence, another iterative approach is incorporated. The proposed algorithm can reach a feasible solution over the entire prediction horizon. Simulation results on both a numerical example and the continuous stirred tank reactors (CSTR demonstrate the effectiveness of the proposed method.

A geomorphic process law for detachment-limited hillslopes

Science.gov (United States)

Turowski, Jens

2015-04-01

Geomorphic process laws are used to assess the shape evolution of structures at the Earth's surface over geological time scales, and are routinely used in landscape evolution models. There are two currently available concepts on which process laws for hillslope evolution rely. In the transport-limited concept, the evolution of a hillslope is described by a linear or a non-linear diffusion equation. In contrast, in the threshold slope concept, the hillslope is assumed to collapse to a slope equal to the internal friction angle of the material when the load due to the relief exists the material strength. Many mountains feature bedrock slopes, especially in the high mountains, and material transport along the slope is limited by the erosion of the material from the bedrock. Here, I suggest a process law for detachment-limited or threshold-dominated hillslopes, in which the erosion rate is a function of the applied stress minus the surface stress due to structural loading. The process law leads to the prediction of an equilibrium form that compares well to the shape of many mountain domes.

Dynamic Algorithm for LQGPC Predictive Control

DEFF Research Database (Denmark)

Hangstrup, M.; Ordys, A.W.; Grimble, M.J.

1998-01-01

In this paper the optimal control law is derived for a multi-variable state space Linear Quadratic Gaussian Predictive Controller (LQGPC). A dynamic performance index is utilized resulting in an optimal steady state controller. Knowledge of future reference values is incorporated into the control......In this paper the optimal control law is derived for a multi-variable state space Linear Quadratic Gaussian Predictive Controller (LQGPC). A dynamic performance index is utilized resulting in an optimal steady state controller. Knowledge of future reference values is incorporated...... into the controller design and the solution is derived using the method of Lagrange multipliers. It is shown how well-known GPC controller can be obtained as a special case of the LQGPC controller design. The important advantage of using the LQGPC framework for designing predictive, e.g. GPS is that LQGPC enables...

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

Science.gov (United States)

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-12-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

The Inverse System Method Applied to the Derivation of Power System Non—linear Control Laws

Institute of Scientific and Technical Information of China (English)

DonghaiLI; XuezhiJIANG; 等

1997-01-01

The differential geometric method has been applied to a series of power system non-linear control problems effectively.However a set of differential equations must be solved for obtaining the required diffeomorphic transformation.Therefore the derivation of control laws is very complicated.In fact because of the specificity of power system models the required diffeomorphic transformation may be obtained directly,so it is unnecessary to solve a set of differential equations.In addition inverse system method is equivalent to differential geometric method in reality and not limited to affine nonlinear systems,Its physical meaning is able to be viewed directly and its deduction needs only algebraic operation and derivation,so control laws can be obtained easily and the application to engineering is very convenient.Authors of this paper take steam valving control of power system as a typical case to be studied.It is demonstrated that the control law deduced by inverse system method is just the same as one by differential geometric method.The conclusion will simplify the control law derivations of steam valving,excitation,converter and static var compensator by differential geometric method and may be suited to similar control problems in other areas.

Allometric scaling of population variance with mean body size is predicted from Taylor's law and density-mass allometry.

Science.gov (United States)

Cohen, Joel E; Xu, Meng; Schuster, William S F

2012-09-25

Two widely tested empirical patterns in ecology are combined here to predict how the variation of population density relates to the average body size of organisms. Taylor's law (TL) asserts that the variance of the population density of a set of populations is a power-law function of the mean population density. Density-mass allometry (DMA) asserts that the mean population density of a set of populations is a power-law function of the mean individual body mass. Combined, DMA and TL predict that the variance of the population density is a power-law function of mean individual body mass. We call this relationship "variance-mass allometry" (VMA). We confirmed the theoretically predicted power-law form and the theoretically predicted parameters of VMA, using detailed data on individual oak trees (Quercus spp.) of Black Rock Forest, Cornwall, New York. These results connect the variability of population density to the mean body mass of individuals.

Predicting musically induced emotions from physiological inputs: linear and neural network models.

Science.gov (United States)

Russo, Frank A; Vempala, Naresh N; Sandstrom, Gillian M

2013-01-01

Listening to music often leads to physiological responses. Do these physiological responses contain sufficient information to infer emotion induced in the listener? The current study explores this question by attempting to predict judgments of "felt" emotion from physiological responses alone using linear and neural network models. We measured five channels of peripheral physiology from 20 participants-heart rate (HR), respiration, galvanic skin response, and activity in corrugator supercilii and zygomaticus major facial muscles. Using valence and arousal (VA) dimensions, participants rated their felt emotion after listening to each of 12 classical music excerpts. After extracting features from the five channels, we examined their correlation with VA ratings, and then performed multiple linear regression to see if a linear relationship between the physiological responses could account for the ratings. Although linear models predicted a significant amount of variance in arousal ratings, they were unable to do so with valence ratings. We then used a neural network to provide a non-linear account of the ratings. The network was trained on the mean ratings of eight of the 12 excerpts and tested on the remainder. Performance of the neural network confirms that physiological responses alone can be used to predict musically induced emotion. The non-linear model derived from the neural network was more accurate than linear models derived from multiple linear regression, particularly along the valence dimension. A secondary analysis allowed us to quantify the relative contributions of inputs to the non-linear model. The study represents a novel approach to understanding the complex relationship between physiological responses and musically induced emotion.

Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information.

Science.gov (United States)

Zhang, Wen; Chen, Yanlin; Li, Dingfang

2017-11-25

Interactions between drugs and target proteins provide important information for the drug discovery. Currently, experiments identified only a small number of drug-target interactions. Therefore, the development of computational methods for drug-target interaction prediction is an urgent task of theoretical interest and practical significance. In this paper, we propose a label propagation method with linear neighborhood information (LPLNI) for predicting unobserved drug-target interactions. Firstly, we calculate drug-drug linear neighborhood similarity in the feature spaces, by considering how to reconstruct data points from neighbors. Then, we take similarities as the manifold of drugs, and assume the manifold unchanged in the interaction space. At last, we predict unobserved interactions between known drugs and targets by using drug-drug linear neighborhood similarity and known drug-target interactions. The experiments show that LPLNI can utilize only known drug-target interactions to make high-accuracy predictions on four benchmark datasets. Furthermore, we consider incorporating chemical structures into LPLNI models. Experimental results demonstrate that the model with integrated information (LPLNI-II) can produce improved performances, better than other state-of-the-art methods. The known drug-target interactions are an important information source for computational predictions. The usefulness of the proposed method is demonstrated by cross validation and the case study.

Darcy’s law predicts widespread forest mortality under climate warming

Science.gov (United States)

McDowell, Nate G.; Allen, Craig D.

2015-01-01

Drought and heat-induced tree mortality is accelerating in many forest biomes as a consequence of a warming climate, resulting in a threat to global forests unlike any in recorded history. Forests store the majority of terrestrial carbon, thus their loss may have significant and sustained impacts on the global carbon cycle. We use a hydraulic corollary to Darcy’s law, a core principle of vascular plant physiology, to predict characteristics of plants that will survive and die during drought under warmer future climates. Plants that are tall with isohydric stomatal regulation, low hydraulic conductance, and high leaf area are most likely to die from future drought stress. Thus, tall trees of old-growth forests are at the greatest risk of loss, which has ominous implications for terrestrial carbon storage. This application of Darcy’s law indicates today’s forests generally should be replaced by shorter and more xeric plants, owing to future warmer droughts and associated wildfires and pest attacks. The Darcy’s corollary also provides a simple, robust framework for informing forest management interventions needed to promote the survival of current forests. Given the robustness of Darcy’s law for predictions of vascular plant function, we conclude with high certainty that today’s forests are going to be subject to continued increases in mortality rates that will result in substantial reorganization of their structure and carbon storage.

Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling.

Science.gov (United States)

Kawashima, Issaku; Kumano, Hiroaki

2017-01-01

Mind-wandering (MW), task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG) variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR) to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.

Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling

Directory of Open Access Journals (Sweden)

Issaku Kawashima

2017-07-01

Full Text Available Mind-wandering (MW, task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.

A unified frame of predicting side effects of drugs by using linear neighborhood similarity.

Science.gov (United States)

Zhang, Wen; Yue, Xiang; Liu, Feng; Chen, Yanlin; Tu, Shikui; Zhang, Xining

2017-12-14

Drug side effects are one of main concerns in the drug discovery, which gains wide attentions. Investigating drug side effects is of great importance, and the computational prediction can help to guide wet experiments. As far as we known, a great number of computational methods have been proposed for the side effect predictions. The assumption that similar drugs may induce same side effects is usually employed for modeling, and how to calculate the drug-drug similarity is critical in the side effect predictions. In this paper, we present a novel measure of drug-drug similarity named "linear neighborhood similarity", which is calculated in a drug feature space by exploring linear neighborhood relationship. Then, we transfer the similarity from the feature space into the side effect space, and predict drug side effects by propagating known side effect information through a similarity-based graph. Under a unified frame based on the linear neighborhood similarity, we propose method "LNSM" and its extension "LNSM-SMI" to predict side effects of new drugs, and propose the method "LNSM-MSE" to predict unobserved side effect of approved drugs. We evaluate the performances of LNSM and LNSM-SMI in predicting side effects of new drugs, and evaluate the performances of LNSM-MSE in predicting missing side effects of approved drugs. The results demonstrate that the linear neighborhood similarity can improve the performances of side effect prediction, and the linear neighborhood similarity-based methods can outperform existing side effect prediction methods. More importantly, the proposed methods can predict side effects of new drugs as well as unobserved side effects of approved drugs under a unified frame.

Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models.

Science.gov (United States)

Pozo, Carlos; Marín-Sanguino, Alberto; Alves, Rui; Guillén-Gosálbez, Gonzalo; Jiménez, Laureano; Sorribas, Albert

2011-08-25

Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA) models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization. Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC) models that extend the power-law formalism to deal with saturation and cooperativity. Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task.

Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models

Directory of Open Access Journals (Sweden)

Sorribas Albert

2011-08-01

Full Text Available Abstract Background Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization. Results Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC models that extend the power-law formalism to deal with saturation and cooperativity. Conclusions Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task.

Relating Cohesive Zone Model to Linear Elastic Fracture Mechanics

Science.gov (United States)

Wang, John T.

2010-01-01

The conditions required for a cohesive zone model (CZM) to predict a failure load of a cracked structure similar to that obtained by a linear elastic fracture mechanics (LEFM) analysis are investigated in this paper. This study clarifies why many different phenomenological cohesive laws can produce similar fracture predictions. Analytical results for five cohesive zone models are obtained, using five different cohesive laws that have the same cohesive work rate (CWR-area under the traction-separation curve) but different maximum tractions. The effect of the maximum traction on the predicted cohesive zone length and the remote applied load at fracture is presented. Similar to the small scale yielding condition for an LEFM analysis to be valid. the cohesive zone length also needs to be much smaller than the crack length. This is a necessary condition for a CZM to obtain a fracture prediction equivalent to an LEFM result.

Structural Dynamic Analyses And Test Predictions For Spacecraft Structures With Non-Linearities

Science.gov (United States)

Vergniaud, Jean-Baptiste; Soula, Laurent; Newerla, Alfred

2012-07-01

The overall objective of the mechanical development and verification process is to ensure that the spacecraft structure is able to sustain the mechanical environments encountered during launch. In general the spacecraft structures are a-priori assumed to behave linear, i.e. the responses to a static load or dynamic excitation, respectively, will increase or decrease proportionally to the amplitude of the load or excitation induced. However, past experiences have shown that various non-linearities might exist in spacecraft structures and the consequences of their dynamic effects can significantly affect the development and verification process. Current processes are mainly adapted to linear spacecraft structure behaviour. No clear rules exist for dealing with major structure non-linearities. They are handled outside the process by individual analysis and margin policy, and analyses after tests to justify the CLA coverage. Non-linearities can primarily affect the current spacecraft development and verification process on two aspects. Prediction of flights loads by launcher/satellite coupled loads analyses (CLA): only linear satellite models are delivered for performing CLA and no well-established rules exist how to properly linearize a model when non- linearities are present. The potential impact of the linearization on the results of the CLA has not yet been properly analyzed. There are thus difficulties to assess that CLA results will cover actual flight levels. Management of satellite verification tests: the CLA results generated with a linear satellite FEM are assumed flight representative. If the internal non- linearities are present in the tested satellite then there might be difficulties to determine which input level must be passed to cover satellite internal loads. The non-linear behaviour can also disturb the shaker control, putting the satellite at risk by potentially imposing too high levels. This paper presents the results of a test campaign performed in

Technical note: A linear model for predicting δ13 Cprotein.

Science.gov (United States)

Pestle, William J; Hubbe, Mark; Smith, Erin K; Stevenson, Joseph M

2015-08-01

Development of a model for the prediction of δ(13) Cprotein from δ(13) Ccollagen and Δ(13) Cap-co . Model-generated values could, in turn, serve as "consumer" inputs for multisource mixture modeling of paleodiet. Linear regression analysis of previously published controlled diet data facilitated the development of a mathematical model for predicting δ(13) Cprotein (and an experimentally generated error term) from isotopic data routinely generated during the analysis of osseous remains (δ(13) Cco and Δ(13) Cap-co ). Regression analysis resulted in a two-term linear model (δ(13) Cprotein (%) = (0.78 × δ(13) Cco ) - (0.58× Δ(13) Cap-co ) - 4.7), possessing a high R-value of 0.93 (r(2)  = 0.86, P analysis of human osseous remains. These predicted values are ideal for use in multisource mixture modeling of dietary protein source contribution. © 2015 Wiley Periodicals, Inc.

Prediction of Process-Induced Distortions in L-Shaped Composite Profiles Using Path-Dependent Constitutive Law

Science.gov (United States)

Ding, Anxin; Li, Shuxin; Wang, Jihui; Ni, Aiqing; Sun, Liangliang; Chang, Lei

2016-10-01

In this paper, the corner spring-in angles of AS4/8552 L-shaped composite profiles with different thicknesses are predicted using path-dependent constitutive law with the consideration of material properties variation due to phase change during curing. The prediction accuracy mainly depends on the properties in the rubbery and glassy states obtained by homogenization method rather than experimental measurements. Both analytical and finite element (FE) homogenization methods are applied to predict the overall properties of AS4/8552 composite. The effect of fiber volume fraction on the properties is investigated for both rubbery and glassy states using both methods. And the predicted results are compared with experimental measurements for the glassy state. Good agreement is achieved between the predicted results and available experimental data, showing the reliability of the homogenization method. Furthermore, the corner spring-in angles of L-shaped composite profiles are measured experimentally and the reliability of path-dependent constitutive law is validated as well as the properties prediction by FE homogenization method.

Mechanistic formulation of a lineal-quadratic-linear (LQL) model: Split-dose experiments and exponentially decaying sources

International Nuclear Information System (INIS)

Guerrero, Mariana; Carlone, Marco

2010-01-01

Purpose: In recent years, several models were proposed that modify the standard linear-quadratic (LQ) model to make the predicted survival curve linear at high doses. Most of these models are purely phenomenological and can only be applied in the particular case of acute doses per fraction. The authors consider a mechanistic formulation of a linear-quadratic-linear (LQL) model in the case of split-dose experiments and exponentially decaying sources. This model provides a comprehensive description of radiation response for arbitrary dose rate and fractionation with only one additional parameter. Methods: The authors use a compartmental formulation of the LQL model from the literature. They analytically solve the model's differential equations for the case of a split-dose experiment and for an exponentially decaying source. They compare the solutions of the survival fraction with the standard LQ equations and with the lethal-potentially lethal (LPL) model. Results: In the case of the split-dose experiment, the LQL model predicts a recovery ratio as a function of dose per fraction that deviates from the square law of the standard LQ. The survival fraction as a function of time between fractions follows a similar exponential law as the LQ but adds a multiplicative factor to the LQ parameter β. The LQL solution for the split-dose experiment is very close to the LPL prediction. For the decaying source, the differences between the LQL and the LQ solutions are negligible when the half-life of the source is much larger than the characteristic repair time, which is the clinically relevant case. Conclusions: The compartmental formulation of the LQL model can be used for arbitrary dose rates and provides a comprehensive description of dose response. When the survival fraction for acute doses is linear for high dose, a deviation of the square law formula of the recovery ratio for split doses is also predicted.

Power laws from linear neuronal cable theory

DEFF Research Database (Denmark)

Pettersen, Klas H; Lindén, Henrik Anders; Tetzlaff, Tom

2014-01-01

suggested to be at the root of this phenomenon, we here demonstrate a possible origin of such power laws in the biophysical properties of single neurons described by the standard cable equation. Taking advantage of the analytical tractability of the so called ball and stick neuron model, we derive general...... are homogeneously distributed across the neural membranes and themselves exhibit pink ([Formula: see text]) noise distributions. While the PSD noise spectra at low frequencies may be dominated by synaptic noise, our findings suggest that the high-frequency power laws may originate in noise from intrinsic ion...

Predicting musically induced emotions from physiological inputs: Linear and neural network models

Directory of Open Access Journals (Sweden)

Frank A. Russo

2013-08-01

Full Text Available Listening to music often leads to physiological responses. Do these physiological responses contain sufficient information to infer emotion induced in the listener? The current study explores this question by attempting to predict judgments of 'felt' emotion from physiological responses alone using linear and neural network models. We measured five channels of peripheral physiology from 20 participants – heart rate, respiration, galvanic skin response, and activity in corrugator supercilii and zygomaticus major facial muscles. Using valence and arousal (VA dimensions, participants rated their felt emotion after listening to each of 12 classical music excerpts. After extracting features from the five channels, we examined their correlation with VA ratings, and then performed multiple linear regression to see if a linear relationship between the physiological responses could account for the ratings. Although linear models predicted a significant amount of variance in arousal ratings, they were unable to do so with valence ratings. We then used a neural network to provide a nonlinear account of the ratings. The network was trained on the mean ratings of eight of the 12 excerpts and tested on the remainder. Performance of the neural network confirms that physiological responses alone can be used to predict musically induced emotion. The nonlinear model derived from the neural network was more accurate than linear models derived from multiple linear regression, particularly along the valence dimension. A secondary analysis allowed us to quantify the relative contributions of inputs to the nonlinear model. The study represents a novel approach to understanding the complex relationship between physiological responses and musically induced emotion.

Characterizing and predicting the robustness of power-law networks

International Nuclear Information System (INIS)

LaRocca, Sarah; Guikema, Seth D.

2015-01-01

Power-law networks such as the Internet, terrorist cells, species relationships, and cellular metabolic interactions are susceptible to node failures, yet maintaining network connectivity is essential for network functionality. Disconnection of the network leads to fragmentation and, in some cases, collapse of the underlying system. However, the influences of the topology of networks on their ability to withstand node failures are poorly understood. Based on a study of the response of 2000 randomly-generated power-law networks to node failures, we find that networks with higher nodal degree and clustering coefficient, lower betweenness centrality, and lower variability in path length and clustering coefficient maintain their cohesion better during such events. We also find that network robustness, i.e., the ability to withstand node failures, can be accurately predicted a priori for power-law networks across many fields. These results provide a basis for designing new, more robust networks, improving the robustness of existing networks such as the Internet and cellular metabolic pathways, and efficiently degrading networks such as terrorist cells. - Highlights: • Examine relationship between network topology and robustness to failures. • Relationship is statistically significant for scale-free networks. • Use statistical models to estimate robustness to failures for real-world networks

Prediction of the creep properties of discontinuous fibre composites from the matrix creep law

International Nuclear Information System (INIS)

Bilde-Soerensen, J.B.; Boecker Pedersen, O.; Lilholt, H.

1975-02-01

Existing theories for predicting the creep properties of discontinuous fibre composites with non-creeping fibres from matrix creep properties, originally based on a power law, are extended to include an exponential law, and in principle a general matrixlaw. An analysis shows that the composite creep curve can be obtained by a simple displacement of the matrix creep curve in a log sigma vs. log epsilon diagram. This principle, that each point on the matrix curve has a corresponding point on the composite curve,is given a physical interpretation. The direction of displacement is such that the transition from a power law toan exponential law occurs at a lower strain rate for the composite than for the unreinforced matrix. This emphasizes the importance of the exponential creep range in the creep of fibre composites. The combined use of matrix and composite data may allow the creep phenomenon to be studied over a larger range of strain rates than otherwise possible. A method for constructing generalized composite creep diagrams is suggested. Creep properties predicted from matrix data by the present analysis are compared with experimental data from the literature. (author)

Real-time Non-linear Target Tracking Control of Wheeled Mobile Robots

Institute of Scientific and Technical Information of China (English)

YU Wenyong

2006-01-01

A control strategy for real-time target tracking for wheeled mobile robots is presented. Using a modified Kalman filter for environment perception, a novel tracking control law derived from Lyapunov stability theory is introduced. Tuning of linear velocity and angular velocity with mechanical constraints is applied. The proposed control system can simultaneously solve the target trajectory prediction, real-time tracking, and posture regulation problems of a wheeled mobile robot. Experimental results illustrate the effectiveness of the proposed tracking control laws.

Modified linear predictive coding approach for moving target tracking by Doppler radar

Science.gov (United States)

Ding, Yipeng; Lin, Xiaoyi; Sun, Ke-Hui; Xu, Xue-Mei; Liu, Xi-Yao

2016-07-01

Doppler radar is a cost-effective tool for moving target tracking, which can support a large range of civilian and military applications. A modified linear predictive coding (LPC) approach is proposed to increase the target localization accuracy of the Doppler radar. Based on the time-frequency analysis of the received echo, the proposed approach first real-time estimates the noise statistical parameters and constructs an adaptive filter to intelligently suppress the noise interference. Then, a linear predictive model is applied to extend the available data, which can help improve the resolution of the target localization result. Compared with the traditional LPC method, which empirically decides the extension data length, the proposed approach develops an error array to evaluate the prediction accuracy and thus, adjust the optimum extension data length intelligently. Finally, the prediction error array is superimposed with the predictor output to correct the prediction error. A series of experiments are conducted to illustrate the validity and performance of the proposed techniques.

Predicting the long tail of book sales: Unearthing the power-law exponent

Science.gov (United States)

Fenner, Trevor; Levene, Mark; Loizou, George

2010-06-01

The concept of the long tail has recently been used to explain the phenomenon in e-commerce where the total volume of sales of the items in the tail is comparable to that of the most popular items. In the case of online book sales, the proportion of tail sales has been estimated using regression techniques on the assumption that the data obeys a power-law distribution. Here we propose a different technique for estimation based on a generative model of book sales that results in an asymptotic power-law distribution of sales, but which does not suffer from the problems related to power-law regression techniques. We show that the proportion of tail sales predicted is very sensitive to the estimated power-law exponent. In particular, if we assume that the power-law exponent of the cumulative distribution is closer to 1.1 rather than to 1.2 (estimates published in 2003, calculated using regression by two groups of researchers), then our computations suggest that the tail sales of Amazon.com, rather than being 40% as estimated by Brynjolfsson, Hu and Smith in 2003, are actually closer to 20%, the proportion estimated by its CEO.

Linear and nonlinear models for predicting fish bioconcentration factors for pesticides.

Science.gov (United States)

Yuan, Jintao; Xie, Chun; Zhang, Ting; Sun, Jinfang; Yuan, Xuejie; Yu, Shuling; Zhang, Yingbiao; Cao, Yunyuan; Yu, Xingchen; Yang, Xuan; Yao, Wu

2016-08-01

This work is devoted to the applications of the multiple linear regression (MLR), multilayer perceptron neural network (MLP NN) and projection pursuit regression (PPR) to quantitative structure-property relationship analysis of bioconcentration factors (BCFs) of pesticides tested on Bluegill (Lepomis macrochirus). Molecular descriptors of a total of 107 pesticides were calculated with the DRAGON Software and selected by inverse enhanced replacement method. Based on the selected DRAGON descriptors, a linear model was built by MLR, nonlinear models were developed using MLP NN and PPR. The robustness of the obtained models was assessed by cross-validation and external validation using test set. Outliers were also examined and deleted to improve predictive power. Comparative results revealed that PPR achieved the most accurate predictions. This study offers useful models and information for BCF prediction, risk assessment, and pesticide formulation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Dark Energy and the Hubble Law

Science.gov (United States)

Chernin, A. D.; Dolgachev, V. P.; Domozhilova, L. M.

The Big Bang predicted by Friedmann could not be empirically discovered in the 1920th, since global cosmological distances (more than 300-1000 Mpc) were not available for observations at that time. Lemaitre and Hubble studied receding motions of galaxies at local distances of less than 20-30 Mpc and found that the motions followed the (nearly) linear velocity-distance relation, known now as Hubble's law. For decades, the real nature of this phenomenon has remained a mystery, in Sandage's words. After the discovery of dark energy, it was suggested that the dynamics of local expansion flows is dominated by omnipresent dark energy, and it is the dark energy antigravity that is able to introduce the linear velocity-distance relation to the flows. It implies that Hubble's law observed at local distances was in fact the first observational manifestation of dark energy. If this is the case, the commonly accepted criteria of scientific discovery lead to the conclusion: In 1927, Lemaitre discovered dark energy and Hubble confirmed this in 1929.

A national-scale model of linear features improves predictions of farmland biodiversity.

Science.gov (United States)

Sullivan, Martin J P; Pearce-Higgins, James W; Newson, Stuart E; Scholefield, Paul; Brereton, Tom; Oliver, Tom H

2017-12-01

Modelling species distribution and abundance is important for many conservation applications, but it is typically performed using relatively coarse-scale environmental variables such as the area of broad land-cover types. Fine-scale environmental data capturing the most biologically relevant variables have the potential to improve these models. For example, field studies have demonstrated the importance of linear features, such as hedgerows, for multiple taxa, but the absence of large-scale datasets of their extent prevents their inclusion in large-scale modelling studies.We assessed whether a novel spatial dataset mapping linear and woody-linear features across the UK improves the performance of abundance models of 18 bird and 24 butterfly species across 3723 and 1547 UK monitoring sites, respectively.Although improvements in explanatory power were small, the inclusion of linear features data significantly improved model predictive performance for many species. For some species, the importance of linear features depended on landscape context, with greater importance in agricultural areas. Synthesis and applications . This study demonstrates that a national-scale model of the extent and distribution of linear features improves predictions of farmland biodiversity. The ability to model spatial variability in the role of linear features such as hedgerows will be important in targeting agri-environment schemes to maximally deliver biodiversity benefits. Although this study focuses on farmland, data on the extent of different linear features are likely to improve species distribution and abundance models in a wide range of systems and also can potentially be used to assess habitat connectivity.

Prediction of minimum temperatures in an alpine region by linear and non-linear post-processing of meteorological models

Directory of Open Access Journals (Sweden)

R. Barbiero

2007-05-01

Full Text Available Model Output Statistics (MOS refers to a method of post-processing the direct outputs of numerical weather prediction (NWP models in order to reduce the biases introduced by a coarse horizontal resolution. This technique is especially useful in orographically complex regions, where large differences can be found between the NWP elevation model and the true orography. This study carries out a comparison of linear and non-linear MOS methods, aimed at the prediction of minimum temperatures in a fruit-growing region of the Italian Alps, based on the output of two different NWPs (ECMWF T511–L60 and LAMI-3. Temperature, of course, is a particularly important NWP output; among other roles it drives the local frost forecast, which is of great interest to agriculture. The mechanisms of cold air drainage, a distinctive aspect of mountain environments, are often unsatisfactorily captured by global circulation models. The simplest post-processing technique applied in this work was a correction for the mean bias, assessed at individual model grid points. We also implemented a multivariate linear regression on the output at the grid points surrounding the target area, and two non-linear models based on machine learning techniques: Neural Networks and Random Forest. We compare the performance of all these techniques on four different NWP data sets. Downscaling the temperatures clearly improved the temperature forecasts with respect to the raw NWP output, and also with respect to the basic mean bias correction. Multivariate methods generally yielded better results, but the advantage of using non-linear algorithms was small if not negligible. RF, the best performing method, was implemented on ECMWF prognostic output at 06:00 UTC over the 9 grid points surrounding the target area. Mean absolute errors in the prediction of 2 m temperature at 06:00 UTC were approximately 1.2°C, close to the natural variability inside the area itself.

Warped Linear Prediction of Physical Model Excitations with Applications in Audio Compression and Instrument Synthesis

Science.gov (United States)

Glass, Alexis; Fukudome, Kimitoshi

2004-12-01

A sound recording of a plucked string instrument is encoded and resynthesized using two stages of prediction. In the first stage of prediction, a simple physical model of a plucked string is estimated and the instrument excitation is obtained. The second stage of prediction compensates for the simplicity of the model in the first stage by encoding either the instrument excitation or the model error using warped linear prediction. These two methods of compensation are compared with each other, and to the case of single-stage warped linear prediction, adjustments are introduced, and their applications to instrument synthesis and MPEG4's audio compression within the structured audio format are discussed.

Applications of Kalman filters based on non-linear functions to numerical weather predictions

Directory of Open Access Journals (Sweden)

G. Galanis

2006-10-01

Full Text Available This paper investigates the use of non-linear functions in classical Kalman filter algorithms on the improvement of regional weather forecasts. The main aim is the implementation of non linear polynomial mappings in a usual linear Kalman filter in order to simulate better non linear problems in numerical weather prediction. In addition, the optimal order of the polynomials applied for such a filter is identified. This work is based on observations and corresponding numerical weather predictions of two meteorological parameters characterized by essential differences in their evolution in time, namely, air temperature and wind speed. It is shown that in both cases, a polynomial of low order is adequate for eliminating any systematic error, while higher order functions lead to instabilities in the filtered results having, at the same time, trivial contribution to the sensitivity of the filter. It is further demonstrated that the filter is independent of the time period and the geographic location of application.

Applications of Kalman filters based on non-linear functions to numerical weather predictions

Directory of Open Access Journals (Sweden)

G. Galanis

2006-10-01

Full Text Available This paper investigates the use of non-linear functions in classical Kalman filter algorithms on the improvement of regional weather forecasts. The main aim is the implementation of non linear polynomial mappings in a usual linear Kalman filter in order to simulate better non linear problems in numerical weather prediction. In addition, the optimal order of the polynomials applied for such a filter is identified. This work is based on observations and corresponding numerical weather predictions of two meteorological parameters characterized by essential differences in their evolution in time, namely, air temperature and wind speed. It is shown that in both cases, a polynomial of low order is adequate for eliminating any systematic error, while higher order functions lead to instabilities in the filtered results having, at the same time, trivial contribution to the sensitivity of the filter. It is further demonstrated that the filter is independent of the time period and the geographic location of application.

Titius-Bode laws in the solar system. 2: Build your own law from disk models

Science.gov (United States)

Dubrulle, B.; Graner, F.

1994-02-01

Simply respecting both scale and rotational invariance, it is easy to construct an endless collection of theoretical models predicting a Titius-Bode law, irrespective to their physical content. Due to the numerous ways to get the law and its intrinsic arbitrariness, it is not a useful constraint on theories of solar system formation. To illustrate the simple elegance of scale-invariant methods, we explicitly cook up one of the simplest examples, an infinitely thin cold gaseous disk rotating around a central object. In that academic case, the Titius-Bode law holds during the linear stage of the gravitational instability. The time scale of the instability is of the order of a self-gravitating time scale, (G rhod)-1/2, where rhod is the disk density. This model links the separation between different density maxima with the ratio MD/MC of the masses of the disk and the central object; for instance, MD/MC of the order of 0.18 roughly leads to the observed separation between the planets. We discuss the boundary conditions and the limit of the Wentzel-Kramer-Brillouin (WKB) approximation.

Improving the Prediction of Total Surgical Procedure Time Using Linear Regression Modeling

Directory of Open Access Journals (Sweden)

Eric R. Edelman

2017-06-01

Full Text Available For efficient utilization of operating rooms (ORs, accurate schedules of assigned block time and sequences of patient cases need to be made. The quality of these planning tools is dependent on the accurate prediction of total procedure time (TPT per case. In this paper, we attempt to improve the accuracy of TPT predictions by using linear regression models based on estimated surgeon-controlled time (eSCT and other variables relevant to TPT. We extracted data from a Dutch benchmarking database of all surgeries performed in six academic hospitals in The Netherlands from 2012 till 2016. The final dataset consisted of 79,983 records, describing 199,772 h of total OR time. Potential predictors of TPT that were included in the subsequent analysis were eSCT, patient age, type of operation, American Society of Anesthesiologists (ASA physical status classification, and type of anesthesia used. First, we computed the predicted TPT based on a previously described fixed ratio model for each record, multiplying eSCT by 1.33. This number is based on the research performed by van Veen-Berkx et al., which showed that 33% of SCT is generally a good approximation of anesthesia-controlled time (ACT. We then systematically tested all possible linear regression models to predict TPT using eSCT in combination with the other available independent variables. In addition, all regression models were again tested without eSCT as a predictor to predict ACT separately (which leads to TPT by adding SCT. TPT was most accurately predicted using a linear regression model based on the independent variables eSCT, type of operation, ASA classification, and type of anesthesia. This model performed significantly better than the fixed ratio model and the method of predicting ACT separately. Making use of these more accurate predictions in planning and sequencing algorithms may enable an increase in utilization of ORs, leading to significant financial and productivity related

Improving the Prediction of Total Surgical Procedure Time Using Linear Regression Modeling.

Science.gov (United States)

Edelman, Eric R; van Kuijk, Sander M J; Hamaekers, Ankie E W; de Korte, Marcel J M; van Merode, Godefridus G; Buhre, Wolfgang F F A

2017-01-01

For efficient utilization of operating rooms (ORs), accurate schedules of assigned block time and sequences of patient cases need to be made. The quality of these planning tools is dependent on the accurate prediction of total procedure time (TPT) per case. In this paper, we attempt to improve the accuracy of TPT predictions by using linear regression models based on estimated surgeon-controlled time (eSCT) and other variables relevant to TPT. We extracted data from a Dutch benchmarking database of all surgeries performed in six academic hospitals in The Netherlands from 2012 till 2016. The final dataset consisted of 79,983 records, describing 199,772 h of total OR time. Potential predictors of TPT that were included in the subsequent analysis were eSCT, patient age, type of operation, American Society of Anesthesiologists (ASA) physical status classification, and type of anesthesia used. First, we computed the predicted TPT based on a previously described fixed ratio model for each record, multiplying eSCT by 1.33. This number is based on the research performed by van Veen-Berkx et al., which showed that 33% of SCT is generally a good approximation of anesthesia-controlled time (ACT). We then systematically tested all possible linear regression models to predict TPT using eSCT in combination with the other available independent variables. In addition, all regression models were again tested without eSCT as a predictor to predict ACT separately (which leads to TPT by adding SCT). TPT was most accurately predicted using a linear regression model based on the independent variables eSCT, type of operation, ASA classification, and type of anesthesia. This model performed significantly better than the fixed ratio model and the method of predicting ACT separately. Making use of these more accurate predictions in planning and sequencing algorithms may enable an increase in utilization of ORs, leading to significant financial and productivity related benefits.

Conservation Laws in Biochemical Reaction Networks

DEFF Research Database (Denmark)

Mahdi, Adam; Ferragut, Antoni; Valls, Claudia

2017-01-01

We study the existence of linear and nonlinear conservation laws in biochemical reaction networks with mass-action kinetics. It is straightforward to compute the linear conservation laws as they are related to the left null-space of the stoichiometry matrix. The nonlinear conservation laws...... are difficult to identify and have rarely been considered in the context of mass-action reaction networks. Here, using the Darboux theory of integrability, we provide necessary structural (i.e., parameterindependent) conditions on a reaction network to guarantee the existence of nonlinear conservation laws...

Linear and Non-linear Multi-Input Multi-Output Model Predictive Control of Continuous Stirred Tank Reactor

Directory of Open Access Journals (Sweden)

Muayad Al-Qaisy

2015-02-01

Full Text Available In this article, multi-input multi-output (MIMO linear model predictive controller (LMPC based on state space model and nonlinear model predictive controller based on neural network (NNMPC are applied on a continuous stirred tank reactor (CSTR. The idea is to have a good control system that will be able to give optimal performance, reject high load disturbance, and track set point change. In order to study the performance of the two model predictive controllers, MIMO Proportional-Integral-Derivative controller (PID strategy is used as benchmark. The LMPC, NNMPC, and PID strategies are used for controlling the residual concentration (CA and reactor temperature (T. NNMPC control shows a superior performance over the LMPC and PID controllers by presenting a smaller overshoot and shorter settling time.

Rate-Based Model Predictive Control of Turbofan Engine Clearance

Science.gov (United States)

DeCastro, Jonathan A.

2006-01-01

An innovative model predictive control strategy is developed for control of nonlinear aircraft propulsion systems and sub-systems. At the heart of the controller is a rate-based linear parameter-varying model that propagates the state derivatives across the prediction horizon, extending prediction fidelity to transient regimes where conventional models begin to lose validity. The new control law is applied to a demanding active clearance control application, where the objectives are to tightly regulate blade tip clearances and also anticipate and avoid detrimental blade-shroud rub occurrences by optimally maintaining a predefined minimum clearance. Simulation results verify that the rate-based controller is capable of satisfying the objectives during realistic flight scenarios where both a conventional Jacobian-based model predictive control law and an unconstrained linear-quadratic optimal controller are incapable of doing so. The controller is evaluated using a variety of different actuators, illustrating the efficacy and versatility of the control approach. It is concluded that the new strategy has promise for this and other nonlinear aerospace applications that place high importance on the attainment of control objectives during transient regimes.

Prediction of Accurate Mixed Mode Fatigue Crack Growth Curves using the Paris' Law

Science.gov (United States)

Sajith, S.; Krishna Murthy, K. S. R.; Robi, P. S.

2017-12-01

Accurate information regarding crack growth times and structural strength as a function of the crack size is mandatory in damage tolerance analysis. Various equivalent stress intensity factor (SIF) models are available for prediction of mixed mode fatigue life using the Paris' law. In the present investigation these models have been compared to assess their efficacy in prediction of the life close to the experimental findings as there are no guidelines/suggestions available on selection of these models for accurate and/or conservative predictions of fatigue life. Within the limitations of availability of experimental data and currently available numerical simulation techniques, the results of present study attempts to outline models that would provide accurate and conservative life predictions.

Weber's law, the magnitude effect and discrimination of sugar concentrations in nectar-feeding animals.

Science.gov (United States)

Nachev, Vladislav; Stich, Kai Petra; Winter, York

2013-01-01

Weber's law quantifies the perception of difference between stimuli. For instance, it can explain why we are less likely to detect the removal of three nuts from a bowl if the bowl is full than if it is nearly empty. This is an example of the magnitude effect - the phenomenon that the subjective perception of a linear difference between a pair of stimuli progressively diminishes when the average magnitude of the stimuli increases. Although discrimination performances of both human and animal subjects in various sensory modalities exhibit the magnitude effect, results sometimes systematically deviate from the quantitative predictions based on Weber's law. An attempt to reformulate the law to better fit data from acoustic discrimination tasks has been dubbed the "near-miss to Weber's law". Here, we tested the gustatory discrimination performance of nectar-feeding bats (Glossophaga soricina), in order to investigate whether the original version of Weber's law accurately predicts choice behavior in a two-alternative forced choice task. As expected, bats either preferred the sweeter of the two options or showed no preference. In 4 out of 6 bats the near-miss to Weber's law provided a better fit and Weber's law underestimated the magnitude effect. In order to test the generality of this observation in nectar-feeders, we reviewed previously published data on bats, hummingbirds, honeybees, and bumblebees. In all groups of animals the near-miss to Weber's law provided better fits than Weber's law. Furthermore, whereas the magnitude effect was stronger than predicted by Weber's law in vertebrates, it was weaker than predicted in insects. Thus nectar-feeding vertebrates and insects seem to differ in how their choice behavior changes as sugar concentration is increased. We discuss the ecological and evolutionary implications of the observed patterns of sugar concentration discrimination.

Weber's law, the magnitude effect and discrimination of sugar concentrations in nectar-feeding animals.

Directory of Open Access Journals (Sweden)

Vladislav Nachev

Full Text Available Weber's law quantifies the perception of difference between stimuli. For instance, it can explain why we are less likely to detect the removal of three nuts from a bowl if the bowl is full than if it is nearly empty. This is an example of the magnitude effect - the phenomenon that the subjective perception of a linear difference between a pair of stimuli progressively diminishes when the average magnitude of the stimuli increases. Although discrimination performances of both human and animal subjects in various sensory modalities exhibit the magnitude effect, results sometimes systematically deviate from the quantitative predictions based on Weber's law. An attempt to reformulate the law to better fit data from acoustic discrimination tasks has been dubbed the "near-miss to Weber's law". Here, we tested the gustatory discrimination performance of nectar-feeding bats (Glossophaga soricina, in order to investigate whether the original version of Weber's law accurately predicts choice behavior in a two-alternative forced choice task. As expected, bats either preferred the sweeter of the two options or showed no preference. In 4 out of 6 bats the near-miss to Weber's law provided a better fit and Weber's law underestimated the magnitude effect. In order to test the generality of this observation in nectar-feeders, we reviewed previously published data on bats, hummingbirds, honeybees, and bumblebees. In all groups of animals the near-miss to Weber's law provided better fits than Weber's law. Furthermore, whereas the magnitude effect was stronger than predicted by Weber's law in vertebrates, it was weaker than predicted in insects. Thus nectar-feeding vertebrates and insects seem to differ in how their choice behavior changes as sugar concentration is increased. We discuss the ecological and evolutionary implications of the observed patterns of sugar concentration discrimination.

Genomic prediction based on data from three layer lines using non-linear regression models

NARCIS (Netherlands)

Huang, H.; Windig, J.J.; Vereijken, A.; Calus, M.P.L.

2014-01-01

Background - Most studies on genomic prediction with reference populations that include multiple lines or breeds have used linear models. Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods. Methods - In an attempt to alleviate

Iterated non-linear model predictive control based on tubes and contractive constraints.

Science.gov (United States)

Murillo, M; Sánchez, G; Giovanini, L

2016-05-01

This paper presents a predictive control algorithm for non-linear systems based on successive linearizations of the non-linear dynamic around a given trajectory. A linear time varying model is obtained and the non-convex constrained optimization problem is transformed into a sequence of locally convex ones. The robustness of the proposed algorithm is addressed adding a convex contractive constraint. To account for linearization errors and to obtain more accurate results an inner iteration loop is added to the algorithm. A simple methodology to obtain an outer bounding-tube for state trajectories is also presented. The convergence of the iterative process and the stability of the closed-loop system are analyzed. The simulation results show the effectiveness of the proposed algorithm in controlling a quadcopter type unmanned aerial vehicle. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Bayesian prediction of spatial count data using generalized linear mixed models

DEFF Research Database (Denmark)

Christensen, Ole Fredslund; Waagepetersen, Rasmus Plenge

2002-01-01

Spatial weed count data are modeled and predicted using a generalized linear mixed model combined with a Bayesian approach and Markov chain Monte Carlo. Informative priors for a data set with sparse sampling are elicited using a previously collected data set with extensive sampling. Furthermore, ...

Hubble's Law Implies Benford's Law for Distances to Galaxies ...

Indian Academy of Sciences (India)

in both time and space, predicts that conformity to Benford's law will improve as more data on distances to galaxies becomes available. Con- versely, with the logical derivation of this law presented here, the recent empirical observations may beviewed as independent evidence of the validity of Hubble's law. Key words.

Combining linear polarization spectroscopy and the Representative Layer Theory to measure the Beer-Lambert law absorbance of highly scattering materials.

Science.gov (United States)

Gobrecht, Alexia; Bendoula, Ryad; Roger, Jean-Michel; Bellon-Maurel, Véronique

2015-01-01

Visible and Near Infrared (Vis-NIR) Spectroscopy is a powerful non destructive analytical method used to analyze major compounds in bulk materials and products and requiring no sample preparation. It is widely used in routine analysis and also in-line in industries, in-vivo with biomedical applications or in-field for agricultural and environmental applications. However, highly scattering samples subvert Beer-Lambert law's linear relationship between spectral absorbance and the concentrations. Instead of spectral pre-processing, which is commonly used by Vis-NIR spectroscopists to mitigate the scattering effect, we put forward an optical method, based on Polarized Light Spectroscopy to improve the absorbance signal measurement on highly scattering samples. This method selects part of the signal which is less impacted by scattering. The resulted signal is combined in the Absorption/Remission function defined in Dahm's Representative Layer Theory to compute an absorbance signal fulfilling Beer-Lambert's law, i.e. being linearly related to concentration of the chemicals composing the sample. The underpinning theories have been experimentally evaluated on scattering samples in liquid form and in powdered form. The method produced more accurate spectra and the Pearson's coefficient assessing the linearity between the absorbance spectra and the concentration of the added dye improved from 0.94 to 0.99 for liquid samples and 0.84-0.97 for powdered samples. Copyright © 2014 Elsevier B.V. All rights reserved.

Financial Distress Prediction using Linear Discriminant Analysis and Support Vector Machine

Science.gov (United States)

Santoso, Noviyanti; Wibowo, Wahyu

2018-03-01

A financial difficulty is the early stages before the bankruptcy. Bankruptcies caused by the financial distress can be seen from the financial statements of the company. The ability to predict financial distress became an important research topic because it can provide early warning for the company. In addition, predicting financial distress is also beneficial for investors and creditors. This research will be made the prediction model of financial distress at industrial companies in Indonesia by comparing the performance of Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) combined with variable selection technique. The result of this research is prediction model based on hybrid Stepwise-SVM obtains better balance among fitting ability, generalization ability and model stability than the other models.

Quantum dissipation from power-law memory

International Nuclear Information System (INIS)

Tarasov, Vasily E.

2012-01-01

A new quantum dissipation model based on memory mechanism is suggested. Dynamics of open and closed quantum systems with power-law memory is considered. The processes with power-law memory are described by using integration and differentiation of non-integer orders, by methods of fractional calculus. An example of quantum oscillator with linear friction and power-law memory is considered. - Highlights: ► A new quantum dissipation model based on memory mechanism is suggested. ► The generalization of Lindblad equation is considered. ► An exact solution of generalized Lindblad equation for quantum oscillator with linear friction and power-law memory is derived.

Using NCAP to predict RFI effects in linear bipolar integrated circuits

Science.gov (United States)

Fang, T.-F.; Whalen, J. J.; Chen, G. K. C.

1980-11-01

Applications of the Nonlinear Circuit Analysis Program (NCAP) to calculate RFI effects in electronic circuits containing discrete semiconductor devices have been reported upon previously. The objective of this paper is to demonstrate that the computer program NCAP also can be used to calcuate RFI effects in linear bipolar integrated circuits (IC's). The IC's reported upon are the microA741 operational amplifier (op amp) which is one of the most widely used IC's, and a differential pair which is a basic building block in many linear IC's. The microA741 op amp was used as the active component in a unity-gain buffer amplifier. The differential pair was used in a broad-band cascode amplifier circuit. The computer program NCAP was used to predict how amplitude-modulated RF signals are demodulated in the IC's to cause undesired low-frequency responses. The predicted and measured results for radio frequencies in the 0.050-60-MHz range are in good agreement.

Predicting recovery of cognitive function soon after stroke: differential modeling of logarithmic and linear regression.

Science.gov (United States)

Suzuki, Makoto; Sugimura, Yuko; Yamada, Sumio; Omori, Yoshitsugu; Miyamoto, Masaaki; Yamamoto, Jun-ichi

2013-01-01

Cognitive disorders in the acute stage of stroke are common and are important independent predictors of adverse outcome in the long term. Despite the impact of cognitive disorders on both patients and their families, it is still difficult to predict the extent or duration of cognitive impairments. The objective of the present study was, therefore, to provide data on predicting the recovery of cognitive function soon after stroke by differential modeling with logarithmic and linear regression. This study included two rounds of data collection comprising 57 stroke patients enrolled in the first round for the purpose of identifying the time course of cognitive recovery in the early-phase group data, and 43 stroke patients in the second round for the purpose of ensuring that the correlation of the early-phase group data applied to the prediction of each individual's degree of cognitive recovery. In the first round, Mini-Mental State Examination (MMSE) scores were assessed 3 times during hospitalization, and the scores were regressed on the logarithm and linear of time. In the second round, calculations of MMSE scores were made for the first two scoring times after admission to tailor the structures of logarithmic and linear regression formulae to fit an individual's degree of functional recovery. The time course of early-phase recovery for cognitive functions resembled both logarithmic and linear functions. However, MMSE scores sampled at two baseline points based on logarithmic regression modeling could estimate prediction of cognitive recovery more accurately than could linear regression modeling (logarithmic modeling, R(2) = 0.676, PLogarithmic modeling based on MMSE scores could accurately predict the recovery of cognitive function soon after the occurrence of stroke. This logarithmic modeling with mathematical procedures is simple enough to be adopted in daily clinical practice.

Size effects in non-linear heat conduction with flux-limited behaviors

Science.gov (United States)

Li, Shu-Nan; Cao, Bing-Yang

2017-11-01

Size effects are discussed for several non-linear heat conduction models with flux-limited behaviors, including the phonon hydrodynamic, Lagrange multiplier, hierarchy moment, nonlinear phonon hydrodynamic, tempered diffusion, thermon gas and generalized nonlinear models. For the phonon hydrodynamic, Lagrange multiplier and tempered diffusion models, heat flux will not exist in problems with sufficiently small scale. The existence of heat flux needs the sizes of heat conduction larger than their corresponding critical sizes, which are determined by the physical properties and boundary temperatures. The critical sizes can be regarded as the theoretical limits of the applicable ranges for these non-linear heat conduction models with flux-limited behaviors. For sufficiently small scale heat conduction, the phonon hydrodynamic and Lagrange multiplier models can also predict the theoretical possibility of violating the second law and multiplicity. Comparisons are also made between these non-Fourier models and non-linear Fourier heat conduction in the type of fast diffusion, which can also predict flux-limited behaviors.

Hamiltonian structures of some non-linear evolution equations

International Nuclear Information System (INIS)

Tu, G.Z.

1983-06-01

The Hamiltonian structure of the O(2,1) non-linear sigma model, generalized AKNS equations, are discussed. By reducing the O(2,1) non-linear sigma model to its Hamiltonian form some new conservation laws are derived. A new hierarchy of non-linear evolution equations is proposed and shown to be generalized Hamiltonian equations with an infinite number of conservation laws. (author)

Linear filters as a method of real-time prediction of geomagnetic activity

International Nuclear Information System (INIS)

McPherron, R.L.; Baker, D.N.; Bargatze, L.F.

1985-01-01

Important factors controlling geomagnetic activity include the solar wind velocity, the strength of the interplanetary magnetic field (IMF), and the field orientation. Because these quantities change so much in transit through the solar wind, real-time monitoring immediately upstream of the earth provides the best input for any technique of real-time prediction. One such technique is linear prediction filtering which utilizes past histories of the input and output of a linear system to create a time-invariant filter characterizing the system. Problems of nonlinearity or temporal changes of the system can be handled by appropriate choice of input parameters and piecewise approximation in various ranges of the input. We have created prediction filters for all the standard magnetic indices and tested their efficiency. The filters show that the initial response of the magnetosphere to a southward turning of the IMF peaks in 20 minutes and then again in 55 minutes. After a northward turning, auroral zone indices and the midlatitude ASYM index return to background within 2 hours, while Dst decays exponentially with a time constant of about 8 hours. This paper describes a simple, real-time system utilizing these filters which could predict a substantial fraction of the variation in magnetic activity indices 20 to 50 minutes in advance

Linear colliders - prospects 1985

International Nuclear Information System (INIS)

Rees, J.

1985-06-01

We discuss the scaling laws of linear colliders and their consequences for accelerator design. We then report on the SLAC Linear Collider project and comment on experience gained on that project and its application to future colliders. 9 refs., 2 figs

Model for predicting non-linear crack growth considering load sequence effects (LOSEQ)

International Nuclear Information System (INIS)

Fuehring, H.

1982-01-01

A new analytical model for predicting non-linear crack growth is presented which takes into account the retardation as well as the acceleration effects due to irregular loading. It considers not only the maximum peak of a load sequence to effect crack growth but also all other loads of the history according to a generalised memory criterion. Comparisons between crack growth predicted by using the LOSEQ-programme and experimentally observed data are presented. (orig.) [de

Feedback linearizing control of a MIMO power system

Science.gov (United States)

Ilyes, Laszlo

Prior research has demonstrated that either the mechanical or electrical subsystem of a synchronous electric generator may be controlled using single-input single-output (SISO) nonlinear feedback linearization. This research suggests a new approach which applies nonlinear feedback linearization to a multi-input multi-output (MIMO) model of the synchronous electric generator connected to an infinite bus load model. In this way, the electrical and mechanical subsystems may be linearized and simultaneously decoupled through the introduction of a pair of auxiliary inputs. This allows well known, linear, SISO control methods to be effectively applied to the resulting systems. The derivation of the feedback linearizing control law is presented in detail, including a discussion on the use of symbolic math processing as a development tool. The linearizing and decoupling properties of the control law are validated through simulation. And finally, the robustness of the control law is demonstrated.

Application of linear and non-linear low-Re k-ε models in two-dimensional predictions of convective heat transfer in passages with sudden contractions

International Nuclear Information System (INIS)

Raisee, M.; Hejazi, S.H.

2007-01-01

This paper presents comparisons between heat transfer predictions and measurements for developing turbulent flow through straight rectangular channels with sudden contractions at the mid-channel section. The present numerical results were obtained using a two-dimensional finite-volume code which solves the governing equations in a vertical plane located at the lateral mid-point of the channel. The pressure field is obtained with the well-known SIMPLE algorithm. The hybrid scheme was employed for the discretization of convection in all transport equations. For modeling of the turbulence, a zonal low-Reynolds number k-ε model and the linear and non-linear low-Reynolds number k-ε models with the 'Yap' and 'NYP' length-scale correction terms have been employed. The main objective of present study is to examine the ability of the above turbulence models in the prediction of convective heat transfer in channels with sudden contraction at a mid-channel section. The results of this study show that a sudden contraction creates a relatively small recirculation bubble immediately downstream of the channel contraction. This separation bubble influences the distribution of local heat transfer coefficient and increases the heat transfer levels by a factor of three. Computational results indicate that all the turbulence models employed produce similar flow fields. The zonal k-ε model produces the wrong Nusselt number distribution by underpredicting heat transfer levels in the recirculation bubble and overpredicting them in the developing region. The linear low-Re k-ε model, on the other hand, returns the correct Nusselt number distribution in the recirculation region, although it somewhat overpredicts heat transfer levels in the developing region downstream of the separation bubble. The replacement of the 'Yap' term with the 'NYP' term in the linear low-Re k-ε model results in a more accurate local Nusselt number distribution. Moreover, the application of the non-linear k

Comparison of Linear Prediction Models for Audio Signals

Directory of Open Access Journals (Sweden)

2009-03-01

Full Text Available While linear prediction (LP has become immensely popular in speech modeling, it does not seem to provide a good approach for modeling audio signals. This is somewhat surprising, since a tonal signal consisting of a number of sinusoids can be perfectly predicted based on an (all-pole LP model with a model order that is twice the number of sinusoids. We provide an explanation why this result cannot simply be extrapolated to LP of audio signals. If noise is taken into account in the tonal signal model, a low-order all-pole model appears to be only appropriate when the tonal components are uniformly distributed in the Nyquist interval. Based on this observation, different alternatives to the conventional LP model can be suggested. Either the model should be changed to a pole-zero, a high-order all-pole, or a pitch prediction model, or the conventional LP model should be preceded by an appropriate frequency transform, such as a frequency warping or downsampling. By comparing these alternative LP models to the conventional LP model in terms of frequency estimation accuracy, residual spectral flatness, and perceptual frequency resolution, we obtain several new and promising approaches to LP-based audio modeling.

Linearized and Kernelized Sparse Multitask Learning for Predicting Cognitive Outcomes in Alzheimer’s Disease

Directory of Open Access Journals (Sweden)

Xiaoli Liu

2018-01-01

Full Text Available Alzheimer’s disease (AD has been not only the substantial financial burden to the health care system but also the emotional burden to patients and their families. Predicting cognitive performance of subjects from their magnetic resonance imaging (MRI measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer’s disease. Recently, the multitask learning (MTL methods with sparsity-inducing norm (e.g., l2,1-norm have been widely studied to select the discriminative feature subset from MRI features by incorporating inherent correlations among multiple clinical cognitive measures. However, these previous works formulate the prediction tasks as a linear regression problem. The major limitation is that they assumed a linear relationship between the MRI features and the cognitive outcomes. Some multikernel-based MTL methods have been proposed and shown better generalization ability due to the nonlinear advantage. We quantify the power of existing linear and nonlinear MTL methods by evaluating their performance on cognitive score prediction of Alzheimer’s disease. Moreover, we extend the traditional l2,1-norm to a more general lql1-norm (q≥1. Experiments on the Alzheimer’s Disease Neuroimaging Initiative database showed that the nonlinear l2,1lq-MKMTL method not only achieved better prediction performance than the state-of-the-art competitive methods but also effectively fused the multimodality data.

Generalized non-linear Schroedinger hierarchy

International Nuclear Information System (INIS)

Aratyn, H.; Gomes, J.F.; Zimerman, A.H.

1994-01-01

The importance in studying the completely integrable models have became evident in the last years due to the fact that those models present an algebraic structure extremely rich, providing the natural scenery for solitons description. Those models can be described through non-linear differential equations, pseudo-linear operators (Lax formulation), or a matrix formulation. The integrability implies in the existence of a conservation law associated to each of degree of freedom. Each conserved charge Q i can be associated to a Hamiltonian, defining a time evolution related to to a time t i through the Hamilton equation ∂A/∂t i =[A,Q i ]. Particularly, for a two-dimensions field theory, infinite degree of freedom exist, and consequently infinite conservation laws describing the time evolution in space of infinite times. The Hamilton equation defines a hierarchy of models which present a infinite set of conservation laws. This paper studies the generalized non-linear Schroedinger hierarchy

Multispectral code excited linear prediction coding and its application in magnetic resonance images.

Science.gov (United States)

Hu, J H; Wang, Y; Cahill, P T

1997-01-01

This paper reports a multispectral code excited linear prediction (MCELP) method for the compression of multispectral images. Different linear prediction models and adaptation schemes have been compared. The method that uses a forward adaptive autoregressive (AR) model has been proven to achieve a good compromise between performance, complexity, and robustness. This approach is referred to as the MFCELP method. Given a set of multispectral images, the linear predictive coefficients are updated over nonoverlapping three-dimensional (3-D) macroblocks. Each macroblock is further divided into several 3-D micro-blocks, and the best excitation signal for each microblock is determined through an analysis-by-synthesis procedure. The MFCELP method has been applied to multispectral magnetic resonance (MR) images. To satisfy the high quality requirement for medical images, the error between the original image set and the synthesized one is further specified using a vector quantizer. This method has been applied to images from 26 clinical MR neuro studies (20 slices/study, three spectral bands/slice, 256x256 pixels/band, 12 b/pixel). The MFCELP method provides a significant visual improvement over the discrete cosine transform (DCT) based Joint Photographers Expert Group (JPEG) method, the wavelet transform based embedded zero-tree wavelet (EZW) coding method, and the vector tree (VT) coding method, as well as the multispectral segmented autoregressive moving average (MSARMA) method we developed previously.

Predicting Madura cattle growth curve using non-linear model

Science.gov (United States)

Widyas, N.; Prastowo, S.; Widi, T. S. M.; Baliarti, E.

2018-03-01

Madura cattle is Indonesian native. It is a composite breed that has undergone hundreds of years of selection and domestication to reach nowadays remarkable uniformity. Crossbreeding has reached the isle of Madura and the Madrasin, a cross between Madura cows and Limousine semen emerged. This paper aimed to compare the growth curve between Madrasin and one type of pure Madura cows, the common Madura cattle (Madura) using non-linear models. Madura cattles are kept traditionally thus reliable records are hardly available. Data were collected from small holder farmers in Madura. Cows from different age classes (5years) were observed, and body measurements (chest girth, body length and wither height) were taken. In total 63 Madura and 120 Madrasin records obtained. Linear model was built with cattle sub-populations and age as explanatory variables. Body weights were estimated based on the chest girth. Growth curves were built using logistic regression. Results showed that within the same age, Madrasin has significantly larger body compared to Madura (plogistic models fit better for Madura and Madrasin cattle data; with the estimated MSE for these models were 39.09 and 759.28 with prediction accuracy of 99 and 92% for Madura and Madrasin, respectively. Prediction of growth curve using logistic regression model performed well in both types of Madura cattle. However, attempts to administer accurate data on Madura cattle are necessary to better characterize and study these cattle.

Conservation laws for multidimensional systems and related linear algebra problems

International Nuclear Information System (INIS)

Igonin, Sergei

2002-01-01

We consider multidimensional systems of PDEs of generalized evolution form with t-derivatives of arbitrary order on the left-hand side and with the right-hand side dependent on lower order t-derivatives and arbitrary space derivatives. For such systems we find an explicit necessary condition for the existence of higher conservation laws in terms of the system's symbol. For systems that violate this condition we give an effective upper bound on the order of conservation laws. Using this result, we completely describe conservation laws for viscous transonic equations, for the Brusselator model and the Belousov-Zhabotinskii system. To achieve this, we solve over an arbitrary field the matrix equations SA=A t S and SA=-A t S for a quadratic matrix A and its transpose A t , which may be of independent interest

Linearizing feedforward/feedback attitude control

Science.gov (United States)

Paielli, Russell A.; Bach, Ralph E.

1991-01-01

An approach to attitude control theory is introduced in which a linear form is postulated for the closed-loop rotation error dynamics, then the exact control law required to realize it is derived. The nonminimal (four-component) quaternion form is used to attitude because it is globally nonsingular, but the minimal (three-component) quaternion form is used for attitude error because it has no nonlinear constraints to prevent the rotational error dynamics from being linearized, and the definition of the attitude error is based on quaternion algebra. This approach produces an attitude control law that linearizes the closed-loop rotational error dynamics exactly, without any attitude singularities, even if the control errors become large.

Improved Methods for Pitch Synchronous Linear Prediction Analysis of Speech

OpenAIRE

劉, 麗清

2015-01-01

Linear prediction (LP) analysis has been applied to speech system over the last few decades. LP technique is well-suited for speech analysis due to its ability to model speech production process approximately. Hence LP analysis has been widely used for speech enhancement, low-bit-rate speech coding in cellular telephony, speech recognition, characteristic parameter extraction (vocal tract resonances frequencies, fundamental frequency called pitch) and so on. However, the performance of the co...

A Homogeneous and Self-Dual Interior-Point Linear Programming Algorithm for Economic Model Predictive Control

DEFF Research Database (Denmark)

Sokoler, Leo Emil; Frison, Gianluca; Skajaa, Anders

2015-01-01

We develop an efficient homogeneous and self-dual interior-point method (IPM) for the linear programs arising in economic model predictive control of constrained linear systems with linear objective functions. The algorithm is based on a Riccati iteration procedure, which is adapted to the linear...... system of equations solved in homogeneous and self-dual IPMs. Fast convergence is further achieved using a warm-start strategy. We implement the algorithm in MATLAB and C. Its performance is tested using a conceptual power management case study. Closed loop simulations show that 1) the proposed algorithm...

The Dangers of Estimating V˙O2max Using Linear, Nonexercise Prediction Models.

Science.gov (United States)

Nevill, Alan M; Cooke, Carlton B

2017-05-01

This study aimed to compare the accuracy and goodness of fit of two competing models (linear vs allometric) when estimating V˙O2max (mL·kg·min) using nonexercise prediction models. The two competing models were fitted to the V˙O2max (mL·kg·min) data taken from two previously published studies. Study 1 (the Allied Dunbar National Fitness Survey) recruited 1732 randomly selected healthy participants, 16 yr and older, from 30 English parliamentary constituencies. Estimates of V˙O2max were obtained using a progressive incremental test on a motorized treadmill. In study 2, maximal oxygen uptake was measured directly during a fatigue limited treadmill test in older men (n = 152) and women (n = 146) 55 to 86 yr old. In both studies, the quality of fit associated with estimating V˙O2max (mL·kg·min) was superior using allometric rather than linear (additive) models based on all criteria (R, maximum log-likelihood, and Akaike information criteria). Results suggest that linear models will systematically overestimate V˙O2max for participants in their 20s and underestimate V˙O2max for participants in their 60s and older. The residuals saved from the linear models were neither normally distributed nor independent of the predicted values nor age. This will probably explain the absence of a key quadratic age term in the linear models, crucially identified using allometric models. Not only does the curvilinear age decline within an exponential function follow a more realistic age decline (the right-hand side of a bell-shaped curve), but the allometric models identified either a stature-to-body mass ratio (study 1) or a fat-free mass-to-body mass ratio (study 2), both associated with leanness when estimating V˙O2max. Adopting allometric models will provide more accurate predictions of V˙O2max (mL·kg·min) using plausible, biologically sound, and interpretable models.

Application of genetic algorithm - multiple linear regressions to predict the activity of RSK inhibitors

Directory of Open Access Journals (Sweden)

Avval Zhila Mohajeri

2015-01-01

Full Text Available This paper deals with developing a linear quantitative structure-activity relationship (QSAR model for predicting the RSK inhibition activity of some new compounds. A dataset consisting of 62 pyrazino [1,2-α] indole, diazepino [1,2-α] indole, and imidazole derivatives with known inhibitory activities was used. Multiple linear regressions (MLR technique combined with the stepwise (SW and the genetic algorithm (GA methods as variable selection tools was employed. For more checking stability, robustness and predictability of the proposed models, internal and external validation techniques were used. Comparison of the results obtained, indicate that the GA-MLR model is superior to the SW-MLR model and that it isapplicable for designing novel RSK inhibitors.

Molecular Dynamics Simulations for Resolving Scaling Laws of Polyethylene Melts

Directory of Open Access Journals (Sweden)

Kazuaki Z. Takahashi

2017-01-01

Full Text Available Long-timescale molecular dynamics simulations were performed to estimate the actual physical nature of a united-atom model of polyethylene (PE. Several scaling laws for representative polymer properties are compared to theoretical predictions. Internal structure results indicate a clear departure from theoretical predictions that assume ideal chain statics. Chain motion deviates from predictions that assume ideal motion of short chains. With regard to linear viscoelasticity, the presence or absence of entanglements strongly affects the duration of the theoretical behavior. Overall, the results indicate that Gaussian statics and dynamics are not necessarily established for real atomistic models of PE. Moreover, the actual physical nature should be carefully considered when using atomistic models for applications that expect typical polymer behaviors.

Predicting Fuel Ignition Quality Using 1H NMR Spectroscopy and Multiple Linear Regression

KAUST Repository

Abdul Jameel, Abdul Gani; Naser, Nimal; Emwas, Abdul-Hamid M.; Dooley, Stephen; Sarathy, Mani

2016-01-01

An improved model for the prediction of ignition quality of hydrocarbon fuels has been developed using 1H nuclear magnetic resonance (NMR) spectroscopy and multiple linear regression (MLR) modeling. Cetane number (CN) and derived cetane number (DCN

Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Data Sets.

Science.gov (United States)

Marchese Robinson, Richard L; Palczewska, Anna; Palczewski, Jan; Kidley, Nathan

2017-08-28

The ability to interpret the predictions made by quantitative structure-activity relationships (QSARs) offers a number of advantages. While QSARs built using nonlinear modeling approaches, such as the popular Random Forest algorithm, might sometimes be more predictive than those built using linear modeling approaches, their predictions have been perceived as difficult to interpret. However, a growing number of approaches have been proposed for interpreting nonlinear QSAR models in general and Random Forest in particular. In the current work, we compare the performance of Random Forest to those of two widely used linear modeling approaches: linear Support Vector Machines (SVMs) (or Support Vector Regression (SVR)) and partial least-squares (PLS). We compare their performance in terms of their predictivity as well as the chemical interpretability of the predictions using novel scoring schemes for assessing heat map images of substructural contributions. We critically assess different approaches for interpreting Random Forest models as well as for obtaining predictions from the forest. We assess the models on a large number of widely employed public-domain benchmark data sets corresponding to regression and binary classification problems of relevance to hit identification and toxicology. We conclude that Random Forest typically yields comparable or possibly better predictive performance than the linear modeling approaches and that its predictions may also be interpreted in a chemically and biologically meaningful way. In contrast to earlier work looking at interpretation of nonlinear QSAR models, we directly compare two methodologically distinct approaches for interpreting Random Forest models. The approaches for interpreting Random Forest assessed in our article were implemented using open-source programs that we have made available to the community. These programs are the rfFC package ( https://r-forge.r-project.org/R/?group_id=1725 ) for the R statistical

Predicting oropharyngeal tumor volume throughout the course of radiation therapy from pretreatment computed tomography data using general linear models.

Science.gov (United States)

Yock, Adam D; Rao, Arvind; Dong, Lei; Beadle, Beth M; Garden, Adam S; Kudchadker, Rajat J; Court, Laurence E

2014-05-01

The purpose of this work was to develop and evaluate the accuracy of several predictive models of variation in tumor volume throughout the course of radiation therapy. Nineteen patients with oropharyngeal cancers were imaged daily with CT-on-rails for image-guided alignment per an institutional protocol. The daily volumes of 35 tumors in these 19 patients were determined and used to generate (1) a linear model in which tumor volume changed at a constant rate, (2) a general linear model that utilized the power fit relationship between the daily and initial tumor volumes, and (3) a functional general linear model that identified and exploited the primary modes of variation between time series describing the changing tumor volumes. Primary and nodal tumor volumes were examined separately. The accuracy of these models in predicting daily tumor volumes were compared with those of static and linear reference models using leave-one-out cross-validation. In predicting the daily volume of primary tumors, the general linear model and the functional general linear model were more accurate than the static reference model by 9.9% (range: -11.6%-23.8%) and 14.6% (range: -7.3%-27.5%), respectively, and were more accurate than the linear reference model by 14.2% (range: -6.8%-40.3%) and 13.1% (range: -1.5%-52.5%), respectively. In predicting the daily volume of nodal tumors, only the 14.4% (range: -11.1%-20.5%) improvement in accuracy of the functional general linear model compared to the static reference model was statistically significant. A general linear model and a functional general linear model trained on data from a small population of patients can predict the primary tumor volume throughout the course of radiation therapy with greater accuracy than standard reference models. These more accurate models may increase the prognostic value of information about the tumor garnered from pretreatment computed tomography images and facilitate improved treatment management.

Predicting oropharyngeal tumor volume throughout the course of radiation therapy from pretreatment computed tomography data using general linear models

International Nuclear Information System (INIS)

Yock, Adam D.; Kudchadker, Rajat J.; Rao, Arvind; Dong, Lei; Beadle, Beth M.; Garden, Adam S.; Court, Laurence E.

2014-01-01

Purpose: The purpose of this work was to develop and evaluate the accuracy of several predictive models of variation in tumor volume throughout the course of radiation therapy. Methods: Nineteen patients with oropharyngeal cancers were imaged daily with CT-on-rails for image-guided alignment per an institutional protocol. The daily volumes of 35 tumors in these 19 patients were determined and used to generate (1) a linear model in which tumor volume changed at a constant rate, (2) a general linear model that utilized the power fit relationship between the daily and initial tumor volumes, and (3) a functional general linear model that identified and exploited the primary modes of variation between time series describing the changing tumor volumes. Primary and nodal tumor volumes were examined separately. The accuracy of these models in predicting daily tumor volumes were compared with those of static and linear reference models using leave-one-out cross-validation. Results: In predicting the daily volume of primary tumors, the general linear model and the functional general linear model were more accurate than the static reference model by 9.9% (range: −11.6%–23.8%) and 14.6% (range: −7.3%–27.5%), respectively, and were more accurate than the linear reference model by 14.2% (range: −6.8%–40.3%) and 13.1% (range: −1.5%–52.5%), respectively. In predicting the daily volume of nodal tumors, only the 14.4% (range: −11.1%–20.5%) improvement in accuracy of the functional general linear model compared to the static reference model was statistically significant. Conclusions: A general linear model and a functional general linear model trained on data from a small population of patients can predict the primary tumor volume throughout the course of radiation therapy with greater accuracy than standard reference models. These more accurate models may increase the prognostic value of information about the tumor garnered from pretreatment computed tomography

Scaling laws and fluctuations in the statistics of word frequencies

Science.gov (United States)

Gerlach, Martin; Altmann, Eduardo G.

2014-11-01

In this paper, we combine statistical analysis of written texts and simple stochastic models to explain the appearance of scaling laws in the statistics of word frequencies. The average vocabulary of an ensemble of fixed-length texts is known to scale sublinearly with the total number of words (Heaps’ law). Analyzing the fluctuations around this average in three large databases (Google-ngram, English Wikipedia, and a collection of scientific articles), we find that the standard deviation scales linearly with the average (Taylor's law), in contrast to the prediction of decaying fluctuations obtained using simple sampling arguments. We explain both scaling laws (Heaps’ and Taylor) by modeling the usage of words using a Poisson process with a fat-tailed distribution of word frequencies (Zipf's law) and topic-dependent frequencies of individual words (as in topic models). Considering topical variations lead to quenched averages, turn the vocabulary size a non-self-averaging quantity, and explain the empirical observations. For the numerous practical applications relying on estimations of vocabulary size, our results show that uncertainties remain large even for long texts. We show how to account for these uncertainties in measurements of lexical richness of texts with different lengths.

The application of sparse linear prediction dictionary to compressive sensing in speech signals

Directory of Open Access Journals (Sweden)

YOU Hanxu

2016-04-01

Full Text Available Appling compressive sensing (CS,which theoretically guarantees that signal sampling and signal compression can be achieved simultaneously,into audio and speech signal processing is one of the most popular research topics in recent years.In this paper,K-SVD algorithm was employed to learn a sparse linear prediction dictionary regarding as the sparse basis of underlying speech signals.Compressed signals was obtained by applying random Gaussian matrix to sample original speech frames.Orthogonal matching pursuit (OMP and compressive sampling matching pursuit (CoSaMP were adopted to recovery original signals from compressed one.Numbers of experiments were carried out to investigate the impact of speech frames length,compression ratios,sparse basis and reconstruction algorithms on CS performance.Results show that sparse linear prediction dictionary can advance the performance of speech signals reconstruction compared with discrete cosine transform (DCT matrix.

The Schroedinger equation for central power law potentials and the classical theory of ordinary linear differential equations of the second order

International Nuclear Information System (INIS)

Lima, M.L.; Mignaco, J.A.

1985-01-01

It is shown that the rational power law potentials in the two-body radial Schoedinger equation admit a systematic treatment available from the classical theory of ordinary linear differential equations of the second order. The admissible potentials come into families evolved from equations having a fixed number of elementary singularities. As a consequence, relations are found and discussed among the several potentials in a family. (Author) [pt

Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.

Science.gov (United States)

Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko

2016-03-01

In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

Computationally Efficient Amplitude Modulated Sinusoidal Audio Coding using Frequency-Domain Linear Prediction

DEFF Research Database (Denmark)

Christensen, M. G.; Jensen, Søren Holdt

2006-01-01

A method for amplitude modulated sinusoidal audio coding is presented that has low complexity and low delay. This is based on a subband processing system, where, in each subband, the signal is modeled as an amplitude modulated sum of sinusoids. The envelopes are estimated using frequency......-domain linear prediction and the prediction coefficients are quantized. As a proof of concept, we evaluate different configurations in a subjective listening test, and this shows that the proposed method offers significant improvements in sinusoidal coding. Furthermore, the properties of the frequency...

Robust entry guidance using linear covariance-based model predictive control

Directory of Open Access Journals (Sweden)

Jianjun Luo

2017-02-01

Full Text Available For atmospheric entry vehicles, guidance design can be accomplished by solving an optimal issue using optimal control theories. However, traditional design methods generally focus on the nominal performance and do not include considerations of the robustness in the design process. This paper proposes a linear covariance-based model predictive control method for robust entry guidance design. Firstly, linear covariance analysis is employed to directly incorporate the robustness into the guidance design. The closed-loop covariance with the feedback updated control command is initially formulated to provide the expected errors of the nominal state variables in the presence of uncertainties. Then, the closed-loop covariance is innovatively used as a component of the cost function to guarantee the robustness to reduce its sensitivity to uncertainties. After that, the models predictive control is used to solve the optimal problem, and the control commands (bank angles are calculated. Finally, a series of simulations for different missions have been completed to demonstrate the high performance in precision and the robustness with respect to initial perturbations as well as uncertainties in the entry process. The 3σ confidence region results in the presence of uncertainties which show that the robustness of the guidance has been improved, and the errors of the state variables are decreased by approximately 35%.

Evaluation of accuracy of linear regression models in predicting urban stormwater discharge characteristics.

Science.gov (United States)

Madarang, Krish J; Kang, Joo-Hyon

2014-06-01

Stormwater runoff has been identified as a source of pollution for the environment, especially for receiving waters. In order to quantify and manage the impacts of stormwater runoff on the environment, predictive models and mathematical models have been developed. Predictive tools such as regression models have been widely used to predict stormwater discharge characteristics. Storm event characteristics, such as antecedent dry days (ADD), have been related to response variables, such as pollutant loads and concentrations. However it has been a controversial issue among many studies to consider ADD as an important variable in predicting stormwater discharge characteristics. In this study, we examined the accuracy of general linear regression models in predicting discharge characteristics of roadway runoff. A total of 17 storm events were monitored in two highway segments, located in Gwangju, Korea. Data from the monitoring were used to calibrate United States Environmental Protection Agency's Storm Water Management Model (SWMM). The calibrated SWMM was simulated for 55 storm events, and the results of total suspended solid (TSS) discharge loads and event mean concentrations (EMC) were extracted. From these data, linear regression models were developed. R(2) and p-values of the regression of ADD for both TSS loads and EMCs were investigated. Results showed that pollutant loads were better predicted than pollutant EMC in the multiple regression models. Regression may not provide the true effect of site-specific characteristics, due to uncertainty in the data. Copyright © 2014 The Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network

Science.gov (United States)

Uca; Toriman, Ekhwan; Jaafar, Othman; Maru, Rosmini; Arfan, Amal; Saleh Ahmar, Ansari

2018-01-01

Prediction of suspended sediment discharge in a catchments area is very important because it can be used to evaluation the erosion hazard, management of its water resources, water quality, hydrology project management (dams, reservoirs, and irrigation) and to determine the extent of the damage that occurred in the catchments. Multiple Linear Regression analysis and artificial neural network can be used to predict the amount of daily suspended sediment discharge. Regression analysis using the least square method, whereas artificial neural networks using Radial Basis Function (RBF) and feedforward multilayer perceptron with three learning algorithms namely Levenberg-Marquardt (LM), Scaled Conjugate Descent (SCD) and Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFGS). The number neuron of hidden layer is three to sixteen, while in output layer only one neuron because only one output target. The mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2 ) and coefficient of efficiency (CE) of the multiple linear regression (MLRg) value Model 2 (6 input variable independent) has the lowest the value of MAE and RMSE (0.0000002 and 13.6039) and highest R2 and CE (0.9971 and 0.9971). When compared between LM, SCG and RBF, the BFGS model structure 3-7-1 is the better and more accurate to prediction suspended sediment discharge in Jenderam catchment. The performance value in testing process, MAE and RMSE (13.5769 and 17.9011) is smallest, meanwhile R2 and CE (0.9999 and 0.9998) is the highest if it compared with the another BFGS Quasi-Newton model (6-3-1, 9-10-1 and 12-12-1). Based on the performance statistics value, MLRg, LM, SCG, BFGS and RBF suitable and accurately for prediction by modeling the non-linear complex behavior of suspended sediment responses to rainfall, water depth and discharge. The comparison between artificial neural network (ANN) and MLRg, the MLRg Model 2 accurately for to prediction suspended sediment discharge (kg

Relativistic Linear Restoring Force

Science.gov (United States)

Clark, D.; Franklin, J.; Mann, N.

2012-01-01

We consider two different forms for a relativistic version of a linear restoring force. The pair comes from taking Hooke's law to be the force appearing on the right-hand side of the relativistic expressions: d"p"/d"t" or d"p"/d["tau"]. Either formulation recovers Hooke's law in the non-relativistic limit. In addition to these two forces, we…

Sparsity in Linear Predictive Coding of Speech

DEFF Research Database (Denmark)

Giacobello, Daniele

of the effectiveness of their application in audio processing. The second part of the thesis deals with introducing sparsity directly in the linear prediction analysis-by-synthesis (LPAS) speech coding paradigm. We first propose a novel near-optimal method to look for a sparse approximate excitation using a compressed...... one with direct applications to coding but also consistent with the speech production model of voiced speech, where the excitation of the all-pole filter can be modeled as an impulse train, i.e., a sparse sequence. Introducing sparsity in the LP framework will also bring to de- velop the concept...... sensing formulation. Furthermore, we define a novel re-estimation procedure to adapt the predictor coefficients to the given sparse excitation, balancing the two representations in the context of speech coding. Finally, the advantages of the compact parametric representation of a segment of speech, given...

Quasi-closed phase forward-backward linear prediction analysis of speech for accurate formant detection and estimation.

Science.gov (United States)

Gowda, Dhananjaya; Airaksinen, Manu; Alku, Paavo

2017-09-01

Recently, a quasi-closed phase (QCP) analysis of speech signals for accurate glottal inverse filtering was proposed. However, the QCP analysis which belongs to the family of temporally weighted linear prediction (WLP) methods uses the conventional forward type of sample prediction. This may not be the best choice especially in computing WLP models with a hard-limiting weighting function. A sample selective minimization of the prediction error in WLP reduces the effective number of samples available within a given window frame. To counter this problem, a modified quasi-closed phase forward-backward (QCP-FB) analysis is proposed, wherein each sample is predicted based on its past as well as future samples thereby utilizing the available number of samples more effectively. Formant detection and estimation experiments on synthetic vowels generated using a physical modeling approach as well as natural speech utterances show that the proposed QCP-FB method yields statistically significant improvements over the conventional linear prediction and QCP methods.

A Riccati Based Homogeneous and Self-Dual Interior-Point Method for Linear Economic Model Predictive Control

DEFF Research Database (Denmark)

Sokoler, Leo Emil; Frison, Gianluca; Edlund, Kristian

2013-01-01

In this paper, we develop an efficient interior-point method (IPM) for the linear programs arising in economic model predictive control of linear systems. The novelty of our algorithm is that it combines a homogeneous and self-dual model, and a specialized Riccati iteration procedure. We test...

Relationship between time-resolved and non-time-resolved Beer-Lambert law in turbid media.

Science.gov (United States)

Nomura, Y; Hazeki, O; Tamura, M

1997-06-01

The time-resolved Beer-Lambert law proposed for oxygen monitoring using pulsed light was extended to the non-time-resolved case in a scattered medium such as living tissues with continuous illumination. The time-resolved Beer-Lambert law was valid for the phantom model and living tissues in the visible and near-infrared regions. The absolute concentration and oxygen saturation of haemoglobin in rat brain and thigh muscle could be determined. The temporal profile of rat brain was reproduced by Monte Carlo simulation. When the temporal profiles of rat brain under different oxygenation states were integrated with time, the absorbance difference was linearly related to changes in the absorption coefficient. When the simulated profiles were integrated, there was a linear relationship within the absorption coefficient which was predicted for fractional inspiratory oxygen concentration from 10 to 100% and, in the case beyond the range of the absorption coefficient, the deviation from linearity was slight. We concluded that an optical pathlength which is independent of changes in the absorption coefficient is a good approximation for near-infrared oxygen monitoring.

Prediction of Complex Human Traits Using the Genomic Best Linear Unbiased Predictor

DEFF Research Database (Denmark)

de los Campos, Gustavo; Vazquez, Ana I; Fernando, Rohan

2013-01-01

Despite important advances from Genome Wide Association Studies (GWAS), for most complex human traits and diseases, a sizable proportion of genetic variance remains unexplained and prediction accuracy (PA) is usually low. Evidence suggests that PA can be improved using Whole-Genome Regression (WGR......) models where phenotypes are regressed on hundreds of thousands of variants simultaneously. The Genomic Best Linear Unbiased Prediction G-BLUP, a ridge-regression type method) is a commonly used WGR method and has shown good predictive performance when applied to plant and animal breeding populations....... However, breeding and human populations differ greatly in a number of factors that can affect the predictive performance of G-BLUP. Using theory, simulations, and real data analysis, we study the erformance of G-BLUP when applied to data from related and unrelated human subjects. Under perfect linkage...

Statistical Basis for Predicting Technological Progress

Science.gov (United States)

Nagy, Béla; Farmer, J. Doyne; Bui, Quan M.; Trancik, Jessika E.

2013-01-01

Forecasting technological progress is of great interest to engineers, policy makers, and private investors. Several models have been proposed for predicting technological improvement, but how well do these models perform? An early hypothesis made by Theodore Wright in 1936 is that cost decreases as a power law of cumulative production. An alternative hypothesis is Moore's law, which can be generalized to say that technologies improve exponentially with time. Other alternatives were proposed by Goddard, Sinclair et al., and Nordhaus. These hypotheses have not previously been rigorously tested. Using a new database on the cost and production of 62 different technologies, which is the most expansive of its kind, we test the ability of six different postulated laws to predict future costs. Our approach involves hindcasting and developing a statistical model to rank the performance of the postulated laws. Wright's law produces the best forecasts, but Moore's law is not far behind. We discover a previously unobserved regularity that production tends to increase exponentially. A combination of an exponential decrease in cost and an exponential increase in production would make Moore's law and Wright's law indistinguishable, as originally pointed out by Sahal. We show for the first time that these regularities are observed in data to such a degree that the performance of these two laws is nearly the same. Our results show that technological progress is forecastable, with the square root of the logarithmic error growing linearly with the forecasting horizon at a typical rate of 2.5% per year. These results have implications for theories of technological change, and assessments of candidate technologies and policies for climate change mitigation. PMID:23468837

Statistical basis for predicting technological progress.

Directory of Open Access Journals (Sweden)

Béla Nagy

Full Text Available Forecasting technological progress is of great interest to engineers, policy makers, and private investors. Several models have been proposed for predicting technological improvement, but how well do these models perform? An early hypothesis made by Theodore Wright in 1936 is that cost decreases as a power law of cumulative production. An alternative hypothesis is Moore's law, which can be generalized to say that technologies improve exponentially with time. Other alternatives were proposed by Goddard, Sinclair et al., and Nordhaus. These hypotheses have not previously been rigorously tested. Using a new database on the cost and production of 62 different technologies, which is the most expansive of its kind, we test the ability of six different postulated laws to predict future costs. Our approach involves hindcasting and developing a statistical model to rank the performance of the postulated laws. Wright's law produces the best forecasts, but Moore's law is not far behind. We discover a previously unobserved regularity that production tends to increase exponentially. A combination of an exponential decrease in cost and an exponential increase in production would make Moore's law and Wright's law indistinguishable, as originally pointed out by Sahal. We show for the first time that these regularities are observed in data to such a degree that the performance of these two laws is nearly the same. Our results show that technological progress is forecastable, with the square root of the logarithmic error growing linearly with the forecasting horizon at a typical rate of 2.5% per year. These results have implications for theories of technological change, and assessments of candidate technologies and policies for climate change mitigation.

The Schroedinger equation for central power law potentials and the classical theory of ordinary linear differential equations of the second order

International Nuclear Information System (INIS)

Lima, M.L.; Mignaco, J.A.

1985-01-01

It is shown that the rational power law potentials in the two-body radial Schrodinger equations admit a systematic treatment available from the classical theory of ordinary linear differential equations of the second order. The resulting potentials come into families evolved from equations having a fixed number of elementary regular singularities. As a consequence, relations are found and discussed among the several potentials in a family. (Author) [pt

An improved robust model predictive control for linear parameter-varying input-output models

NARCIS (Netherlands)

Abbas, H.S.; Hanema, J.; Tóth, R.; Mohammadpour, J.; Meskin, N.

2018-01-01

This paper describes a new robust model predictive control (MPC) scheme to control the discrete-time linear parameter-varying input-output models subject to input and output constraints. Closed-loop asymptotic stability is guaranteed by including a quadratic terminal cost and an ellipsoidal terminal

Linear predictions of supercritical flow instability in two parallel channels

International Nuclear Information System (INIS)

Shah, M.

2008-01-01

A steady state linear code that can predict thermo-hydraulic instability boundaries in a two parallel channel system under supercritical conditions has been developed. Linear and non-linear solutions of the instability boundary in a two parallel channel system are also compared. The effect of gravity on the instability boundary in a two parallel channel system, by changing the orientation of the system flow from horizontal flow to vertical up-flow and vertical down-flow has been analyzed. Vertical up-flow is found to be more unstable than horizontal flow and vertical down flow is found to be the most unstable configuration. The type of instability present in each flow-orientation of a parallel channel system has been checked and the density wave oscillation type is observed in horizontal flow and vertical up-flow, while the static type of instability is observed in a vertical down-flow for the cases studied here. The parameters affecting the instability boundary, such as the heating power, inlet temperature, inlet and outlet K-factors are varied to assess their effects. This study is important for the design of future Generation IV nuclear reactors in which supercritical light water is proposed as the primary coolant. (author)

Quantifying the predictive consequences of model error with linear subspace analysis

Science.gov (United States)

White, Jeremy T.; Doherty, John E.; Hughes, Joseph D.

2014-01-01

All computer models are simplified and imperfect simulators of complex natural systems. The discrepancy arising from simplification induces bias in model predictions, which may be amplified by the process of model calibration. This paper presents a new method to identify and quantify the predictive consequences of calibrating a simplified computer model. The method is based on linear theory, and it scales efficiently to the large numbers of parameters and observations characteristic of groundwater and petroleum reservoir models. The method is applied to a range of predictions made with a synthetic integrated surface-water/groundwater model with thousands of parameters. Several different observation processing strategies and parameterization/regularization approaches are examined in detail, including use of the Karhunen-Loève parameter transformation. Predictive bias arising from model error is shown to be prediction specific and often invisible to the modeler. The amount of calibration-induced bias is influenced by several factors, including how expert knowledge is applied in the design of parameterization schemes, the number of parameters adjusted during calibration, how observations and model-generated counterparts are processed, and the level of fit with observations achieved through calibration. Failure to properly implement any of these factors in a prediction-specific manner may increase the potential for predictive bias in ways that are not visible to the calibration and uncertainty analysis process.

Model predictive control of a high speed switched reluctance generator system

NARCIS (Netherlands)

Marinkov, Sava; De Jager, Bram; Steinbuch, Maarten

2013-01-01

This paper presents a novel voltage control strategy for the high-speed operation of a Switched Reluctance Generator. It uses a linear Model Predictive Control law based on the average system model. The controller computes the DC-link current needed to achieve the tracking of a desired voltage

Energy harvesting with stacked dielectric elastomer transducers: Nonlinear theory, optimization, and linearized scaling law

Science.gov (United States)

Tutcuoglu, A.; Majidi, C.

2014-12-01

Using principles of damped harmonic oscillation with continuous media, we examine electrostatic energy harvesting with a "soft-matter" array of dielectric elastomer (DE) transducers. The array is composed of infinitely thin and deformable electrodes separated by layers of insulating elastomer. During vibration, it deforms longitudinally, resulting in a change in the capacitance and electrical enthalpy of the charged electrodes. Depending on the phase of electrostatic loading, the DE array can function as either an actuator that amplifies small vibrations or a generator that converts these external excitations into electrical power. Both cases are addressed with a comprehensive theory that accounts for the influence of viscoelasticity, dielectric breakdown, and electromechanical coupling induced by Maxwell stress. In the case of a linearized Kelvin-Voigt model of the dielectric, we obtain a closed-form estimate for the electrical power output and a scaling law for DE generator design. For the complete nonlinear model, we obtain the optimal electrostatic voltage input for maximum electrical power output.

Stochastic equivalent linearization in 3-D hysteretic frames

International Nuclear Information System (INIS)

Casciati, F.; Faravelli, L.

1987-01-01

Stochastic equivalent linearization technique for hysteretic systems is extended to study the dynamic response of 3-D frames with hysteretic constitutive laws in the potential plastic hinges. The constitutive law is idealized by an appropriate endochronic model. A general purpose finite element code is adopted in order to generate the matrices by which the equations of motion to be linearized are built. (orig./HP)

A review of model predictive control: moving from linear to nonlinear design methods

International Nuclear Information System (INIS)

Nandong, J.; Samyudia, Y.; Tade, M.O.

2006-01-01

Linear model predictive control (LMPC) has now been considered as an industrial control standard in process industry. Its extension to nonlinear cases however has not yet gained wide acceptance due to many reasons, e.g. excessively heavy computational load and effort, thus, preventing its practical implementation in real-time control. The application of nonlinear MPC (NMPC) is advantageous for processes with strong nonlinearity or when the operating points are frequently moved from one set point to another due to, for instance, changes in market demands. Much effort has been dedicated towards improving the computational efficiency of NMPC as well as its stability analysis. This paper provides a review on alternative ways of extending linear MPC to the nonlinear one. We also highlight the critical issues pertinent to the applications of NMPC and discuss possible solutions to address these issues. In addition, we outline the future research trend in the area of model predictive control by emphasizing on the potential applications of multi-scale process model within NMPC

Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions

Science.gov (United States)

Tewarie, P.; Bright, M.G.; Hillebrand, A.; Robson, S.E.; Gascoyne, L.E.; Morris, P.G.; Meier, J.; Van Mieghem, P.; Brookes, M.J.

2016-01-01

Understanding the electrophysiological basis of resting state networks (RSNs) in the human brain is a critical step towards elucidating how inter-areal connectivity supports healthy brain function. In recent years, the relationship between RSNs (typically measured using haemodynamic signals) and electrophysiology has been explored using functional Magnetic Resonance Imaging (fMRI) and magnetoencephalography (MEG). Significant progress has been made, with similar spatial structure observable in both modalities. However, there is a pressing need to understand this relationship beyond simple visual similarity of RSN patterns. Here, we introduce a mathematical model to predict fMRI-based RSNs using MEG. Our unique model, based upon a multivariate Taylor series, incorporates both phase and amplitude based MEG connectivity metrics, as well as linear and non-linear interactions within and between neural oscillations measured in multiple frequency bands. We show that including non-linear interactions, multiple frequency bands and cross-frequency terms significantly improves fMRI network prediction. This shows that fMRI connectivity is not only the result of direct electrophysiological connections, but is also driven by the overlap of connectivity profiles between separate regions. Our results indicate that a complete understanding of the electrophysiological basis of RSNs goes beyond simple frequency-specific analysis, and further exploration of non-linear and cross-frequency interactions will shed new light on distributed network connectivity, and its perturbation in pathology. PMID:26827811

On Newton's third law and its symmetry-breaking effects

International Nuclear Information System (INIS)

Pinheiro, Mario J

2011-01-01

The law of action-reaction, considered by Ernst Mach as the cornerstone of physics, is thoroughly used to derive the conservation laws of linear and angular momentum. However, the conflict between momentum conservation law and Newton's third law, on experimental and theoretical grounds, calls for more attention. We give a background survey of several questions raised by the action-reaction law and, in particular, the role of the physical vacuum is shown to provide an appropriate framework for clarifying the occurrence of possible violations of the action-reaction law. Then, in the framework of statistical mechanics, using a maximizing entropy procedure, we obtain an expression for the general linear momentum of a body particle. The new approach presented here shows that Newton's third law is not verified in systems out of equilibrium due to an additional entropic gradient term present in the particle's momentum.

Scaling laws and fluctuations in the statistics of word frequencies

International Nuclear Information System (INIS)

Gerlach, Martin; Altmann, Eduardo G

2014-01-01

In this paper, we combine statistical analysis of written texts and simple stochastic models to explain the appearance of scaling laws in the statistics of word frequencies. The average vocabulary of an ensemble of fixed-length texts is known to scale sublinearly with the total number of words (Heaps’ law). Analyzing the fluctuations around this average in three large databases (Google-ngram, English Wikipedia, and a collection of scientific articles), we find that the standard deviation scales linearly with the average (Taylor's law), in contrast to the prediction of decaying fluctuations obtained using simple sampling arguments. We explain both scaling laws (Heaps’ and Taylor) by modeling the usage of words using a Poisson process with a fat-tailed distribution of word frequencies (Zipf's law) and topic-dependent frequencies of individual words (as in topic models). Considering topical variations lead to quenched averages, turn the vocabulary size a non-self-averaging quantity, and explain the empirical observations. For the numerous practical applications relying on estimations of vocabulary size, our results show that uncertainties remain large even for long texts. We show how to account for these uncertainties in measurements of lexical richness of texts with different lengths. (paper)

Predicting and understanding law-making with word vectors and an ensemble model.

Science.gov (United States)

Nay, John J

2017-01-01

Out of nearly 70,000 bills introduced in the U.S. Congress from 2001 to 2015, only 2,513 were enacted. We developed a machine learning approach to forecasting the probability that any bill will become law. Starting in 2001 with the 107th Congress, we trained models on data from previous Congresses, predicted all bills in the current Congress, and repeated until the 113th Congress served as the test. For prediction we scored each sentence of a bill with a language model that embeds legislative vocabulary into a high-dimensional, semantic-laden vector space. This language representation enables our investigation into which words increase the probability of enactment for any topic. To test the relative importance of text and context, we compared the text model to a context-only model that uses variables such as whether the bill's sponsor is in the majority party. To test the effect of changes to bills after their introduction on our ability to predict their final outcome, we compared using the bill text and meta-data available at the time of introduction with using the most recent data. At the time of introduction context-only predictions outperform text-only, and with the newest data text-only outperforms context-only. Combining text and context always performs best. We conducted a global sensitivity analysis on the combined model to determine important variables predicting enactment.

Linear analysis of signal and noise characteristics of a nonlinear CMOS active-pixel detector for mammography

Energy Technology Data Exchange (ETDEWEB)

Yun, Seungman [School of Mechanical Engineering, Pusan National University, Busan 46241 (Korea, Republic of); Kim, Ho Kyung, E-mail: hokyung@pusan.ac.kr [School of Mechanical Engineering, Pusan National University, Busan 46241 (Korea, Republic of); Center for Advanced Medical Engineering Research, Pusan National University, Busan 46241 (Korea, Republic of); Han, Jong Chul; Kam, Soohwa [School of Mechanical Engineering, Pusan National University, Busan 46241 (Korea, Republic of); Youn, Hanbean [Department of Radiation Oncology, Pusan National University Yangsan Hospital, Yangsan, Gyeongsangnam-do 50612 (Korea, Republic of); Cunningham, Ian A. [Robarts Research Institute, Western University, London, Ontario N6A 5C1 (Canada)

2017-03-01

The imaging properties of a complementary metal-oxide-semiconductor (CMOS) active-pixel photodiode array coupled to a thin gadolinium-based granular phosphor screen with a fiber-optic faceplate are investigated. It is shown that this system has a nonlinear response at low detector exposure levels (<10 mR), resulting in an over-estimation of the detective quantum efficiency (DQE) by a factor of two in some cases. Errors in performance metrics on this scale make it difficult to compare new technologies with established systems and predict performance benchmarks that can be achieved in practice and help understand performance bottlenecks. It is shown the CMOS response is described by a power-law model that can be used to linearize image data. Linearization removed an unexpected dependence of the DQE on detector exposure level. - Highlights: • A nonlinear response of a CMOS detector at low exposure levels can overestimate DQE. • A power-law form can model the response of a CMOS detector at low exposure levels, and can be used to linearize image data. • Performance evaluation of nonlinear imaging systems must incorporate adequate linearizations.

Linear genetic programming application for successive-station monthly streamflow prediction

Science.gov (United States)

Danandeh Mehr, Ali; Kahya, Ercan; Yerdelen, Cahit

2014-09-01

In recent decades, artificial intelligence (AI) techniques have been pronounced as a branch of computer science to model wide range of hydrological phenomena. A number of researches have been still comparing these techniques in order to find more effective approaches in terms of accuracy and applicability. In this study, we examined the ability of linear genetic programming (LGP) technique to model successive-station monthly streamflow process, as an applied alternative for streamflow prediction. A comparative efficiency study between LGP and three different artificial neural network algorithms, namely feed forward back propagation (FFBP), generalized regression neural networks (GRNN), and radial basis function (RBF), has also been presented in this study. For this aim, firstly, we put forward six different successive-station monthly streamflow prediction scenarios subjected to training by LGP and FFBP using the field data recorded at two gauging stations on Çoruh River, Turkey. Based on Nash-Sutcliffe and root mean squared error measures, we then compared the efficiency of these techniques and selected the best prediction scenario. Eventually, GRNN and RBF algorithms were utilized to restructure the selected scenario and to compare with corresponding FFBP and LGP. Our results indicated the promising role of LGP for successive-station monthly streamflow prediction providing more accurate results than those of all the ANN algorithms. We found an explicit LGP-based expression evolved by only the basic arithmetic functions as the best prediction model for the river, which uses the records of the both target and upstream stations.

A Non-Linear Upscaling Approach for Wind Turbines Blades Based on Stresses

NARCIS (Netherlands)

Castillo Capponi, P.; Van Bussel, G.J.W.; Ashuri, T.; Kallesoe, B.

2011-01-01

The linear scaling laws for upscaling wind turbine blades show a linear increase of stresses due to the weight. However, the stresses should remain the same for a suitable design. Application of linear scaling laws may lead to an upscaled blade that may not be any more a feasible design. In this

Effect of the concentration of magnetic grains on the linear-optical-absorption coefficient of ferrofluid-doped lyotropic mesophases: deviation from the Beer-Lambert law.

Science.gov (United States)

Cuppo, F L S; Gómez, S L; Figueiredo Neto, A M

2004-04-01

In this paper is reported a systematic experimental study of the linear-optical-absorption coefficient of ferrofluid-doped isotropic lyotropic mixtures as a function of the magnetic-grains concentration. The linear optical absorption of ferrolyomesophases increases in a nonlinear manner with the concentration of magnetic grains, deviating from the usual Beer-Lambert law. This behavior is associated to the presence of correlated micelles in the mixture which favors the formation of small-scale aggregates of magnetic grains (dimers), which have a higher absorption coefficient with respect to that of isolated grains. We propose that the indirect heating of the micelles via the ferrofluid grains (hyperthermia) could account for this nonlinear increase of the linear-optical-absorption coefficient as a function of the grains concentration.

A Decomposition Algorithm for Mean-Variance Economic Model Predictive Control of Stochastic Linear Systems

DEFF Research Database (Denmark)

Sokoler, Leo Emil; Dammann, Bernd; Madsen, Henrik

2014-01-01

This paper presents a decomposition algorithm for solving the optimal control problem (OCP) that arises in Mean-Variance Economic Model Predictive Control of stochastic linear systems. The algorithm applies the alternating direction method of multipliers to a reformulation of the OCP...

Using Hierarchical Linear Modelling to Examine Factors Predicting English Language Students' Reading Achievement

Science.gov (United States)

Fung, Karen; ElAtia, Samira

2015-01-01

Using Hierarchical Linear Modelling (HLM), this study aimed to identify factors such as ESL/ELL/EAL status that would predict students' reading performance in an English language arts exam taken across Canada. Using data from the 2007 administration of the Pan-Canadian Assessment Program (PCAP) along with the accompanying surveys for students and…

QSAR models for prediction study of HIV protease inhibitors using support vector machines, neural networks and multiple linear regression

Directory of Open Access Journals (Sweden)

Rachid Darnag

2017-02-01

Full Text Available Support vector machines (SVM represent one of the most promising Machine Learning (ML tools that can be applied to develop a predictive quantitative structure–activity relationship (QSAR models using molecular descriptors. Multiple linear regression (MLR and artificial neural networks (ANNs were also utilized to construct quantitative linear and non linear models to compare with the results obtained by SVM. The prediction results are in good agreement with the experimental value of HIV activity; also, the results reveal the superiority of the SVM over MLR and ANN model. The contribution of each descriptor to the structure–activity relationships was evaluated.

Exact solutions to robust control problems involving scalar hyperbolic conservation laws using Mixed Integer Linear Programming

KAUST Repository

Li, Yanning

2013-10-01

This article presents a new robust control framework for transportation problems in which the state is modeled by a first order scalar conservation law. Using an equivalent formulation based on a Hamilton-Jacobi equation, we pose the problem of controlling the state of the system on a network link, using boundary flow control, as a Linear Program. Unlike many previously investigated transportation control schemes, this method yields a globally optimal solution and is capable of handling shocks (i.e. discontinuities in the state of the system). We also demonstrate that the same framework can handle robust control problems, in which the uncontrollable components of the initial and boundary conditions are encoded in intervals on the right hand side of inequalities in the linear program. The lower bound of the interval which defines the smallest feasible solution set is used to solve the robust LP (or MILP if the objective function depends on boolean variables). Since this framework leverages the intrinsic properties of the Hamilton-Jacobi equation used to model the state of the system, it is extremely fast. Several examples are given to demonstrate the performance of the robust control solution and the trade-off between the robustness and the optimality. © 2013 IEEE.

Exact solutions to robust control problems involving scalar hyperbolic conservation laws using Mixed Integer Linear Programming

KAUST Repository

Li, Yanning; Canepa, Edward S.; Claudel, Christian G.

2013-01-01

This article presents a new robust control framework for transportation problems in which the state is modeled by a first order scalar conservation law. Using an equivalent formulation based on a Hamilton-Jacobi equation, we pose the problem of controlling the state of the system on a network link, using boundary flow control, as a Linear Program. Unlike many previously investigated transportation control schemes, this method yields a globally optimal solution and is capable of handling shocks (i.e. discontinuities in the state of the system). We also demonstrate that the same framework can handle robust control problems, in which the uncontrollable components of the initial and boundary conditions are encoded in intervals on the right hand side of inequalities in the linear program. The lower bound of the interval which defines the smallest feasible solution set is used to solve the robust LP (or MILP if the objective function depends on boolean variables). Since this framework leverages the intrinsic properties of the Hamilton-Jacobi equation used to model the state of the system, it is extremely fast. Several examples are given to demonstrate the performance of the robust control solution and the trade-off between the robustness and the optimality. © 2013 IEEE.

Discontinuous Galerkin Method for Hyperbolic Conservation Laws

KAUST Repository

Mousikou, Ioanna

2016-11-11

Hyperbolic conservation laws form a special class of partial differential equations. They describe phenomena that involve conserved quantities and their solutions show discontinuities which reflect the formation of shock waves. We consider one-dimensional systems of hyperbolic conservation laws and produce approximations using finite difference, finite volume and finite element methods. Due to stability issues of classical finite element methods for hyperbolic conservation laws, we study the discontinuous Galerkin method, which was recently introduced. The method involves completely discontinuous basis functions across each element and it can be considered as a combination of finite volume and finite element methods. We illustrate the implementation of discontinuous Galerkin method using Legendre polynomials, in case of scalar equations and in case of quasi-linear systems, and we review important theoretical results about stability and convergence of the method. The applications of finite volume and discontinuous Galerkin methods to linear and non-linear scalar equations, as well as to the system of elastodynamics, are exhibited.

Discontinuous Galerkin Method for Hyperbolic Conservation Laws

KAUST Repository

Mousikou, Ioanna

2016-01-01

Hyperbolic conservation laws form a special class of partial differential equations. They describe phenomena that involve conserved quantities and their solutions show discontinuities which reflect the formation of shock waves. We consider one-dimensional systems of hyperbolic conservation laws and produce approximations using finite difference, finite volume and finite element methods. Due to stability issues of classical finite element methods for hyperbolic conservation laws, we study the discontinuous Galerkin method, which was recently introduced. The method involves completely discontinuous basis functions across each element and it can be considered as a combination of finite volume and finite element methods. We illustrate the implementation of discontinuous Galerkin method using Legendre polynomials, in case of scalar equations and in case of quasi-linear systems, and we review important theoretical results about stability and convergence of the method. The applications of finite volume and discontinuous Galerkin methods to linear and non-linear scalar equations, as well as to the system of elastodynamics, are exhibited.

On the derivation of the ionisation threshold law

International Nuclear Information System (INIS)

Peterkop, R.

1983-01-01

The different procedures for derivation of the electron-atom ionisation threshold law have been analysed and the reasons for discrepancies in the results are pointed out. It is shown that if the wavefunction has a linear node at equal electron distances (r 1 =r 2 ), then the threshold law for the total cross section has the form σ approx. Esup(3m), where σ approx. Esup(m) is the Wannier law. The distribution of energy between escaping electrons is non-uniform and has a parabolic node at equal energies (epsilon 1 = epsilon 2 ). The linear node at opposite directions of electrons (theta = π) does not change the Wannier law but leads to a parabolic node in angular distribution at theta = π. The existence of both nodes leads to the threshold law σ approx. Esup(3m) and to parabolic nodes in energy and angular distributions. (author)

Catastrophic Failure and Critical Scaling Laws of Fiber Bundle Material

Directory of Open Access Journals (Sweden)

Shengwang Hao

2017-05-01

Full Text Available This paper presents a spring-fiber bundle model used to describe the failure process induced by energy release in heterogeneous materials. The conditions that induce catastrophic failure are determined by geometric conditions and energy equilibrium. It is revealed that the relative rates of deformation of, and damage to the fiber bundle with respect to the boundary controlling displacement ε0 exhibit universal power law behavior near the catastrophic point, with a critical exponent of −1/2. The proportion of the rate of response with respect to acceleration exhibits a linear relationship with increasing displacement in the vicinity of the catastrophic point. This allows for the prediction of catastrophic failure immediately prior to failure by extrapolating the trajectory of this relationship as it asymptotes to zero. Monte Carlo simulations are completed and these two critical scaling laws are confirmed.

Linear regression

CERN Document Server

Olive, David J

2017-01-01

This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...

Position Control of Linear Synchronous Motor Drives with Exploitation of Forced Dynamics Control Principles

Directory of Open Access Journals (Sweden)

Jan Vittek

2004-01-01

Full Text Available Closed-loop position control of mechanisms directly driven by linear synchronous motors with permanent magnets is presented. The control strategy is based on forced dynamic control, which is a form of feedback linearisation, yielding a non-liner multivariable control law to obtain a prescribed linear speed dynamics together with the vector control condition of mutal orthogonality between the stator current and magnetic flux vectors (assuming perfect estimates of the plant parameters. Outer position control loop is closed via simple feedback with proportional gain. Simulations of the design control sysstem, including the drive with power electronic switching, predict the intended drive performance.

MULTIPLE LINEAR REGRESSION ANALYSIS FOR PREDICTION OF BOILER LOSSES AND BOILER EFFICIENCY

OpenAIRE

Chayalakshmi C.L

2018-01-01

MULTIPLE LINEAR REGRESSION ANALYSIS FOR PREDICTION OF BOILER LOSSES AND BOILER EFFICIENCY ABSTRACT Calculation of boiler efficiency is essential if its parameters need to be controlled for either maintaining or enhancing its efficiency. But determination of boiler efficiency using conventional method is time consuming and very expensive. Hence, it is not recommended to find boiler efficiency frequently. The work presented in this paper deals with establishing the statistical mo...

Can Law Foster Social-Ecological Resilience?

Directory of Open Access Journals (Sweden)

Ahjond S. Garmestani

2013-06-01

Full Text Available Law plays an essential role in shaping natural resource and environmental policy, but unfortunately, many environmental laws were developed around the prevailing scientific understanding that there was a "balance of nature" that could be managed and sustained. This view assumes that natural resource managers have the capacity to predict the behavior of ecological systems, know what its important functional components are, and successfully predict the outcome of management interventions. This paper takes on this problem by summarizing and synthesizing the contributions to this Special Feature (Law and Social-Ecological Resilience, Part I: Contributions from Resilience 2011, focusing on the interaction of law and social-ecological resilience, and then offering recommendations for the integration of law and social-ecological resilience.

Optimal Control of Scalar Conservation Laws Using Linear/Quadratic Programming: Application to Transportation Networks

KAUST Repository

Li, Yanning

2014-03-01

This article presents a new optimal control framework for transportation networks in which the state is modeled by a first order scalar conservation law. Using an equivalent formulation based on a Hamilton-Jacobi (H-J) equation and the commonly used triangular fundamental diagram, we pose the problem of controlling the state of the system on a network link, in a finite horizon, as a Linear Program (LP). We then show that this framework can be extended to an arbitrary transportation network, resulting in an LP or a Quadratic Program. Unlike many previously investigated transportation network control schemes, this method yields a globally optimal solution and is capable of handling shocks (i.e., discontinuities in the state of the system). As it leverages the intrinsic properties of the H-J equation used to model the state of the system, it does not require any approximation, unlike classical methods that are based on discretizations of the model. The computational efficiency of the method is illustrated on a transportation network. © 2014 IEEE.

Optimal Control of Scalar Conservation Laws Using Linear/Quadratic Programming: Application to Transportation Networks

KAUST Repository

Li, Yanning; Canepa, Edward S.; Claudel, Christian

2014-01-01

This article presents a new optimal control framework for transportation networks in which the state is modeled by a first order scalar conservation law. Using an equivalent formulation based on a Hamilton-Jacobi (H-J) equation and the commonly used triangular fundamental diagram, we pose the problem of controlling the state of the system on a network link, in a finite horizon, as a Linear Program (LP). We then show that this framework can be extended to an arbitrary transportation network, resulting in an LP or a Quadratic Program. Unlike many previously investigated transportation network control schemes, this method yields a globally optimal solution and is capable of handling shocks (i.e., discontinuities in the state of the system). As it leverages the intrinsic properties of the H-J equation used to model the state of the system, it does not require any approximation, unlike classical methods that are based on discretizations of the model. The computational efficiency of the method is illustrated on a transportation network. © 2014 IEEE.

Variables Predicting Foreign Language Reading Comprehension and Vocabulary Acquisition in a Linear Hypermedia Environment

Science.gov (United States)

Akbulut, Yavuz

2007-01-01

Factors predicting vocabulary learning and reading comprehension of advanced language learners of English in a linear multimedia text were investigated in the current study. Predictor variables of interest were multimedia type, reading proficiency, learning styles, topic interest and background knowledge about the topic. The outcome variables of…

A comparison of random forest regression and multiple linear regression for prediction in neuroscience.

Science.gov (United States)

Smith, Paul F; Ganesh, Siva; Liu, Ping

2013-10-30

Regression is a common statistical tool for prediction in neuroscience. However, linear regression is by far the most common form of regression used, with regression trees receiving comparatively little attention. In this study, the results of conventional multiple linear regression (MLR) were compared with those of random forest regression (RFR), in the prediction of the concentrations of 9 neurochemicals in the vestibular nucleus complex and cerebellum that are part of the l-arginine biochemical pathway (agmatine, putrescine, spermidine, spermine, l-arginine, l-ornithine, l-citrulline, glutamate and γ-aminobutyric acid (GABA)). The R(2) values for the MLRs were higher than the proportion of variance explained values for the RFRs: 6/9 of them were ≥ 0.70 compared to 4/9 for RFRs. Even the variables that had the lowest R(2) values for the MLRs, e.g. ornithine (0.50) and glutamate (0.61), had much lower proportion of variance explained values for the RFRs (0.27 and 0.49, respectively). The RSE values for the MLRs were lower than those for the RFRs in all but two cases. In general, MLRs seemed to be superior to the RFRs in terms of predictive value and error. In the case of this data set, MLR appeared to be superior to RFR in terms of its explanatory value and error. This result suggests that MLR may have advantages over RFR for prediction in neuroscience with this kind of data set, but that RFR can still have good predictive value in some cases. Copyright © 2013 Elsevier B.V. All rights reserved.

Prediction of linear B-cell epitopes of hepatitis C virus for vaccine development

Science.gov (United States)

2015-01-01

Background High genetic heterogeneity in the hepatitis C virus (HCV) is the major challenge of the development of an effective vaccine. Existing studies for developing HCV vaccines have mainly focused on T-cell immune response. However, identification of linear B-cell epitopes that can stimulate B-cell response is one of the major tasks of peptide-based vaccine development. Owing to the variability in B-cell epitope length, the prediction of B-cell epitopes is much more complex than that of T-cell epitopes. Furthermore, the motifs of linear B-cell epitopes in different pathogens are quite different (e. g. HCV and hepatitis B virus). To cope with this challenge, this work aims to propose an HCV-customized sequence-based prediction method to identify B-cell epitopes of HCV. Results This work establishes an experimentally verified dataset comprising the B-cell response of HCV dataset consisting of 774 linear B-cell epitopes and 774 non B-cell epitopes from the Immune Epitope Database. An interpretable rule mining system of B-cell epitopes (IRMS-BE) is proposed to select informative physicochemical properties (PCPs) and then extracts several if-then rule-based knowledge for identifying B-cell epitopes. A web server Bcell-HCV was implemented using an SVM with the 34 informative PCPs, which achieved a training accuracy of 79.7% and test accuracy of 70.7% better than the SVM-based methods for identifying B-cell epitopes of HCV and the two general-purpose methods. This work performs advanced analysis of the 34 informative properties, and the results indicate that the most effective property is the alpha-helix structure of epitopes, which influences the connection between host cells and the E2 proteins of HCV. Furthermore, 12 interpretable rules are acquired from top-five PCPs and achieve a sensitivity of 75.6% and specificity of 71.3%. Finally, a conserved promising vaccine candidate, PDREMVLYQE, is identified for inclusion in a vaccine against HCV. Conclusions This work

Universal Inverse Power-Law Distribution for Fractal Fluctuations in Dynamical Systems: Applications for Predictability of Inter-Annual Variability of Indian and USA Region Rainfall

Science.gov (United States)

Selvam, A. M.

2017-01-01

Dynamical systems in nature exhibit self-similar fractal space-time fluctuations on all scales indicating long-range correlations and, therefore, the statistical normal distribution with implicit assumption of independence, fixed mean and standard deviation cannot be used for description and quantification of fractal data sets. The author has developed a general systems theory based on classical statistical physics for fractal fluctuations which predicts the following. (1) The fractal fluctuations signify an underlying eddy continuum, the larger eddies being the integrated mean of enclosed smaller-scale fluctuations. (2) The probability distribution of eddy amplitudes and the variance (square of eddy amplitude) spectrum of fractal fluctuations follow the universal Boltzmann inverse power law expressed as a function of the golden mean. (3) Fractal fluctuations are signatures of quantum-like chaos since the additive amplitudes of eddies when squared represent probability densities analogous to the sub-atomic dynamics of quantum systems such as the photon or electron. (4) The model predicted distribution is very close to statistical normal distribution for moderate events within two standard deviations from the mean but exhibits a fat long tail that are associated with hazardous extreme events. Continuous periodogram power spectral analyses of available GHCN annual total rainfall time series for the period 1900-2008 for Indian and USA stations show that the power spectra and the corresponding probability distributions follow model predicted universal inverse power law form signifying an eddy continuum structure underlying the observed inter-annual variability of rainfall. On a global scale, man-made greenhouse gas related atmospheric warming would result in intensification of natural climate variability, seen immediately in high frequency fluctuations such as QBO and ENSO and even shorter timescales. Model concepts and results of analyses are discussed with reference

Impact of Behavioral Symptoms in Dementia Patients on Depression in Daughter and Daughter-in-Law Caregivers.

Science.gov (United States)

Lee, Juwon; Sohn, Bo Kyung; Lee, Hyunjoo; Seong, Sujeong; Park, Soowon; Lee, Jun-Young

2017-01-01

One caregiver relationship that has been neglected in caregiver depression research is the daughter-in-law. Compared with Western countries, in which those who are closer in familial relationships such as the spouse or child usually take care of the patient, in many Asian countries, the daughter-in-law often assumes the caretaker role. However, not much research has been done on how this relationship may result in different caregiver outcomes. We sought to identify whether the association between patient characteristics and caregiver depressive symptoms differs according to the familial relationship between caregiver and patient. Ninety-five daughter (n = 47) and daughter-in-law (n = 48) caregivers of dementia patients were asked to report their own depressive symptoms and patient behavioral symptoms. Patients' cognitive abilities, daily activities, and global dementia ratings were obtained. Hierarchical linear regression was employed to determine predictors of depressive symptoms. Daughters-in-law had marginally higher depressive scores. After adjusting for caregiver and patient characteristics, in both groups, greater dependency in activities of daily living and more severe and frequent behavioral symptoms predicted higher caregiver depressive scores. However, greater severity and frequency of behavioral symptoms predicted depression to a greater degree in daughters compared with daughters-in-law. Although behavioral symptoms predicted depression in both caregiver groups, the association was much stronger for daughters. This suggests that the emotional relationship between the daughter and patient exacerbates the negative effect of behavioral symptoms on caregiver depression. The familial relationship between the caregiver and dementia patient should be considered in managing caregiver stress.

Flexible non-linear predictive models for large-scale wind turbine diagnostics

DEFF Research Database (Denmark)

Bach-Andersen, Martin; Rømer-Odgaard, Bo; Winther, Ole

2017-01-01

We demonstrate how flexible non-linear models can provide accurate and robust predictions on turbine component temperature sensor data using data-driven principles and only a minimum of system modeling. The merits of different model architectures are evaluated using data from a large set...... of turbines operating under diverse conditions. We then go on to test the predictive models in a diagnostic setting, where the output of the models are used to detect mechanical faults in rotor bearings. Using retrospective data from 22 actual rotor bearing failures, the fault detection performance...... of the models are quantified using a structured framework that provides the metrics required for evaluating the performance in a fleet wide monitoring setup. It is demonstrated that faults are identified with high accuracy up to 45 days before a warning from the hard-threshold warning system....

Effect of continuum damage mechanics on spring back prediction in metal forming processes

International Nuclear Information System (INIS)

Nayebi, Ali; Shahabi, Mehdi

2017-01-01

The influence of considering the variations in material properties was investigated through continuum damage mechanics according to the Lemaitre isotropic unified damage law to predict the bending force and spring back in V-bending sheet metal forming processes, with emphasis on Finite element (FE) simulation considerations. The material constants of the damage model were calibrated through a uniaxial tensile test with an appropriate and convenient repeating strategy. Holloman’s isotropic and Ziegler’s linear kinematic hardening laws were employed to describe the behavior of a hardening material. To specify the ideal FE conditions for simulating spring back, the effect of the various numerical considerations during FE simulation was investigated and compared with the experimental outcome. Results indicate that considering continuum damage mechanics decreased the predicted bending force and improved the accuracy of spring back prediction.

Steinmetz law in iron–phenolformaldehyde resin soft magnetic composites

International Nuclear Information System (INIS)

Kollár, Peter; Vojtek, Vladimír; Birčáková, Zuzana; Füzer, Ján; Fáberová, Mária; Bureš, Radovan

2014-01-01

The validity of Steinmetz law describing the dc energy losses as a function of maximum induction has been investigated for iron based soft magnetic composites (SMCs) up to 1.4 T with the effort to find a physical meaning of the coefficients in Steinmetz law. In the Rayleigh region the coefficients were expressed mathematically using the Rayleigh law. Further the “range of validity of Steinmetz law” was found to be from 0.3 T to 1.2 T. The typical “straight” shape of hysteresis loops of SMCs at lower maximum induction was approximated by linear functions in order to express the dc losses in form of Steinmetz law. - Highlights: • The exponent x in Steinmetz law in Rayleigh region for Fe-based SMC is equal to 3. • The validity of Steinmetz law is from 0.3 T to 1.2 T with exponent x=1.5. • The straight shape of hysteresis loop is approximated by linear functions. • This approximation provides the relation for dc losses in form of Steinmetz law

The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

International Nuclear Information System (INIS)

Jahandideh, Sepideh; Jahandideh, Samad; Asadabadi, Ebrahim Barzegari; Askarian, Mehrdad; Movahedi, Mohammad Mehdi; Hosseini, Somayyeh; Jahandideh, Mina

2009-01-01

Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R 2 were used to evaluate performance of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R 2 confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.

Power laws for gravity and topography of Solar System bodies

Science.gov (United States)

Ermakov, A.; Park, R. S.; Bills, B. G.

2017-12-01

When a spacecraft visits a planetary body, it is useful to be able to predict its gravitational and topographic properties. This knowledge is important for determining the level of perturbations in spacecraft's motion as well as for planning the observation campaign. It has been known for the Earth that the power spectrum of gravity follows a power law, also known as the Kaula rule (Kaula, 1963; Rapp, 1989). A similar rule was derived for topography (Vening-Meinesz, 1951). The goal of this paper is to generalize the power law that can characterize the gravity and topography power spectra for bodies across a wide range of size. We have analyzed shape power spectra of the bodies that have either global shape and gravity field measured. These bodies span across five orders of magnitude in their radii and surface gravities and include terrestrial planets, icy moons and minor bodies. We have found that despite having different internal structure, composition and mechanical properties, the topography power spectrum of these bodies' shapes can be modeled with a similar power law rescaled by the surface gravity. Having empirically found a power law for topography, we can map it to a gravity power law. Special care should be taken for low-degree harmonic coefficients due to potential isostatic compensation. For minor bodies, uniform density can be assumed. The gravity coefficients are a linear function of the shape coefficients for close-to-spherical bodoes. In this case, the power law for gravity will be steeper than the power law of topography due to the factor (2n+1) in the gravity expansion (e.g. Eq. 10 in Wieczorek & Phillips, 1998). Higher powers of topography must be retained for irregularly shaped bodies, which breaks the linearity. Therefore, we propose the following procedure to derive an a priori constraint for gravity. First, a surface gravity needs to be determined assuming typical density for the relevant class of bodies. Second, the scaling coefficient of the

Improved prediction of residue flexibility by embedding optimized amino acid grouping into RSA-based linear models.

Science.gov (United States)

Zhang, Hua; Kurgan, Lukasz

2014-12-01

Knowledge of protein flexibility is vital for deciphering the corresponding functional mechanisms. This knowledge would help, for instance, in improving computational drug design and refinement in homology-based modeling. We propose a new predictor of the residue flexibility, which is expressed by B-factors, from protein chains that use local (in the chain) predicted (or native) relative solvent accessibility (RSA) and custom-derived amino acid (AA) alphabets. Our predictor is implemented as a two-stage linear regression model that uses RSA-based space in a local sequence window in the first stage and a reduced AA pair-based space in the second stage as the inputs. This method is easy to comprehend explicit linear form in both stages. Particle swarm optimization was used to find an optimal reduced AA alphabet to simplify the input space and improve the prediction performance. The average correlation coefficients between the native and predicted B-factors measured on a large benchmark dataset are improved from 0.65 to 0.67 when using the native RSA values and from 0.55 to 0.57 when using the predicted RSA values. Blind tests that were performed on two independent datasets show consistent improvements in the average correlation coefficients by a modest value of 0.02 for both native and predicted RSA-based predictions.

Interim evaluation of the effect of a new scrum law on neck and back injuries in rugby union.

Science.gov (United States)

Gianotti, S; Hume, P A; Hopkins, W G; Harawira, J; Truman, R

2008-06-01

In January 2007 the International Rugby Board implemented a new law for scrum engagement aimed at improving player welfare by reducing impact force and scrum collapses. In New Zealand the new law was included in RugbySmart, an annual compulsory workshop for coaches and referees. To determine the effect of the new law on scrum-related moderate to serious neck and back injury claims in 2007. Claims filed with the Accident Compensation Corporation (the provider of no-fault injury compensation and rehabilitation in New Zealand) were combined with numbers of registered players to estimate moderate to serious scrum-related claims for players who take part in scrums (forwards). Poisson linear regression was used to compare the observed claims per 100 000 forwards for 2007 with the rate predicted from data for 2002-6. The observed and predicted claims per 100 000 forwards were 52 and 76, respectively (rate ratio 0.69; 90% CI 0.42 to 1.12). The likelihoods of substantial benefit (rate ratio 1.1) attributable to the scrum law were 82% and 5%, respectively. The decline in scrum-related injury claims is consistent with a beneficial effect of the new scrum law in the first year of its implementation. Another year of monitoring should provide more evidence for the efficacy of the new law.

A simple method for HPLC retention time prediction: linear calibration using two reference substances.

Science.gov (United States)

Sun, Lei; Jin, Hong-Yu; Tian, Run-Tao; Wang, Ming-Juan; Liu, Li-Na; Ye, Liu-Ping; Zuo, Tian-Tian; Ma, Shuang-Cheng

2017-01-01

Analysis of related substances in pharmaceutical chemicals and multi-components in traditional Chinese medicines needs bulk of reference substances to identify the chromatographic peaks accurately. But the reference substances are costly. Thus, the relative retention (RR) method has been widely adopted in pharmacopoeias and literatures for characterizing HPLC behaviors of those reference substances unavailable. The problem is it is difficult to reproduce the RR on different columns due to the error between measured retention time (t R ) and predicted t R in some cases. Therefore, it is useful to develop an alternative and simple method for prediction of t R accurately. In the present study, based on the thermodynamic theory of HPLC, a method named linear calibration using two reference substances (LCTRS) was proposed. The method includes three steps, procedure of two points prediction, procedure of validation by multiple points regression and sequential matching. The t R of compounds on a HPLC column can be calculated by standard retention time and linear relationship. The method was validated in two medicines on 30 columns. It was demonstrated that, LCTRS method is simple, but more accurate and more robust on different HPLC columns than RR method. Hence quality standards using LCTRS method are easy to reproduce in different laboratories with lower cost of reference substances.

[Prediction model of health workforce and beds in county hospitals of Hunan by multiple linear regression].

Science.gov (United States)

Ling, Ru; Liu, Jiawang

2011-12-01

To construct prediction model for health workforce and hospital beds in county hospitals of Hunan by multiple linear regression. We surveyed 16 counties in Hunan with stratified random sampling according to uniform questionnaires,and multiple linear regression analysis with 20 quotas selected by literature view was done. Independent variables in the multiple linear regression model on medical personnels in county hospitals included the counties' urban residents' income, crude death rate, medical beds, business occupancy, professional equipment value, the number of devices valued above 10 000 yuan, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, and utilization rate of hospital beds. Independent variables in the multiple linear regression model on county hospital beds included the the population of aged 65 and above in the counties, disposable income of urban residents, medical personnel of medical institutions in county area, business occupancy, the total value of professional equipment, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, utilization rate of hospital beds, and length of hospitalization. The prediction model shows good explanatory and fitting, and may be used for short- and mid-term forecasting.

Burgers' turbulence problem with linear or quadratic external potential

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole Eiler; Leonenko, N.N.

2005-01-01

We consider solutions of Burgers' equation with linear or quadratic external potential and stationary random initial conditions of Ornstein-Uhlenbeck type. We study a class of limit laws that correspond to a scale renormalization of the solutions.......We consider solutions of Burgers' equation with linear or quadratic external potential and stationary random initial conditions of Ornstein-Uhlenbeck type. We study a class of limit laws that correspond to a scale renormalization of the solutions....

Renormalization, averaging, conservation laws and AdS (in)stability

International Nuclear Information System (INIS)

Craps, Ben; Evnin, Oleg; Vanhoof, Joris

2015-01-01

We continue our analytic investigations of non-linear spherically symmetric perturbations around the anti-de Sitter background in gravity-scalar field systems, and focus on conservation laws restricting the (perturbatively) slow drift of energy between the different normal modes due to non-linearities. We discover two conservation laws in addition to the energy conservation previously discussed in relation to AdS instability. A similar set of three conservation laws was previously noted for a self-interacting scalar field in a non-dynamical AdS background, and we highlight the similarities of this system to the fully dynamical case of gravitational instability. The nature of these conservation laws is best understood through an appeal to averaging methods which allow one to derive an effective Lagrangian or Hamiltonian description of the slow energy transfer between the normal modes. The conservation laws in question then follow from explicit symmetries of this averaged effective theory.

Comparison of Linear and Nonlinear Model Predictive Control for Optimization of Spray Dryer Operation

DEFF Research Database (Denmark)

Petersen, Lars Norbert; Poulsen, Niels Kjølstad; Niemann, Hans Henrik

2015-01-01

In this paper, we compare the performance of an economically optimizing Nonlinear Model Predictive Controller (E-NMPC) to a linear tracking Model Predictive Controller (MPC) for a spray drying plant. We find in this simulation study, that the economic performance of the two controllers are almost...... equal. We evaluate the economic performance with an industrially recorded disturbance scenario, where unmeasured disturbances and model mismatch are present. The state of the spray dryer, used in the E-NMPC and MPC, is estimated using Kalman Filters with noise covariances estimated by a maximum...

Practical scaling law for photoelectron angular distributions

International Nuclear Information System (INIS)

Guo Dongsheng; Zhang Jingtao; Xu Zhizhan; Li Xiaofeng; Fu Panming; Freeman, R.R.

2003-01-01

A practical scaling law that predicts photoelectron angular distributions (PADs) is derived using angular distribution formulas which explicitly contain spontaneous emission. The scaling law is used to analyze recent PAD measurements in above-threshold ionization, and to predict results of future experiments. Our theoretical and numerical studies show that, in the non-relativistic regime and long-wavelength approximation, the shapes of PADs are determined by only three dimensionless numbers: (1) u p ≡U p /(ℎ/2π)ω, the ponderomotive number (ponderomotive energy in units of laser photon energy); (2) ε b ≡E b /(ℎ/2π)ω, the binding number (atomic binding energy in units of the laser photon energy); (3) j, the absorbed-photon number. The scaling law is shown to be useful in predictions of results from strong-field Kapitza-Dirac effect measurements; specifically, the application of this scaling law to recently reported Kapitza-Dirac diffraction is discussed. Possible experimental tests to verify the scaling law are suggested

Towards Automated Binding Affinity Prediction Using an Iterative Linear Interaction Energy Approach

Directory of Open Access Journals (Sweden)

C. Ruben Vosmeer

2014-01-01

Full Text Available Binding affinity prediction of potential drugs to target and off-target proteins is an essential asset in drug development. These predictions require the calculation of binding free energies. In such calculations, it is a major challenge to properly account for both the dynamic nature of the protein and the possible variety of ligand-binding orientations, while keeping computational costs tractable. Recently, an iterative Linear Interaction Energy (LIE approach was introduced, in which results from multiple simulations of a protein-ligand complex are combined into a single binding free energy using a Boltzmann weighting-based scheme. This method was shown to reach experimental accuracy for flexible proteins while retaining the computational efficiency of the general LIE approach. Here, we show that the iterative LIE approach can be used to predict binding affinities in an automated way. A workflow was designed using preselected protein conformations, automated ligand docking and clustering, and a (semi-automated molecular dynamics simulation setup. We show that using this workflow, binding affinities of aryloxypropanolamines to the malleable Cytochrome P450 2D6 enzyme can be predicted without a priori knowledge of dominant protein-ligand conformations. In addition, we provide an outlook for an approach to assess the quality of the LIE predictions, based on simulation outcomes only.

Integrating piecewise linear representation and ensemble neural network for stock price prediction

OpenAIRE

Asaduzzaman, Md.; Shahjahan, Md.; Ahmed, Fatema Johera; Islam, Md. Monirul; Murase, Kazuyuki

2014-01-01

Stock Prices are considered to be very dynamic and susceptible to quick changes because of the underlying nature of the financial domain, and in part because of the interchange between known parameters and unknown factors. Of late, several researchers have used Piecewise Linear Representation (PLR) to predict the stock market pricing. However, some improvements are needed to avoid the appropriate threshold of the trading decision, choosing the input index as well as improving the overall perf...

A Memory-Based Model of Hick's Law

Science.gov (United States)

Schneider, Darryl W.; Anderson, John R.

2011-01-01

We propose and evaluate a memory-based model of Hick's law, the approximately linear increase in choice reaction time with the logarithm of set size (the number of stimulus-response alternatives). According to the model, Hick's law reflects a combination of associative interference during retrieval from declarative memory and occasional savings…

The law and neuroscience.

Science.gov (United States)

Gazzaniga, Michael S

2008-11-06

Some of the implications for law of recent discoveries in neuroscience are considered in a new program established by the MacArthur Foundation. A group of neuroscientists, lawyers, philosophers, and jurists are examining issues in criminal law and, in particular, problems in responsibility and prediction and problems in legal decision making.

The modified life law applied to 1045 steel

International Nuclear Information System (INIS)

Dowdell, D.J.; Leipholz, H.H.E.

1985-01-01

In lifetime prediction of components subject to cyclic loading of varying intensity the concept of damage summation is used. Data for such summation is usually taken from constant amplitude load-life curves. The simplest, and most common damage summation is the linear damage summation, or Miner's rule. Experimental evidence supporting the modified life law is presented. Fatigue tests were performed on smooth specimens of SAE 1045 steel. Strain was taken as the damage parameter. Tests were run on MTS fatigue machines in conjunction with a digital process control computer. By varying the proportion of cycles applied at each load level, the damage corresponding to each level could be found by solving three simultaneous equations in Miner's Rule

A simple model for determining photoelectron-generated radiation scaling laws

International Nuclear Information System (INIS)

Dipp, T.M.

1993-12-01

The generation of radiation via photoelectrons induced off of a conducting surface was explored using a simple model to determine fundamental scaling laws. The model is one-dimensional (small-spot) and uses monoenergetic, nonrelativistic photoelectrons emitted normal to the illuminated conducting surface. Simple steady-state radiation, frequency, and maximum orbital distance equations were derived using small-spot radiation equations, a sin 2 type modulation function, and simple photoelectron dynamics. The result is a system of equations for various scaling laws, which, along with model and user constraints, are simultaneously solved using techniques similar to linear programming. Typical conductors illuminated by low-power sources producing photons with energies less than 5.0 eV are readily modeled by this small-spot, steady-state analysis, which shows they generally produce low efficiency (η rsL -10.5 ) pure photoelectron-induced radiation. However, the small-spot theory predicts that the total conversion efficiency for incident photon power to photoelectron-induced radiated power can go higher than 10 -5.5 for typical real conductors if photons having energies of 15 eV and higher are used, and should go even higher still if the small-spot limit of this theory is exceeded as well. Overall, the simple theory equations, model constraint equations, and solution techniques presented provide a foundation for understanding, predicting, and optimizing the generated radiation, and the simple theory equations provide scaling laws to compare with computational and laboratory experimental data

A study on two phase flows of linear compressors for the prediction of refrigerant leakage

International Nuclear Information System (INIS)

Hwang, Il Sun; Lee, Young Lim; Oh, Won Sik; Park, Kyeong Bae

2015-01-01

Usage of linear compressors is on the rise due to their high efficiency. In this paper, leakage of a linear compressor has been studied through numerical analysis and experiments. First, nitrogen leakage for a stagnant piston with fixed cylinder pressure as well as for a moving piston with fixed cylinder pressure was analyzed to verify the validity of the two-phase flow analysis model. Next, refrigerant leakage of a linear compressor in operation was finally predicted through 3-dimensional unsteady, two phase flow CFD (Computational fluid dynamics). According to the research results, the numerical analyses for the fixed cylinder pressure models were in good agreement with the experimental results. The refrigerant leakage of the linear compressor in operation mainly occurred through the oil exit and the leakage became negligible after about 0.4s following operation where the leakage became lower than 2.0x10 -4 kg/s.

Off-Line Robust Constrained MPC for Linear Time-Varying Systems with Persistent Disturbances

Directory of Open Access Journals (Sweden)

P. Bumroongsri

2014-01-01

Full Text Available An off-line robust constrained model predictive control (MPC algorithm for linear time-varying (LTV systems is developed. A novel feature is the fact that both model uncertainty and bounded additive disturbance are explicitly taken into account in the off-line formulation of MPC. In order to reduce the on-line computational burdens, a sequence of explicit control laws corresponding to a sequence of positively invariant sets is computed off-line. At each sampling time, the smallest positively invariant set containing the measured state is determined and the corresponding control law is implemented in the process. The proposed MPC algorithm can guarantee robust stability while ensuring the satisfaction of input and output constraints. The effectiveness of the proposed MPC algorithm is illustrated by two examples.

Performance Characteristics and Prediction of Bodyweight using Linear Body Measurements in Four Strains of Broiler Chicken

OpenAIRE

I. Udeh; J.O. Isikwenu and G. Ukughere

2011-01-01

The objectives of this study were to compare the performance characteristics of four strains of broiler chicken from 2 to 8 weeks of age and predict body weight of the broilers using linear body measurements. The four strains of broiler chicken used were Anak, Arbor Acre, Ross and Marshall. The parameters recorded were bodyweight, weight gain, total feed intake, feed conversion ratio, mortality and some linear body measurements (body length, body width, breast width, drumstick length, shank l...

Plateletpheresis efficiency and mathematical correction of software-derived platelet yield prediction: A linear regression and ROC modeling approach.

Science.gov (United States)

Jaime-Pérez, José Carlos; Jiménez-Castillo, Raúl Alberto; Vázquez-Hernández, Karina Elizabeth; Salazar-Riojas, Rosario; Méndez-Ramírez, Nereida; Gómez-Almaguer, David

2017-10-01

Advances in automated cell separators have improved the efficiency of plateletpheresis and the possibility of obtaining double products (DP). We assessed cell processor accuracy of predicted platelet (PLT) yields with the goal of a better prediction of DP collections. This retrospective proof-of-concept study included 302 plateletpheresis procedures performed on a Trima Accel v6.0 at the apheresis unit of a hematology department. Donor variables, software predicted yield and actual PLT yield were statistically evaluated. Software prediction was optimized by linear regression analysis and its optimal cut-off to obtain a DP assessed by receiver operating characteristic curve (ROC) modeling. Three hundred and two plateletpheresis procedures were performed; in 271 (89.7%) occasions, donors were men and in 31 (10.3%) women. Pre-donation PLT count had the best direct correlation with actual PLT yield (r = 0.486. P Simple correction derived from linear regression analysis accurately corrected this underestimation and ROC analysis identified a precise cut-off to reliably predict a DP. © 2016 Wiley Periodicals, Inc.

Non-linear laws of echoic memory and auditory change detection in humans.

Science.gov (United States)

Inui, Koji; Urakawa, Tomokazu; Yamashiro, Koya; Otsuru, Naofumi; Nishihara, Makoto; Takeshima, Yasuyuki; Keceli, Sumru; Kakigi, Ryusuke

2010-07-03

The detection of any abrupt change in the environment is important to survival. Since memory of preceding sensory conditions is necessary for detecting changes, such a change-detection system relates closely to the memory system. Here we used an auditory change-related N1 subcomponent (change-N1) of event-related brain potentials to investigate cortical mechanisms underlying change detection and echoic memory. Change-N1 was elicited by a simple paradigm with two tones, a standard followed by a deviant, while subjects watched a silent movie. The amplitude of change-N1 elicited by a fixed sound pressure deviance (70 dB vs. 75 dB) was negatively correlated with the logarithm of the interval between the standard sound and deviant sound (1, 10, 100, or 1000 ms), while positively correlated with the logarithm of the duration of the standard sound (25, 100, 500, or 1000 ms). The amplitude of change-N1 elicited by a deviance in sound pressure, sound frequency, and sound location was correlated with the logarithm of the magnitude of physical differences between the standard and deviant sounds. The present findings suggest that temporal representation of echoic memory is non-linear and Weber-Fechner law holds for the automatic cortical response to sound changes within a suprathreshold range. Since the present results show that the behavior of echoic memory can be understood through change-N1, change-N1 would be a useful tool to investigate memory systems.

Dynamics and control of quadcopter using linear model predictive control approach

Science.gov (United States)

Islam, M.; Okasha, M.; Idres, M. M.

2017-12-01

This paper investigates the dynamics and control of a quadcopter using the Model Predictive Control (MPC) approach. The dynamic model is of high fidelity and nonlinear, with six degrees of freedom that include disturbances and model uncertainties. The control approach is developed based on MPC to track different reference trajectories ranging from simple ones such as circular to complex helical trajectories. In this control technique, a linearized model is derived and the receding horizon method is applied to generate the optimal control sequence. Although MPC is computer expensive, it is highly effective to deal with the different types of nonlinearities and constraints such as actuators’ saturation and model uncertainties. The MPC parameters (control and prediction horizons) are selected by trial-and-error approach. Several simulation scenarios are performed to examine and evaluate the performance of the proposed control approach using MATLAB and Simulink environment. Simulation results show that this control approach is highly effective to track a given reference trajectory.

Prediction of protein interaction hot spots using rough set-based multiple criteria linear programming.

Science.gov (United States)

Chen, Ruoying; Zhang, Zhiwang; Wu, Di; Zhang, Peng; Zhang, Xinyang; Wang, Yong; Shi, Yong

2011-01-21

Protein-protein interactions are fundamentally important in many biological processes and it is in pressing need to understand the principles of protein-protein interactions. Mutagenesis studies have found that only a small fraction of surface residues, known as hot spots, are responsible for the physical binding in protein complexes. However, revealing hot spots by mutagenesis experiments are usually time consuming and expensive. In order to complement the experimental efforts, we propose a new computational approach in this paper to predict hot spots. Our method, Rough Set-based Multiple Criteria Linear Programming (RS-MCLP), integrates rough sets theory and multiple criteria linear programming to choose dominant features and computationally predict hot spots. Our approach is benchmarked by a dataset of 904 alanine-mutated residues and the results show that our RS-MCLP method performs better than other methods, e.g., MCLP, Decision Tree, Bayes Net, and the existing HotSprint database. In addition, we reveal several biological insights based on our analysis. We find that four features (the change of accessible surface area, percentage of the change of accessible surface area, size of a residue, and atomic contacts) are critical in predicting hot spots. Furthermore, we find that three residues (Tyr, Trp, and Phe) are abundant in hot spots through analyzing the distribution of amino acids. Copyright © 2010 Elsevier Ltd. All rights reserved.

Application of viscoelastic, viscoplastic, and rate-and-state friction constitutive laws to the deformation of unconsolidated sands

Science.gov (United States)

Hagin, Paul N.

Laboratory experiments on dry, unconsolidated sands from the Wilmington field, CA, reveal significant viscous creep strain under a variety of loading conditions. In hydrostatic compression tests between 10 and 50 MPa of pressure, the creep strain exceeds the magnitude of the instantaneous strain and follows a power law function of time. Interestingly, the viscous effects only appear when loading a sample beyond its preconsolidation pressure. Cyclic loading tests (at quasi-static frequencies of 10-6 to 10 -2 Hz) show that the bulk modulus increases by a factor of two with increasing frequency while attenuation remains constant. I attempt to fit these observations using three classes of models: linear viscoelastic, viscoplastic, and rate-and-state friction models. For the linear viscoelastic modeling, I investigated two types of models; spring-dashpot (exponential) and power law models. I find that a combined power law-Maxwell solid creep model adequately fits all of the data. Extrapolating the power law-Maxwell creep model out to 30 years (to simulate the lifetime of a reservoir) predicts that the static bulk modulus is 25% of the dynamic modulus, in good agreement with field observations. Laboratory studies also reveal that a large portion of the deformation is permanent, suggesting that an elastic-plastic model is appropriate. However, because the viscous component of deformation is significant, an elastic-viscoplastic model is necessary. An appropriate model for unconsolidated sands is developed by incorporating Perzyna (power law) viscoplasticity theory into the modified Cambridge clay cap model. Hydrostatic compression tests conducted as a function of volumetric strain rate produced values for the required model parameters. As a result, by using an end cap model combined with power law viscoplasticity theory, changes in porosity in both the elastic and viscoplastic regimes can be predicted as a function of both stress path and strain rate. To test whether rate

An anisotropic linear thermo-viscoelastic constitutive law - Elastic relaxation and thermal expansion creep in the time domain

Science.gov (United States)

Pettermann, Heinz E.; DeSimone, Antonio

2017-09-01

A constitutive material law for linear thermo-viscoelasticity in the time domain is presented. The time-dependent relaxation formulation is given for full anisotropy, i.e., both the elastic and the viscous properties are anisotropic. Thereby, each element of the relaxation tensor is described by its own and independent Prony series expansion. Exceeding common viscoelasticity, time-dependent thermal expansion relaxation/creep is treated as inherent material behavior. The pertinent equations are derived and an incremental, implicit time integration scheme is presented. The developments are implemented into an implicit FEM software for orthotropic material symmetry under plane stress assumption. Even if this is a reduced problem, all essential features are present and allow for the entire verification and validation of the approach. Various simulations on isotropic and orthotropic problems are carried out to demonstrate the material behavior under investigation.

Predicting Fuel Ignition Quality Using 1H NMR Spectroscopy and Multiple Linear Regression

KAUST Repository

Abdul Jameel, Abdul Gani

2016-09-14

An improved model for the prediction of ignition quality of hydrocarbon fuels has been developed using 1H nuclear magnetic resonance (NMR) spectroscopy and multiple linear regression (MLR) modeling. Cetane number (CN) and derived cetane number (DCN) of 71 pure hydrocarbons and 54 hydrocarbon blends were utilized as a data set to study the relationship between ignition quality and molecular structure. CN and DCN are functional equivalents and collectively referred to as D/CN, herein. The effect of molecular weight and weight percent of structural parameters such as paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic CH–CH2 groups, naphthenic CH–CH2 groups, and aromatic C–CH groups on D/CN was studied. A particular emphasis on the effect of branching (i.e., methyl substitution) on the D/CN was studied, and a new parameter denoted as the branching index (BI) was introduced to quantify this effect. A new formula was developed to calculate the BI of hydrocarbon fuels using 1H NMR spectroscopy. Multiple linear regression (MLR) modeling was used to develop an empirical relationship between D/CN and the eight structural parameters. This was then used to predict the DCN of many hydrocarbon fuels. The developed model has a high correlation coefficient (R2 = 0.97) and was validated with experimentally measured DCN of twenty-two real fuel mixtures (e.g., gasolines and diesels) and fifty-nine blends of known composition, and the predicted values matched well with the experimental data.

Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

Science.gov (United States)

Drzewiecki, Wojciech

2016-12-01

In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.

A Dantzig-Wolfe decomposition algorithm for linear economic model predictive control of dynamically decoupled subsystems

DEFF Research Database (Denmark)

Sokoler, Leo Emil; Standardi, Laura; Edlund, Kristian

2014-01-01

This paper presents a warm-started Dantzig–Wolfe decomposition algorithm tailored to economic model predictive control of dynamically decoupled subsystems. We formulate the constrained optimal control problem solved at each sampling instant as a linear program with state space constraints, input...... limits, input rate limits, and soft output limits. The objective function of the linear program is related directly to the cost of operating the subsystems, and the cost of violating the soft output constraints. Simulations for large-scale economic power dispatch problems show that the proposed algorithm...... is significantly faster than both state-of-the-art linear programming solvers, and a structure exploiting implementation of the alternating direction method of multipliers. It is also demonstrated that the control strategy presented in this paper can be tuned using a weighted ℓ1-regularization term...

Linear positivity and virtual probability

International Nuclear Information System (INIS)

Hartle, James B.

2004-01-01

We investigate the quantum theory of closed systems based on the linear positivity decoherence condition of Goldstein and Page. The objective of any quantum theory of a closed system, most generally the universe, is the prediction of probabilities for the individual members of sets of alternative coarse-grained histories of the system. Quantum interference between members of a set of alternative histories is an obstacle to assigning probabilities that are consistent with the rules of probability theory. A quantum theory of closed systems therefore requires two elements: (1) a condition specifying which sets of histories may be assigned probabilities and (2) a rule for those probabilities. The linear positivity condition of Goldstein and Page is the weakest of the general conditions proposed so far. Its general properties relating to exact probability sum rules, time neutrality, and conservation laws are explored. Its inconsistency with the usual notion of independent subsystems in quantum mechanics is reviewed. Its relation to the stronger condition of medium decoherence necessary for classicality is discussed. The linear positivity of histories in a number of simple model systems is investigated with the aim of exhibiting linearly positive sets of histories that are not decoherent. The utility of extending the notion of probability to include values outside the range of 0-1 is described. Alternatives with such virtual probabilities cannot be measured or recorded, but can be used in the intermediate steps of calculations of real probabilities. Extended probabilities give a simple and general way of formulating quantum theory. The various decoherence conditions are compared in terms of their utility for characterizing classicality and the role they might play in further generalizations of quantum mechanics

Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network.

Science.gov (United States)

Gilra, Aditya; Gerstner, Wulfram

2017-11-27

The brain needs to predict how the body reacts to motor commands, but how a network of spiking neurons can learn non-linear body dynamics using local, online and stable learning rules is unclear. Here, we present a supervised learning scheme for the feedforward and recurrent connections in a network of heterogeneous spiking neurons. The error in the output is fed back through fixed random connections with a negative gain, causing the network to follow the desired dynamics. The rule for Feedback-based Online Local Learning Of Weights (FOLLOW) is local in the sense that weight changes depend on the presynaptic activity and the error signal projected onto the postsynaptic neuron. We provide examples of learning linear, non-linear and chaotic dynamics, as well as the dynamics of a two-link arm. Under reasonable approximations, we show, using the Lyapunov method, that FOLLOW learning is uniformly stable, with the error going to zero asymptotically.

Applying linear discriminant analysis to predict groundwater redox conditions conducive to denitrification

Science.gov (United States)

Wilson, S. R.; Close, M. E.; Abraham, P.

2018-01-01

Diffuse nitrate losses from agricultural land pollute groundwater resources worldwide, but can be attenuated under reducing subsurface conditions. In New Zealand, the ability to predict where groundwater denitrification occurs is important for understanding the linkage between land use and discharges of nitrate-bearing groundwater to streams. This study assesses the application of linear discriminant analysis (LDA) for predicting groundwater redox status for Southland, a major dairy farming region in New Zealand. Data cases were developed by assigning a redox status to samples derived from a regional groundwater quality database. Pre-existing regional-scale geospatial databases were used as training variables for the discriminant functions. The predictive accuracy of the discriminant functions was slightly improved by optimising the thresholds between sample depth classes. The models predict 23% of the region as being reducing at shallow depths (water table, and low-permeability clastic sediments. The coastal plains are an area of widespread groundwater discharge, and the soil and hydrology characteristics require the land to be artificially drained to render the land suitable for farming. For the improvement of water quality in coastal areas, it is therefore important that land and water management efforts focus on understanding hydrological bypassing that may occur via artificial drainage systems.

Homogenization of linear viscoelastic three phase media: internal variable formulation versus full-field computation

International Nuclear Information System (INIS)

Blanc, V.; Barbie, L.; Masson, R.

2011-01-01

Homogenization of linear viscoelastic heterogeneous media is here extended from two phase inclusion-matrix media to three phase inclusion-matrix media. Each phase obeying to a compressible Maxwellian behaviour, this analytic method leads to an equivalent elastic homogenization problem in the Laplace-Carson space. For some particular microstructures, such as the Hashin composite sphere assemblage, an exact solution is obtained. The inversion of the Laplace-Carson transforms of the overall stress-strain behaviour gives in such cases an internal variable formulation. As expected, the number of these internal variables and their evolution laws are modified to take into account the third phase. Moreover, evolution laws of averaged stresses and strains per phase can still be derived for three phase media. Results of this model are compared to full fields computations of representative volume elements using finite element method, for various concentrations and sizes of inclusion. Relaxation and creep test cases are performed in order to compare predictions of the effective response. The internal variable formulation is shown to yield accurate prediction in both cases. (authors)

Considering linear generator copper losses on model predictive control for a point absorber wave energy converter

International Nuclear Information System (INIS)

Montoya Andrade, Dan-El; Villa Jaén, Antonio de la; García Santana, Agustín

2014-01-01

Highlights: • We considered the linear generator copper losses in the proposed MPC strategy. • We maximized the power transferred to the generator side power converter. • The proposed MPC increases the useful average power injected into the grid. • The stress level of the PTO system can be reduced by the proposed MPC. - Abstract: The amount of energy that a wave energy converter can extract depends strongly on the control strategy applied to the power take-off system. It is well known that, ideally, the reactive control allows for maximum energy extraction from waves. However, the reactive control is intrinsically noncausal in practice and requires some kind of causal approach to be applied. Moreover, this strategy does not consider physical constraints and this could be a problem because the system could achieve unacceptable dynamic values. These, and other control techniques have focused on the wave energy extraction problem in order to maximize the energy absorbed by the power take-off device without considering the possible losses in intermediate devices. In this sense, a reactive control that considers the linear generator copper losses has been recently proposed to increase the useful power injected into the grid. Among the control techniques that have emerged recently, the model predictive control represents a promising strategy. This approach performs an optimization process on a time prediction horizon incorporating dynamic constraints associated with the physical features of the power take-off system. This paper proposes a model predictive control technique that considers the copper losses in the control optimization process of point absorbers with direct drive linear generators. This proposal makes the most of reactive control as it considers the copper losses, and it makes the most of the model predictive control, as it considers the system constraints. This means that the useful power transferred from the linear generator to the power

Linear Model-Based Predictive Control of the LHC 1.8 K Cryogenic Loop

CERN Document Server

Blanco-Viñuela, E; De Prada-Moraga, C

1999-01-01

The LHC accelerator will employ 1800 superconducting magnets (for guidance and focusing of the particle beams) in a pressurized superfluid helium bath at 1.9 K. This temperature is a severely constrained control parameter in order to avoid the transition from the superconducting to the normal state. Cryogenic processes are difficult to regulate due to their highly non-linear physical parameters (heat capacity, thermal conductance, etc.) and undesirable peculiarities like non self-regulating process, inverse response and variable dead time. To reduce the requirements on either temperature sensor or cryogenic system performance, various control strategies have been investigated on a reduced-scale LHC prototype built at CERN (String Test). Model Based Predictive Control (MBPC) is a regulation algorithm based on the explicit use of a process model to forecast the plant output over a certain prediction horizon. This predicted controlled variable is used in an on-line optimization procedure that minimizes an approp...

U.S. Army Armament Research, Development and Engineering Center Grain Evaluation Software to Numerically Predict Linear Burn Regression for Solid Propellant Grain Geometries

Science.gov (United States)

2017-10-01

ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID PROPELLANT GRAIN GEOMETRIES Brian...distribution is unlimited. AD U.S. ARMY ARMAMENT RESEARCH, DEVELOPMENT AND ENGINEERING CENTER Munitions Engineering Technology Center Picatinny...U.S. ARMY ARMAMENT RESEARCH, DEVELOPMENT AND ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID

Predicting respiratory motion signals for image-guided radiotherapy using multi-step linear methods (MULIN)

International Nuclear Information System (INIS)

Ernst, Floris; Schweikard, Achim

2008-01-01

Forecasting of respiration motion in image-guided radiotherapy requires algorithms that can accurately and efficiently predict target location. Improved methods for respiratory motion forecasting were developed and tested. MULIN, a new family of prediction algorithms based on linear expansions of the prediction error, was developed and tested. Computer-generated data with a prediction horizon of 150 ms was used for testing in simulation experiments. MULIN was compared to Least Mean Squares-based predictors (LMS; normalized LMS, nLMS; wavelet-based multiscale autoregression, wLMS) and a multi-frequency Extended Kalman Filter (EKF) approach. The in vivo performance of the algorithms was tested on data sets of patients who underwent radiotherapy. The new MULIN methods are highly competitive, outperforming the LMS and the EKF prediction algorithms in real-world settings and performing similarly to optimized nLMS and wLMS prediction algorithms. On simulated, periodic data the MULIN algorithms are outperformed only by the EKF approach due to its inherent advantage in predicting periodic signals. In the presence of noise, the MULIN methods significantly outperform all other algorithms. The MULIN family of algorithms is a feasible tool for the prediction of respiratory motion, performing as well as or better than conventional algorithms while requiring significantly lower computational complexity. The MULIN algorithms are of special importance wherever high-speed prediction is required. (orig.)

Predicting respiratory motion signals for image-guided radiotherapy using multi-step linear methods (MULIN)

Energy Technology Data Exchange (ETDEWEB)

Ernst, Floris; Schweikard, Achim [University of Luebeck, Institute for Robotics and Cognitive Systems, Luebeck (Germany)

2008-06-15

Forecasting of respiration motion in image-guided radiotherapy requires algorithms that can accurately and efficiently predict target location. Improved methods for respiratory motion forecasting were developed and tested. MULIN, a new family of prediction algorithms based on linear expansions of the prediction error, was developed and tested. Computer-generated data with a prediction horizon of 150 ms was used for testing in simulation experiments. MULIN was compared to Least Mean Squares-based predictors (LMS; normalized LMS, nLMS; wavelet-based multiscale autoregression, wLMS) and a multi-frequency Extended Kalman Filter (EKF) approach. The in vivo performance of the algorithms was tested on data sets of patients who underwent radiotherapy. The new MULIN methods are highly competitive, outperforming the LMS and the EKF prediction algorithms in real-world settings and performing similarly to optimized nLMS and wLMS prediction algorithms. On simulated, periodic data the MULIN algorithms are outperformed only by the EKF approach due to its inherent advantage in predicting periodic signals. In the presence of noise, the MULIN methods significantly outperform all other algorithms. The MULIN family of algorithms is a feasible tool for the prediction of respiratory motion, performing as well as or better than conventional algorithms while requiring significantly lower computational complexity. The MULIN algorithms are of special importance wherever high-speed prediction is required. (orig.)

Non-linear laws of echoic memory and auditory change detection in humans

Directory of Open Access Journals (Sweden)

Takeshima Yasuyuki

2010-07-01

Full Text Available Abstract Background The detection of any abrupt change in the environment is important to survival. Since memory of preceding sensory conditions is necessary for detecting changes, such a change-detection system relates closely to the memory system. Here we used an auditory change-related N1 subcomponent (change-N1 of event-related brain potentials to investigate cortical mechanisms underlying change detection and echoic memory. Results Change-N1 was elicited by a simple paradigm with two tones, a standard followed by a deviant, while subjects watched a silent movie. The amplitude of change-N1 elicited by a fixed sound pressure deviance (70 dB vs. 75 dB was negatively correlated with the logarithm of the interval between the standard sound and deviant sound (1, 10, 100, or 1000 ms, while positively correlated with the logarithm of the duration of the standard sound (25, 100, 500, or 1000 ms. The amplitude of change-N1 elicited by a deviance in sound pressure, sound frequency, and sound location was correlated with the logarithm of the magnitude of physical differences between the standard and deviant sounds. Conclusions The present findings suggest that temporal representation of echoic memory is non-linear and Weber-Fechner law holds for the automatic cortical response to sound changes within a suprathreshold range. Since the present results show that the behavior of echoic memory can be understood through change-N1, change-N1 would be a useful tool to investigate memory systems.

Nonlinear self-adjointness, conservation laws, and the construction of solutions of partial differential equations using conservation laws

International Nuclear Information System (INIS)

Ibragimov, N Kh; Avdonina, E D

2013-01-01

The method of nonlinear self-adjointness, which was recently developed by the first author, gives a generalization of Noether's theorem. This new method significantly extends approaches to constructing conservation laws associated with symmetries, since it does not require the existence of a Lagrangian. In particular, it can be applied to any linear equations and any nonlinear equations that possess at least one local conservation law. The present paper provides a brief survey of results on conservation laws which have been obtained by this method and published mostly in recent preprints of the authors, along with a method for constructing exact solutions of systems of partial differential equations with the use of conservation laws. In most cases the solutions obtained by the method of conservation laws cannot be found as invariant or partially invariant solutions. Bibliography: 23 titles

Non-linear multivariable predictive control of an alcoholic fermentation process using functional link networks

Directory of Open Access Journals (Sweden)

Luiz Augusto da Cruz Meleiro

2005-06-01

Full Text Available In this work a MIMO non-linear predictive controller was developed for an extractive alcoholic fermentation process. The internal model of the controller was represented by two MISO Functional Link Networks (FLNs, identified using simulated data generated from a deterministic mathematical model whose kinetic parameters were determined experimentally. The FLN structure presents as advantages fast training and guaranteed convergence, since the estimation of the weights is a linear optimization problem. Besides, the elimination of non-significant weights generates parsimonious models, which allows for fast execution in an MPC-based algorithm. The proposed algorithm showed good potential in identification and control of non-linear processes.Neste trabalho um controlador preditivo não linear multivariável foi desenvolvido para um processo de fermentação alcoólica extrativa. O modelo interno do controlador foi representado por duas redes do tipo Functional Link (FLN, identificadas usando dados de simulação gerados a partir de um modelo validado experimentalmente. A estrutura FLN apresenta como vantagem o treinamento rápido e convergência garantida, já que a estimação dos seus pesos é um problema de otimização linear. Além disso, a eliminação de pesos não significativos gera modelos parsimoniosos, o que permite a rápida execução em algoritmos de controle preditivo baseado em modelo. Os resultados mostram que o algoritmo proposto tem grande potencial para identificação e controle de processos não lineares.

Radiation protection and the laws and regulations

International Nuclear Information System (INIS)

Takada, Takuo

1980-01-01

In hospitals and clinics, when cobalt remote irradiation apparatuses, betatrons and linear accelerators are installed, the provisions of medical and radiation injury prevention laws and other related laws and regulations must be observed. The following matters are described: the laws and regulations concerning the prevention of radiation injuries, the definitions of the therapeutical equipments, the radiation protection standards for such facilities, radiation exposure dose and permissible dose, the procedures concerning the application before usage, the responsibilities of hospitals and clinics for radiation measurement and management, and shielding and shield calculations. (J.P.N.)

Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study.

Science.gov (United States)

Li, Hongjian; Leung, Kwong-Sak; Wong, Man-Hon; Ballester, Pedro J

2014-08-27

State-of-the-art protein-ligand docking methods are generally limited by the traditionally low accuracy of their scoring functions, which are used to predict binding affinity and thus vital for discriminating between active and inactive compounds. Despite intensive research over the years, classical scoring functions have reached a plateau in their predictive performance. These assume a predetermined additive functional form for some sophisticated numerical features, and use standard multivariate linear regression (MLR) on experimental data to derive the coefficients. In this study we show that such a simple functional form is detrimental for the prediction performance of a scoring function, and replacing linear regression by machine learning techniques like random forest (RF) can improve prediction performance. We investigate the conditions of applying RF under various contexts and find that given sufficient training samples RF manages to comprehensively capture the non-linearity between structural features and measured binding affinities. Incorporating more structural features and training with more samples can both boost RF performance. In addition, we analyze the importance of structural features to binding affinity prediction using the RF variable importance tool. Lastly, we use Cyscore, a top performing empirical scoring function, as a baseline for comparison study. Machine-learning scoring functions are fundamentally different from classical scoring functions because the former circumvents the fixed functional form relating structural features with binding affinities. RF, but not MLR, can effectively exploit more structural features and more training samples, leading to higher prediction performance. The future availability of more X-ray crystal structures will further widen the performance gap between RF-based and MLR-based scoring functions. This further stresses the importance of substituting RF for MLR in scoring function development.

Robust Model Predictive Control Using Linear Matrix Inequalities for the Treatment of Asymmetric Output Constraints

Directory of Open Access Journals (Sweden)

Mariana Santos Matos Cavalca

2012-01-01

Full Text Available One of the main advantages of predictive control approaches is the capability of dealing explicitly with constraints on the manipulated and output variables. However, if the predictive control formulation does not consider model uncertainties, then the constraint satisfaction may be compromised. A solution for this inconvenience is to use robust model predictive control (RMPC strategies based on linear matrix inequalities (LMIs. However, LMI-based RMPC formulations typically consider only symmetric constraints. This paper proposes a method based on pseudoreferences to treat asymmetric output constraints in integrating SISO systems. Such technique guarantees robust constraint satisfaction and convergence of the state to the desired equilibrium point. A case study using numerical simulation indicates that satisfactory results can be achieved.

Division of the momentum of electromagnetic waves in linear media into electromagnetic and material parts.

Science.gov (United States)

Saldanha, Pablo L

2010-02-01

It is proposed a natural and consistent division of the momentum of electromagnetic waves in linear, non-dispersive and non-absorptive dielectric and magnetic media into material and electromagnetic parts. The material part is calculated using directly the Lorentz force law and the electromagnetic momentum density has the form epsilon(0)E x B, without an explicit dependence on the properties of the media. The consistency of the treatment is verified through the obtention of a correct momentum balance equation in many examples and showing the compatibility of the division with the Einstein's theory of relativity by the use of a gedanken experiment. An experimental prediction for the radiation pressure on mirrors immersed in linear dielectric and magnetic media is also made.

Sparse Power-Law Network Model for Reliable Statistical Predictions Based on Sampled Data

Directory of Open Access Journals (Sweden)

Alexander P. Kartun-Giles

2018-04-01

Full Text Available A projective network model is a model that enables predictions to be made based on a subsample of the network data, with the predictions remaining unchanged if a larger sample is taken into consideration. An exchangeable model is a model that does not depend on the order in which nodes are sampled. Despite a large variety of non-equilibrium (growing and equilibrium (static sparse complex network models that are widely used in network science, how to reconcile sparseness (constant average degree with the desired statistical properties of projectivity and exchangeability is currently an outstanding scientific problem. Here we propose a network process with hidden variables which is projective and can generate sparse power-law networks. Despite the model not being exchangeable, it can be closely related to exchangeable uncorrelated networks as indicated by its information theory characterization and its network entropy. The use of the proposed network process as a null model is here tested on real data, indicating that the model offers a promising avenue for statistical network modelling.

On the viscoelastic characterization of the Jeffreys-Lomnitz law of creep

OpenAIRE

Mainardi, Francesco; Spada, Giorgio

2011-01-01

In 1958 Jeffreys proposed a power law of creep, generalizing the logarithmic law earlier introduced by Lomnitz, to broaden the geophysical applications to fluid-like materials including igneous rocks. This generalized law, however, can be applied also to solid-like viscoelastic materials. We revisit the Jeffreys-Lomnitz law of creep by allowing its power law exponent $\\alpha$, usually limited to the range [0,1] to all negative values. This is consistent with the linear theory of viscoelastici...

An atomistic vision of the Mass Action Law: Prediction of carbon/oxygen defects in silicon

Energy Technology Data Exchange (ETDEWEB)

Brenet, G.; Timerkaeva, D.; Caliste, D.; Pochet, P. [CEA, INAC-SP2M, Atomistic Simulation Laboratory, F-38000 Grenoble (France); Univ. Grenoble Alpes, INAC-SP2M, L-Sim, F-38000 Grenoble (France); Sgourou, E. N.; Londos, C. A. [University of Athens, Solid State Physics Section, Panepistimiopolis Zografos, Athens 157 84 (Greece)

2015-09-28

We introduce an atomistic description of the kinetic Mass Action Law to predict concentrations of defects and complexes. We demonstrate in this paper that this approach accurately predicts carbon/oxygen related defect concentrations in silicon upon annealing. The model requires binding and migration energies of the impurities and complexes, here obtained from density functional theory (DFT) calculations. Vacancy-oxygen complex kinetics are studied as a model system during both isochronal and isothermal annealing. Results are in good agreement with experimental data, confirming the success of the methodology. More importantly, it gives access to the sequence of chain reactions by which oxygen and carbon related complexes are created in silicon. Beside the case of silicon, the understanding of such intricate reactions is a key to develop point defect engineering strategies to control defects and thus semiconductors properties.

TBM performance prediction in Yucca Mountain welded tuff from linear cutter tests

International Nuclear Information System (INIS)

Gertsch, R.; Ozdemir, L.; Gertsch, L.

1992-01-01

This paper discusses performance prediction which were developed for tunnel boring machines operating in welded tuff for the construction of the experimental study facility and the potential nuclear waste repository at Yucca Mountain. The predictions were based on test data obtained from an extensive series of linear cutting tests performed on samples of Topopah String welded tuff from the Yucca Mountain Project site. Using the cutter force, spacing, and penetration data from the experimental program, the thrust, torque, power, and rate of penetration were estimated for a 25 ft diameter tunnel boring machine (TBM) operating in welded tuff. The result show that the Topopah Spring welded tuff (TSw2) can be excavated at relatively high rates of advance with state-of-the-art TBMs. The result also show, however, that the TBM torque and power requirements will be higher than estimated based on rock physical properties and past tunneling experience in rock formations of similar strength

Evaluation of Iowa's anti-bullying law.

Science.gov (United States)

Ramirez, Marizen; Ten Eyck, Patrick; Peek-Asa, Corinne; Onwuachi-Willig, Angela; Cavanaugh, Joseph E

2016-12-01

Bullying is the most common form of youth aggression. Although 49 of all 50 states in the U.S. have an anti-bullying law in place to prevent bullying, little is known about the effectiveness of these laws. Our objective was to measure the effectiveness of Iowa's anti-bullying law in preventing bullying and improving teacher response to bullying. Sixth, 8th, and 11th grade children who completed the 2005, 2008 and 2010 Iowa Youth Survey were included in this study (n = 253,000). Students were coded according to exposure to the law: pre-law for 2005 survey data, one year post-law for 2008 data, and three years post-law for 2010 data. The outcome variables were: 1) being bullied (relational, verbal, physical, and cyber) in the last month and 2) extent to which teachers/adults on campus intervened with bullying. Generalized linear mixed models were constructed with random effects. The odds of being bullied increased from pre-law to one year post-law periods, and then decreased from one year to three years post-law but not below 2005 pre-law levels. This pattern was consistent across all bullying types except cyberbullying. The odds of teacher intervention decreased 11 % (OR = 0.89, 95 % CL = 0.88, 0.90) from 2005 (pre-law) to 2010 (post-law). Bullying increased immediately after Iowa's anti-bullying law was passed, possibly due to improved reporting. Reductions in bullying occurred as the law matured. Teacher response did not improve after the passage of the law.

Complex terrain wind resource estimation with the wind-atlas method: Prediction errors using linearized and nonlinear CFD micro-scale models

DEFF Research Database (Denmark)

Troen, Ib; Bechmann, Andreas; Kelly, Mark C.

2014-01-01

Using the Wind Atlas methodology to predict the average wind speed at one location from measured climatological wind frequency distributions at another nearby location we analyse the relative prediction errors using a linearized flow model (IBZ) and a more physically correct fully non-linear 3D...... flow model (CFD) for a number of sites in very complex terrain (large terrain slopes). We first briefly describe the Wind Atlas methodology as implemented in WAsP and the specifics of the “classical” model setup and the new setup allowing the use of the CFD computation engine. We discuss some known...

Evaluation of Linear Regression Simultaneous Myoelectric Control Using Intramuscular EMG.

Science.gov (United States)

Smith, Lauren H; Kuiken, Todd A; Hargrove, Levi J

2016-04-01

The objective of this study was to evaluate the ability of linear regression models to decode patterns of muscle coactivation from intramuscular electromyogram (EMG) and provide simultaneous myoelectric control of a virtual 3-DOF wrist/hand system. Performance was compared to the simultaneous control of conventional myoelectric prosthesis methods using intramuscular EMG (parallel dual-site control)-an approach that requires users to independently modulate individual muscles in the residual limb, which can be challenging for amputees. Linear regression control was evaluated in eight able-bodied subjects during a virtual Fitts' law task and was compared to performance of eight subjects using parallel dual-site control. An offline analysis also evaluated how different types of training data affected prediction accuracy of linear regression control. The two control systems demonstrated similar overall performance; however, the linear regression method demonstrated improved performance for targets requiring use of all three DOFs, whereas parallel dual-site control demonstrated improved performance for targets that required use of only one DOF. Subjects using linear regression control could more easily activate multiple DOFs simultaneously, but often experienced unintended movements when trying to isolate individual DOFs. Offline analyses also suggested that the method used to train linear regression systems may influence controllability. Linear regression myoelectric control using intramuscular EMG provided an alternative to parallel dual-site control for 3-DOF simultaneous control at the wrist and hand. The two methods demonstrated different strengths in controllability, highlighting the tradeoff between providing simultaneous control and the ability to isolate individual DOFs when desired.

Validation of Lifetime Prediction of IGBT Modules Based on Linear Damage Accumulation by Means of Superimposed Power Cycling Tests

DEFF Research Database (Denmark)

Choi, Ui-Min; Ma, Ke; Blaabjerg, Frede

2018-01-01

In this paper, the lifetime prediction of power device modules based on the linear damage accumulation is studied in conjunction with simple mission profiles of converters. Superimposed power cycling conditions, which are called simple mission profiles in this paper, are made based on a lifetime ...... prediction of IGBT modules under power converter applications.......In this paper, the lifetime prediction of power device modules based on the linear damage accumulation is studied in conjunction with simple mission profiles of converters. Superimposed power cycling conditions, which are called simple mission profiles in this paper, are made based on a lifetime...... model in respect to junction temperature swing duration. This model has been built based on 39 power cycling test results of 600-V 30-A three-phase-molded IGBT modules. Six tests are performed under three superimposed power cycling conditions using an advanced power cycling test setup. The experimental...

A scaling law beyond Zipf's law and its relation to Heaps' law

International Nuclear Information System (INIS)

Font-Clos, Francesc; Corral, Álvaro; Boleda, Gemma

2013-01-01

The dependence on text length of the statistical properties of word occurrences has long been considered a severe limitation on the usefulness of quantitative linguistics. We propose a simple scaling form for the distribution of absolute word frequencies that brings to light the robustness of this distribution as text grows. In this way, the shape of the distribution is always the same, and it is only a scale parameter that increases (linearly) with text length. By analyzing very long novels we show that this behavior holds both for raw, unlemmatized texts and for lemmatized texts. In the latter case, the distribution of frequencies is well approximated by a double power law, maintaining the Zipf's exponent value γ ≃ 2 for large frequencies but yielding a smaller exponent in the low-frequency regime. The growth of the distribution with text length allows us to estimate the size of the vocabulary at each step and to propose a generic alternative to Heaps' law, which turns out to be intimately connected to the distribution of frequencies, thanks to its scaling behavior. (paper)

Explicit/multi-parametric model predictive control (MPC) of linear discrete-time systems by dynamic and multi-parametric programming

KAUST Repository

Kouramas, K.I.; Faí sca, N.P.; Panos, C.; Pistikopoulos, E.N.

2011-01-01

This work presents a new algorithm for solving the explicit/multi- parametric model predictive control (or mp-MPC) problem for linear, time-invariant discrete-time systems, based on dynamic programming and multi-parametric programming techniques

Current error vector based prediction control of the section winding permanent magnet linear synchronous motor

Energy Technology Data Exchange (ETDEWEB)

Hong Junjie, E-mail: hongjjie@mail.sysu.edu.cn [School of Engineering, Sun Yat-Sen University, Guangzhou 510006 (China); Li Liyi, E-mail: liliyi@hit.edu.cn [Dept. Electrical Engineering, Harbin Institute of Technology, Harbin 150000 (China); Zong Zhijian; Liu Zhongtu [School of Engineering, Sun Yat-Sen University, Guangzhou 510006 (China)

2011-10-15

Highlights: {yields} The structure of the permanent magnet linear synchronous motor (SW-PMLSM) is new. {yields} A new current control method CEVPC is employed in this motor. {yields} The sectional power supply method is different to the others and effective. {yields} The performance gets worse with voltage and current limitations. - Abstract: To include features such as greater thrust density, higher efficiency without reducing the thrust stability, this paper proposes a section winding permanent magnet linear synchronous motor (SW-PMLSM), whose iron core is continuous, whereas winding is divided. The discrete system model of the motor is derived. With the definition of the current error vector and selection of the value function, the theory of the current error vector based prediction control (CEVPC) for the motor currents is explained clearly. According to the winding section feature, the motion region of the mover is divided into five zones, in which the implementation of the current predictive control method is proposed. Finally, the experimental platform is constructed and experiments are carried out. The results show: the current control effect has good dynamic response, and the thrust on the mover remains constant basically.

Dynamics of unsymmetric piecewise-linear/non-linear systems using finite elements in time

Science.gov (United States)

Wang, Yu

1995-08-01

The dynamic response and stability of a single-degree-of-freedom system with unsymmetric piecewise-linear/non-linear stiffness are analyzed using the finite element method in the time domain. Based on a Hamilton's weak principle, this method provides a simple and efficient approach for predicting all possible fundamental and sub-periodic responses. The stability of the steady state response is determined by using Floquet's theory without any special effort for calculating transition matrices. This method is applied to a number of examples, demonstrating its effectiveness even for a strongly non-linear problem involving both clearance and continuous stiffness non-linearities. Close agreement is found between available published findings and the predictions of the finite element in time approach, which appears to be an efficient and reliable alternative technique for non-linear dynamic response and stability analysis of periodic systems.

EVALUATING PREDICTIVE ERRORS OF A COMPLEX ENVIRONMENTAL MODEL USING A GENERAL LINEAR MODEL AND LEAST SQUARE MEANS

Science.gov (United States)

A General Linear Model (GLM) was used to evaluate the deviation of predicted values from expected values for a complex environmental model. For this demonstration, we used the default level interface of the Regional Mercury Cycling Model (R-MCM) to simulate epilimnetic total mer...

The separation-combination method of linear structures in remote sensing image interpretation and its application

International Nuclear Information System (INIS)

Liu Linqin

1991-01-01

The separation-combination method a new kind of analysis method of linear structures in remote sensing image interpretation is introduced taking northwestern Fujian as the example, its practical application is examined. The practice shows that application results not only reflect intensities of linear structures in overall directions at different locations, but also contribute to the zonation of linear structures and display their space distribution laws. Based on analyses of linear structures, it can provide more information concerning remote sensing on studies of regional mineralization laws and the guide to ore-finding combining with mineralization

Optimal non-linear health insurance.

Science.gov (United States)

Blomqvist, A

1997-06-01

Most theoretical and empirical work on efficient health insurance has been based on models with linear insurance schedules (a constant co-insurance parameter). In this paper, dynamic optimization techniques are used to analyse the properties of optimal non-linear insurance schedules in a model similar to one originally considered by Spence and Zeckhauser (American Economic Review, 1971, 61, 380-387) and reminiscent of those that have been used in the literature on optimal income taxation. The results of a preliminary numerical example suggest that the welfare losses from the implicit subsidy to employer-financed health insurance under US tax law may be a good deal smaller than previously estimated using linear models.

Applicability of linear and non-linear potential flow models on a Wavestar float

DEFF Research Database (Denmark)

Bozonnet, Pauline; Dupin, Victor; Tona, Paolino

2017-01-01

as a model based on non-linear potential flow theory and weakscatterer hypothesis are successively considered. Simple tests, such as dip tests, decay tests and captive tests enable to highlight the improvements obtained with the introduction of nonlinearities. Float motion under wave actions and without...... control action, limited to small amplitude motion with a single float, is well predicted by the numerical models, including the linear one. Still, float velocity is better predicted by accounting for non-linear hydrostatic and Froude-Krylov forces.......Numerical models based on potential flow theory, including different types of nonlinearities are compared and validated against experimental data for the Wavestar wave energy converter technology. Exact resolution of the rotational motion, non-linear hydrostatic and Froude-Krylov forces as well...

Predicting hyperketonemia by logistic and linear regression using test-day milk and performance variables in early-lactation Holstein and Jersey cows.

Science.gov (United States)

Chandler, T L; Pralle, R S; Dórea, J R R; Poock, S E; Oetzel, G R; Fourdraine, R H; White, H M

2018-03-01

Although cowside testing strategies for diagnosing hyperketonemia (HYK) are available, many are labor intensive and costly, and some lack sufficient accuracy. Predicting milk ketone bodies by Fourier transform infrared spectrometry during routine milk sampling may offer a more practical monitoring strategy. The objectives of this study were to (1) develop linear and logistic regression models using all available test-day milk and performance variables for predicting HYK and (2) compare prediction methods (Fourier transform infrared milk ketone bodies, linear regression models, and logistic regression models) to determine which is the most predictive of HYK. Given the data available, a secondary objective was to evaluate differences in test-day milk and performance variables (continuous measurements) between Holsteins and Jerseys and between cows with or without HYK within breed. Blood samples were collected on the same day as milk sampling from 658 Holstein and 468 Jersey cows between 5 and 20 d in milk (DIM). Diagnosis of HYK was at a serum β-hydroxybutyrate (BHB) concentration ≥1.2 mmol/L. Concentrations of milk BHB and acetone were predicted by Fourier transform infrared spectrometry (Foss Analytical, Hillerød, Denmark). Thresholds of milk BHB and acetone were tested for diagnostic accuracy, and logistic models were built from continuous variables to predict HYK in primiparous and multiparous cows within breed. Linear models were constructed from continuous variables for primiparous and multiparous cows within breed that were 5 to 11 DIM or 12 to 20 DIM. Milk ketone body thresholds diagnosed HYK with 64.0 to 92.9% accuracy in Holsteins and 59.1 to 86.6% accuracy in Jerseys. Logistic models predicted HYK with 82.6 to 97.3% accuracy. Internally cross-validated multiple linear regression models diagnosed HYK of Holstein cows with 97.8% accuracy for primiparous and 83.3% accuracy for multiparous cows. Accuracy of Jersey models was 81.3% in primiparous and 83

Hourly predictive Levenberg-Marquardt ANN and multi linear regression models for predicting of dew point temperature

Science.gov (United States)

Zounemat-Kermani, Mohammad

2012-08-01

In this study, the ability of two models of multi linear regression (MLR) and Levenberg-Marquardt (LM) feed-forward neural network was examined to estimate the hourly dew point temperature. Dew point temperature is the temperature at which water vapor in the air condenses into liquid. This temperature can be useful in estimating meteorological variables such as fog, rain, snow, dew, and evapotranspiration and in investigating agronomical issues as stomatal closure in plants. The availability of hourly records of climatic data (air temperature, relative humidity and pressure) which could be used to predict dew point temperature initiated the practice of modeling. Additionally, the wind vector (wind speed magnitude and direction) and conceptual input of weather condition were employed as other input variables. The three quantitative standard statistical performance evaluation measures, i.e. the root mean squared error, mean absolute error, and absolute logarithmic Nash-Sutcliffe efficiency coefficient ( {| {{{Log}}({{NS}})} |} ) were employed to evaluate the performances of the developed models. The results showed that applying wind vector and weather condition as input vectors along with meteorological variables could slightly increase the ANN and MLR predictive accuracy. The results also revealed that LM-NN was superior to MLR model and the best performance was obtained by considering all potential input variables in terms of different evaluation criteria.

The principles and construction of linear colliders

International Nuclear Information System (INIS)

Rees, J.

1986-09-01

The problems posed to the designers and builders of high-energy linear colliders are discussed. Scaling laws of linear colliders are considered. The problem of attainment of small interaction areas is addressed. The physics of damping rings, which are designed to condense beam bunches in phase space, is discussed. The effect of wake fields on a particle bunch in a linac, particularly the conventional disk-loaded microwave linac structures, are discussed, as well as ways of dealing with those effects. Finally, the SLAC Linear Collider is described. 18 refs., 17 figs

Genomic predictions across Nordic Holstein and Nordic Red using the genomic best linear unbiased prediction model with different genomic relationship matrices.

Science.gov (United States)

Zhou, L; Lund, M S; Wang, Y; Su, G

2014-08-01

This study investigated genomic predictions across Nordic Holstein and Nordic Red using various genomic relationship matrices. Different sources of information, such as consistencies of linkage disequilibrium (LD) phase and marker effects, were used to construct the genomic relationship matrices (G-matrices) across these two breeds. Single-trait genomic best linear unbiased prediction (GBLUP) model and two-trait GBLUP model were used for single-breed and two-breed genomic predictions. The data included 5215 Nordic Holstein bulls and 4361 Nordic Red bulls, which was composed of three populations: Danish Red, Swedish Red and Finnish Ayrshire. The bulls were genotyped with 50 000 SNP chip. Using the two-breed predictions with a joint Nordic Holstein and Nordic Red reference population, accuracies increased slightly for all traits in Nordic Red, but only for some traits in Nordic Holstein. Among the three subpopulations of Nordic Red, accuracies increased more for Danish Red than for Swedish Red and Finnish Ayrshire. This is because closer genetic relationships exist between Danish Red and Nordic Holstein. Among Danish Red, individuals with higher genomic relationship coefficients with Nordic Holstein showed more increased accuracies in the two-breed predictions. Weighting the two-breed G-matrices by LD phase consistencies, marker effects or both did not further improve accuracies of the two-breed predictions. © 2014 Blackwell Verlag GmbH.

A disturbance decoupling nonlinear control law for variable speed wind turbines

DEFF Research Database (Denmark)

Thomsen, Sven Creutz; Poulsen, Niels Kjølstad

2007-01-01

This paper describes a nonlinear control law for controlling variable speed wind turbines using feedback linearization. The novel aspect of the control law is its ability to decouple the effect of wind fluctuations. Furthermore, the transformation to feedback linearizable coordinates is chosen...

Accurate electrostatic and van der Waals pull-in prediction for fully clamped nano/micro-beams using linear universal graphs of pull-in instability

Science.gov (United States)

Tahani, Masoud; Askari, Amir R.

2014-09-01

In spite of the fact that pull-in instability of electrically actuated nano/micro-beams has been investigated by many researchers to date, no explicit formula has been presented yet which can predict pull-in voltage based on a geometrically non-linear and distributed parameter model. The objective of present paper is to introduce a simple and accurate formula to predict this value for a fully clamped electrostatically actuated nano/micro-beam. To this end, a non-linear Euler-Bernoulli beam model is employed, which accounts for the axial residual stress, geometric non-linearity of mid-plane stretching, distributed electrostatic force and the van der Waals (vdW) attraction. The non-linear boundary value governing equation of equilibrium is non-dimensionalized and solved iteratively through single-term Galerkin based reduced order model (ROM). The solutions are validated thorough direct comparison with experimental and other existing results reported in previous studies. Pull-in instability under electrical and vdW loads are also investigated using universal graphs. Based on the results of these graphs, non-dimensional pull-in and vdW parameters, which are defined in the text, vary linearly versus the other dimensionless parameters of the problem. Using this fact, some linear equations are presented to predict pull-in voltage, the maximum allowable length, the so-called detachment length, and the minimum allowable gap for a nano/micro-system. These linear equations are also reduced to a couple of universal pull-in formulas for systems with small initial gap. The accuracy of the universal pull-in formulas are also validated by comparing its results with available experimental and some previous geometric linear and closed-form findings published in the literature.

Predicted and verified deviations from Zipf's law in ecology of competing products.

Science.gov (United States)

Hisano, Ryohei; Sornette, Didier; Mizuno, Takayuki

2011-08-01

Zipf's power-law distribution is a generic empirical statistical regularity found in many complex systems. However, rather than universality with a single power-law exponent (equal to 1 for Zipf's law), there are many reported deviations that remain unexplained. A recently developed theory finds that the interplay between (i) one of the most universal ingredients, namely stochastic proportional growth, and (ii) birth and death processes, leads to a generic power-law distribution with an exponent that depends on the characteristics of each ingredient. Here, we report the first complete empirical test of the theory and its application, based on the empirical analysis of the dynamics of market shares in the product market. We estimate directly the average growth rate of market shares and its standard deviation, the birth rates and the "death" (hazard) rate of products. We find that temporal variations and product differences of the observed power-law exponents can be fully captured by the theory with no adjustable parameters. Our results can be generalized to many systems for which the statistical properties revealed by power-law exponents are directly linked to the underlying generating mechanism.

TRM performance prediction in Yucca Mountain welded tuff from linear cutter tests

International Nuclear Information System (INIS)

Gertsch, R.; Ozdemir, L.; Gertsch, L.

1992-01-01

Performance predictions were developed for tunnel boring machines operating in welded tuff for the construction of the experimental study facility and the potential nuclear waste repository at Yucca Mountain. The predictions were based on test data obtained from an extensive series of linear cutting tests performed on samples of Topopah Spring welded tuff from the Yucca Mountain Project site. Using the cutter force, spacing, and penetration data from the experimental program, the thrust, torque, power, and rate of penetration were estimated for a 25 ft diameter tunnel boring machine (TBM) operating in welded tuff. Guidelines were developed for the optimal design of the TBM cutterhead to achieve high production rates at the lowest possible excavation costs. The results show that the Topopah Spring welded tuff (TSw2) can be excavated at relatively high rates of advance with state-of-the-art TBMs. The results also show, however, that the TBM torque and power requirements will be higher than estimated based on rock physical properties and past tunneling experience in rock formations of similar strength

Threshold law for positron-atom impact ionisation

International Nuclear Information System (INIS)

Temkin, A.

1982-01-01

The threshold law for ionisation of atoms by positron impact is adduced in analogy with the author's approach to the electron-atom ionisation. It is concluded the Coulomb-dipole region of potential gives the essential part of the interaction in both cases and leads to the same kind of result: a modulated linear law. An additional process which enters positron ionisation is positronium formation in the continuum, but that will not dominate the threshold yield. The result is in sharp contrast to the positron threshold law as recently derived by Klar (J. Phys. B.; 14:4165 (1981)) on the basis of a Wannier-type (Phys. Rev.; 90:817 (1953)) analysis. (author)

Use of ILTV Control Laws for LaNCETS Flight Research

Science.gov (United States)

Moua, Cheng

2010-01-01

A report discusses the Lift and Nozzle Change Effects on Tail Shock (LaNCETS) test to investigate the effects of lift distribution and nozzle-area ratio changes on tail shock strength of an F-15 aircraft. Specific research objectives are to obtain inflight shock strength for multiple combinations of nozzle-area ratio and lift distribution; compare results with preflight prediction tools; and update predictive tools with flight results. The objectives from a stability and control perspective are to ensure adequate aircraft stability for the changes in lift distribution and plume shape, and ensure manageable transient from engaging and disengaging the ILTV research control laws. In order to change the lift distribution and plume shape of the F-15 aircraft, a decade-old Inner Loop Thrust Vectoring (ILTV) research control law was used. Flight envelope expansion was performed for the test configuration and flight conditions prior to the probing test points. The approach for achieving the research objectives was to utilize the unique capabilities of NASA's NF-15B-837 aircraft to allow the adjustment of the nozzle-area ratio and/or canard positions by engaging the ILTV research control laws. The ILTV control laws provide the ability to add trim command biases to canard positions, nozzle area ratios, and thrust vectoring through the use of datasets. Datasets consist of programmed test inputs (PTIs) that define trims to change the nozzle-area ratio and/or canard positions. The trims are applied as increments to the normally commanded positions. A LaNCETS non-linear, six-degrees-of-freedom simulation capable of realtime pilot-in-the-loop, hardware-in-the-loop, and non-real-time batch support was developed and validated. Prior to first flight, extensive simulation analyses were performed to show adequate stability margins with the changes in lift distribution and plume shape. Additionally, engagement/disengagement transient analysis was also performed to show manageable

On the predictability of extreme events in records with linear and nonlinear long-range memory: Efficiency and noise robustness

Science.gov (United States)

Bogachev, Mikhail I.; Bunde, Armin

2011-06-01

We study the predictability of extreme events in records with linear and nonlinear long-range memory in the presence of additive white noise using two different approaches: (i) the precursory pattern recognition technique (PRT) that exploits solely the information about short-term precursors, and (ii) the return interval approach (RIA) that exploits long-range memory incorporated in the elapsed time after the last extreme event. We find that the PRT always performs better when only linear memory is present. In the presence of nonlinear memory, both methods demonstrate comparable efficiency in the absence of white noise. When additional white noise is present in the record (which is the case in most observational records), the efficiency of the PRT decreases monotonously with increasing noise level. In contrast, the RIA shows an abrupt transition between a phase of low level noise where the prediction is as good as in the absence of noise, and a phase of high level noise where the prediction becomes poor. In the phase of low and intermediate noise the RIA predicts considerably better than the PRT, which explains our recent findings in physiological and financial records.

Integration of Attributes from Non-Linear Characterization of Cardiovascular Time-Series for Prediction of Defibrillation Outcomes.

Directory of Open Access Journals (Sweden)

Sharad Shandilya

Full Text Available The timing of defibrillation is mostly at arbitrary intervals during cardio-pulmonary resuscitation (CPR, rather than during intervals when the out-of-hospital cardiac arrest (OOH-CA patient is physiologically primed for successful countershock. Interruptions to CPR may negatively impact defibrillation success. Multiple defibrillations can be associated with decreased post-resuscitation myocardial function. We hypothesize that a more complete picture of the cardiovascular system can be gained through non-linear dynamics and integration of multiple physiologic measures from biomedical signals.Retrospective analysis of 153 anonymized OOH-CA patients who received at least one defibrillation for ventricular fibrillation (VF was undertaken. A machine learning model, termed Multiple Domain Integrative (MDI model, was developed to predict defibrillation success. We explore the rationale for non-linear dynamics and statistically validate heuristics involved in feature extraction for model development. Performance of MDI is then compared to the amplitude spectrum area (AMSA technique.358 defibrillations were evaluated (218 unsuccessful and 140 successful. Non-linear properties (Lyapunov exponent > 0 of the ECG signals indicate a chaotic nature and validate the use of novel non-linear dynamic methods for feature extraction. Classification using MDI yielded ROC-AUC of 83.2% and accuracy of 78.8%, for the model built with ECG data only. Utilizing 10-fold cross-validation, at 80% specificity level, MDI (74% sensitivity outperformed AMSA (53.6% sensitivity. At 90% specificity level, MDI had 68.4% sensitivity while AMSA had 43.3% sensitivity. Integrating available end-tidal carbon dioxide features into MDI, for the available 48 defibrillations, boosted ROC-AUC to 93.8% and accuracy to 83.3% at 80% sensitivity.At clinically relevant sensitivity thresholds, the MDI provides improved performance as compared to AMSA, yielding fewer unsuccessful defibrillations

Prediction of spontaneous ureteral stone passage: Automated 3D-measurements perform equal to radiologists, and linear measurements equal to volumetric.

Science.gov (United States)

Jendeberg, Johan; Geijer, Håkan; Alshamari, Muhammed; Lidén, Mats

2018-01-24

To compare the ability of different size estimates to predict spontaneous passage of ureteral stones using a 3D-segmentation and to investigate the impact of manual measurement variability on the prediction of stone passage. We retrospectively included 391 consecutive patients with ureteral stones on non-contrast-enhanced CT (NECT). Three-dimensional segmentation size estimates were compared to the mean of three radiologists' measurements. Receiver-operating characteristic (ROC) analysis was performed for the prediction of spontaneous passage for each estimate. The difference in predicted passage probability between the manual estimates in upper and lower stones was compared. The area under the ROC curve (AUC) for the measurements ranged from 0.88 to 0.90. Between the automated 3D algorithm and the manual measurements the 95% limits of agreement were 0.2 ± 1.4 mm for the width. The manual bone window measurements resulted in a > 20 percentage point (ppt) difference between the readers in the predicted passage probability in 44% of the upper and 6% of the lower ureteral stones. All automated 3D algorithm size estimates independently predicted the spontaneous stone passage with similar high accuracy as the mean of three readers' manual linear measurements. Manual size estimation of upper stones showed large inter-reader variations for spontaneous passage prediction. • An automated 3D technique predicts spontaneous stone passage with high accuracy. • Linear, areal and volumetric measurements performed similarly in predicting stone passage. • Reader variability has a large impact on the predicted prognosis for stone passage.

Quasi-linear score for capturing heterogeneous structure in biomarkers.

Science.gov (United States)

Omae, Katsuhiro; Komori, Osamu; Eguchi, Shinto

2017-06-19

Linear scores are widely used to predict dichotomous outcomes in biomedical studies because of their learnability and understandability. Such approaches, however, cannot be used to elucidate biodiversity when there is heterogeneous structure in target population. Our study was focused on describing intrinsic heterogeneity in predictions. Because heterogeneity can be captured by a clustering method, integrating different information from different clusters should yield better predictions. Accordingly, we developed a quasi-linear score, which effectively combines the linear scores of clustered markers. We extended the linear score to the quasi-linear score by a generalized average form, the Kolmogorov-Nagumo average. We observed that two shrinkage methods worked well: ridge shrinkage for estimating the quasi-linear score, and lasso shrinkage for selecting markers within each cluster. Simulation studies and applications to real data show that the proposed method has good predictive performance compared with existing methods. Heterogeneous structure is captured by a clustering method. Quasi-linear scores combine such heterogeneity and have a better predictive ability compared with linear scores.

Real-time detection of musical onsets with linear prediction and sinusoidal modeling

Science.gov (United States)

Glover, John; Lazzarini, Victor; Timoney, Joseph

2011-12-01

Real-time musical note onset detection plays a vital role in many audio analysis processes, such as score following, beat detection and various sound synthesis by analysis methods. This article provides a review of some of the most commonly used techniques for real-time onset detection. We suggest ways to improve these techniques by incorporating linear prediction as well as presenting a novel algorithm for real-time onset detection using sinusoidal modelling. We provide comprehensive results for both the detection accuracy and the computational performance of all of the described techniques, evaluated using Modal, our new open source library for musical onset detection, which comes with a free database of samples with hand-labelled note onsets.

Control of Non-linear Marine Cooling System

DEFF Research Database (Denmark)

Hansen, Michael; Stoustrup, Jakob; Bendtsen, Jan Dimon

2011-01-01

We consider the problem of designing control laws for a marine cooling system used for cooling the main engine and auxiliary components aboard several classes of container vessels. We focus on achieving simple set point control for the system and do not consider compensation of the non-linearitie......-linearities, closed circuit flow dynamics or transport delays that are present in the system. Control laws are therefore designed using classical control theory and the performance of the design is illustrated through two simulation examples....

ANALYSIS OF MARANGONI CONVECTION OF NON-NEWTONIAN POWER LAW FLUIDS WITH LINEAR TEMPERATURE DISTRIBUTION

Directory of Open Access Journals (Sweden)

Yan Zhang

2011-01-01

Full Text Available The problem of steady, laminar, thermal Marangoni convection flow of non-Newtonian power law fluid along a horizontal surface with variable surface temperature is studied. The partial differential equations are transformed into ordinary differential equations by using a suitable similarity transformation and analytical approximate solutions are obtained by an efficient transformation, asymptotic expansion and Padé approximants technique. The effects of power law index and Marangoni number on velocity and temperature profiles are examined and discussed.

Neural Networks for Non-linear Control

DEFF Research Database (Denmark)

Sørensen, O.

1994-01-01

This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process.......This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process....

Characteristic Model-Based Robust Model Predictive Control for Hypersonic Vehicles with Constraints

Directory of Open Access Journals (Sweden)

Jun Zhang

2017-06-01

Full Text Available Designing robust control for hypersonic vehicles in reentry is difficult, due to the features of the vehicles including strong coupling, non-linearity, and multiple constraints. This paper proposed a characteristic model-based robust model predictive control (MPC for hypersonic vehicles with reentry constraints. First, the hypersonic vehicle is modeled by a characteristic model composed of a linear time-varying system and a lumped disturbance. Then, the identification data are regenerated by the accumulative sum idea in the gray theory, which weakens effects of the random noises and strengthens regularity of the identification data. Based on the regenerated data, the time-varying parameters and the disturbance are online estimated according to the gray identification. At last, the mixed H2/H∞ robust predictive control law is proposed based on linear matrix inequalities (LMIs and receding horizon optimization techniques. Using active tackling system constraints of MPC, the input and state constraints are satisfied in the closed-loop control system. The validity of the proposed control is verified theoretically according to Lyapunov theory and illustrated by simulation results.

Confirmation of linear system theory prediction: Changes in Herrnstein's k as a function of changes in reinforcer magnitude.

Science.gov (United States)

McDowell, J J; Wood, H M

1984-03-01

Eight human subjects pressed a lever on a range of variable-interval schedules for 0.25 cent to 35.0 cent per reinforcement. Herrnstein's hyperbola described seven of the eight subjects' response-rate data well. For all subjects, the y-asymptote of the hyperbola increased with increasing reinforcer magnitude and its reciprocal was a linear function of the reciprocal of reinforcer magnitude. These results confirm predictions made by linear system theory; they contradict formal properties of Herrnstein's account and of six other mathematical accounts of single-alternative responding.

`Un-Darkening' the Cosmos: New laws of physics for an expanding universe

Science.gov (United States)

George, William

2017-11-01

Dark matter is believed to exist because Newton's Laws are inconsistent with the visible matter in galaxies. Dark energy is necessary to explain the universe expansion. (also available from www.turbulence-online.com) suggested that the equations themselves might be in error because they implicitly assume that time is measured in linear increments. This presentation couples the possible non-linearity of time with an expanding universe. Maxwell's equations for an expanding universe with constant speed of light are shown to be invariant only if time itself is non-linear. Both linear and exponential expansion rates are considered. A linearly expanding universe corresponds to logarithmic time, while exponential expansion corresponds to exponentially varying time. Revised Newton's laws using either leads to different definitions of mass and kinetic energy, both of which appear time-dependent if expressed in linear time. And provide the possibility of explaining the astronomical observations without either dark matter or dark energy. We would have never noticed the differences on earth, since the leading term in both expansions is linear in δ /to where to is the current age.

Event-triggered decentralized robust model predictive control for constrained large-scale interconnected systems

Directory of Open Access Journals (Sweden)

Ling Lu

2016-12-01

Full Text Available This paper considers the problem of event-triggered decentralized model predictive control (MPC for constrained large-scale linear systems subject to additive bounded disturbances. The constraint tightening method is utilized to formulate the MPC optimization problem. The local predictive control law for each subsystem is determined aperiodically by relevant triggering rule which allows a considerable reduction of the computational load. And then, the robust feasibility and closed-loop stability are proved and it is shown that every subsystem state will be driven into a robust invariant set. Finally, the effectiveness of the proposed approach is illustrated via numerical simulations.

Extended constitutive laws for lamellar phases

Directory of Open Access Journals (Sweden)

Chi-Deuk Yoo

2013-10-01

Full Text Available Classically, stress and strain rate in linear viscoelastic materials are related by a constitutive relationship involving the viscoelastic modulus G(t. The same constitutive law, within Linear Response Theory, relates currents of conserved quantities and gradients of existing conjugate variables, and it involves the autocorrelation functions of the currents in equilibrium. We explore the consequences of the latter relationship in the case of a mesoscale model of a block copolymer, and derive the resulting relationship between viscous friction and order parameter diffusion that would result in a lamellar phase. We also explicitly consider in our derivation the fact that the dissipative part of the stress tensor must be consistent with the uniaxial symmetry of the phase. We then obtain a relationship between the stress and order parameter autocorrelation functions that can be interpreted as an extended constitutive law, one that offers a way to determine them from microscopic experiment or numerical simulation.

Brownian gas models for extreme-value laws

International Nuclear Information System (INIS)

Eliazar, Iddo

2013-01-01

In this paper we establish one-dimensional Brownian gas models for the extreme-value laws of Gumbel, Weibull, and Fréchet. A gas model is a countable collection of independent particles governed by common diffusion dynamics. The extreme-value laws are the universal probability distributions governing the affine scaling limits of the maxima and minima of ensembles of independent and identically distributed one-dimensional random variables. Using the recently introduced concept of stationary Poissonian intensities, we construct two gas models whose global statistical structures are stationary, and yield the extreme-value laws: a linear Brownian motion gas model for the Gumbel law, and a geometric Brownian motion gas model for the Weibull and Fréchet laws. The stochastic dynamics of these gas models are studied in detail, and closed-form analytical descriptions of their temporal correlation structures, their topological phase transitions, and their intrinsic first-passage-time fluxes are presented. (paper)

The potential in general linear electrodynamics. Causal structure, propagators and quantization

Energy Technology Data Exchange (ETDEWEB)

Siemssen, Daniel [Department of Mathematical Methods in Physics, Faculty of Physics, University of Warsaw (Poland); Pfeifer, Christian [Institute for Theoretical Physics, Leibniz Universitaet Hannover (Germany); Center of Applied Space Technology and Microgravity (ZARM), Universitaet Bremen (Germany)

2016-07-01

From an axiomatic point of view, the fundamental input for a theory of electrodynamics are Maxwell's equations dF=0 (or F=dA) and dH=J, and a constitutive law H=F, which relates the field strength 2-form F and the excitation 2-form H. In this talk we consider general linear electrodynamics, the theory of electrodynamics defined by a linear constitutive law. The best known application of this theory is the effective description of electrodynamics inside (linear) media (e.g. birefringence). We analyze the classical theory of the electromagnetic potential A before we use methods familiar from mathematical quantum field theory in curved spacetimes to quantize it. Our analysis of the classical theory contains the derivation of retarded and advanced propagators, the analysis of the causal structure on the basis of the constitutive law (instead of a metric) and a discussion of the classical phase space. This classical analysis sets the stage for the construction of the quantum field algebra and quantum states, including a (generalized) microlocal spectrum condition.

Contributions to micromechanical model of the non linear behavior of the Callovo-Oxfordian argillite

International Nuclear Information System (INIS)

Abou-Chakra Guery, A.

2007-12-01

This work is performed in the general context of the project of underground disposal of radioactive waste, undertaken by the French National Radioactive Waste Management Agency (ANDRA). Due to its strong density and weak permeability, the formation of Callovo-Oxfordian argillite is chosen as one of possible geological barriers to radionuclides. The objective of the study to develop and validate a non linear homogenization approach of the mechanical behavior of Callovo-Oxfordian argillites. The material is modelled as a composite constituted of an elasto(visco)plastic clay matrix and of linear elastic or elastic damage inclusions. The macroscopic constitutive law is obtained by adapting the incremental method proposed by Hill. The derived model is first compared to Finite Element calculations on unit cell. It is then validated and applied for the prediction of the macroscopic stress-strain responses of the argillite at different geological depths. Finally, the micromechanical model is implemented in a commercial finite element code (Abaqus) for the simulation of a vertical shaft of the underground laboratory. This allows predicting the distribution of damage state and plastic strains and characterizing the excavation damage zone (EDZ). (author)

Bond lengths in Cd1-xZnxTe beyond linear laws revisited

International Nuclear Information System (INIS)

Koteski, V.; Haas, H.; Holub-Krappe, E.; Ivanovic, N.; Mahnke, H.-E.

2004-01-01

We have investigated the development of local bond lengths with composition in the Cd 1-x Zn x Te mixed system by measuring the fine structure in X-ray absorption (EXAFS) at all three constituent atoms. The bond strength is found to dominate over the averaging of the bulk so that the local bond length deviates only slightly from its natural value determined for the pure binary components ZnTe and CdTe, respectively. The deviations are significantly less than predicted by a simple radial force constant model for tetrahedrally co-ordinated binary systems, and the bond-length variation with concentration is significantly non-linear. For the second shell, bimodal anion-anion distances are found while the cation-cation distances can already be described by the virtual crystal approximation. In the diluted regime close to the end-point compounds, we have complemented our experimental work by ab initio calculations based on density functional theory with the WIEN97 program using the linearised augmented plane wave method. Equilibrium atomic lattice positions have been calculated for the substitutional isovalent metal atom in a 32-atom super cell, Zn in the CdTe lattice or Cd in the ZnTe lattice, respectively, yielding good agreement with the atomic distances as determined in our EXAFS experiments

Generating linear regression model to predict motor functions by use of laser range finder during TUG.

Science.gov (United States)

Adachi, Daiki; Nishiguchi, Shu; Fukutani, Naoto; Hotta, Takayuki; Tashiro, Yuto; Morino, Saori; Shirooka, Hidehiko; Nozaki, Yuma; Hirata, Hinako; Yamaguchi, Moe; Yorozu, Ayanori; Takahashi, Masaki; Aoyama, Tomoki

2017-05-01

The purpose of this study was to investigate which spatial and temporal parameters of the Timed Up and Go (TUG) test are associated with motor function in elderly individuals. This study included 99 community-dwelling women aged 72.9 ± 6.3 years. Step length, step width, single support time, variability of the aforementioned parameters, gait velocity, cadence, reaction time from starting signal to first step, and minimum distance between the foot and a marker placed to 3 in front of the chair were measured using our analysis system. The 10-m walk test, five times sit-to-stand (FTSTS) test, and one-leg standing (OLS) test were used to assess motor function. Stepwise multivariate linear regression analysis was used to determine which TUG test parameters were associated with each motor function test. Finally, we calculated a predictive model for each motor function test using each regression coefficient. In stepwise linear regression analysis, step length and cadence were significantly associated with the 10-m walk test, FTSTS and OLS test. Reaction time was associated with the FTSTS test, and step width was associated with the OLS test. Each predictive model showed a strong correlation with the 10-m walk test and OLS test (P motor function test. Moreover, the TUG test time regarded as the lower extremity function and mobility has strong predictive ability in each motor function test. Copyright © 2017 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

Non-linear Model Predictive Control for cooling strings of superconducting magnets using superfluid helium

CERN Document Server

AUTHOR|(SzGeCERN)673023; Blanco Viñuela, Enrique

In each of eight arcs of the 27 km circumference Large Hadron Collider (LHC), 2.5 km long strings of super-conducting magnets are cooled with superfluid Helium II at 1.9 K. The temperature stabilisation is a challenging control problem due to complex non-linear dynamics of the magnets temperature and presence of multiple operational constraints. Strong nonlinearities and variable dead-times of the dynamics originate at strongly heat-flux dependent effective heat conductivity of superfluid that varies three orders of magnitude over the range of possible operational conditions. In order to improve the temperature stabilisation, a proof of concept on-line economic output-feedback Non-linear Model Predictive Controller (NMPC) is presented in this thesis. The controller is based on a novel complex first-principles distributed parameters numerical model of the temperature dynamics over a 214 m long sub-sector of the LHC that is characterized by very low computational cost of simulation needed in real-time optimizat...

Construction of local and non-local conservation laws for non-linear field equations

International Nuclear Information System (INIS)

Vladimirov, V.S.; Volovich, I.V.

1984-08-01

A method of constructing conserved currents for non-linear field equations is presented. More explicitly for non-linear equations, which can be derived from compatibility conditions of some linear system with a parameter, a procedure of obtaining explicit expressions for local and non-local currents is developed. Some examples such as the classical Heisenberg spin chain and supersymmetric Yang-Mills theory are considered. (author)

Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

Science.gov (United States)

Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

1997-01-01

One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

Automatic Offline Formulation of Robust Model Predictive Control Based on Linear Matrix Inequalities Method

Directory of Open Access Journals (Sweden)

Longge Zhang

2013-01-01

Full Text Available Two automatic robust model predictive control strategies are presented for uncertain polytopic linear plants with input and output constraints. A sequence of nested geometric proportion asymptotically stable ellipsoids and controllers is constructed offline first. Then the feedback controllers are automatically selected with the receding horizon online in the first strategy. Finally, a modified automatic offline robust MPC approach is constructed to improve the closed system's performance. The new proposed strategies not only reduce the conservatism but also decrease the online computation. Numerical examples are given to illustrate their effectiveness.

Study on linear canonical transformation in a framework of a phase space representation of quantum mechanics

International Nuclear Information System (INIS)

Raoelina Andriambololona; Ranaivoson, R.T.R.; Rakotoson, H.; Solofoarisina, W.C.

2015-04-01

We present a study on linear canonical transformation in the framework of a phase space representation of quantum mechanics that we have introduced in our previous work. We begin with a brief recall about the so called phase space representation. We give the definition of linear canonical transformation with the transformation law of coordinate and momentum operators. We establish successively the transformation laws of mean values, dispersions, basis state and wave functions.Then we introduce the concept of isodispersion linear canonical transformation.

One Layer Nonlinear Economic Closed-Loop Generalized Predictive Control for a Wastewater Treatment Plant

Directory of Open Access Journals (Sweden)

Hicham El bahja

2018-04-01

Full Text Available The main scope of this paper is the proposal of a new single layer Nonlinear Economic Closed-Loop Generalized Predictive Control (NECLGPC as an efficient advanced control technique for improving economics in the operation of nonlinear plants. Instead of the classic dual-mode MPC (model predictive controller schemes, where the terminal control law defined in the terminal region is obtained offline solving a linear quadratic regulator problem, here the terminal control law in the NECLGPC is determined online by an unconstrained Nonlinear Generalized Predictive Control (NGPC. In order to make the optimization problem more tractable two considerations have been made in the present work. Firstly, the prediction model consisting of a nonlinear phenomenological model of the plant is expressed with linear structure and state dependent matrices. Secondly, instead of including the nonlinear economic cost in the objective function, an approximation of the reduced gradient of the economic function is used. These assumptions allow us to design an economic unconstrained nonlinear GPC analytically and to state the NECLGPC allow for the design of an economic problem as a QP (Quadratic Programing problem each sampling time. Four controllers based on GPC that differ in designs and structures are compared with the proposed control technique in terms of process performance and energy costs. Particularly, the methodology is implemented in the N-Removal process of a Wastewater Treatment Plant (WWTP and the results prove the efficiency of the method and that it can be used profitably in practical cases.

Growth laws for sub-delta crevasses in the Mississippi River Delta

Science.gov (United States)

Yocum, T. A.; Georgiou, I. Y.; Straub, K. M.

2017-12-01

River deltas are threatened by environmental change, including subsidence, global sea level rise, reduced sediment inputs and other local factors. In the Mississippi River Delta (MRD) these impacts are exemplified, and have led to proposed solutions to build land that include sediment diversions to reinitiate the delta cycle. Deltas were studied extensively using numerical models, theoretical and conceptual frameworks, empirical scaling relationships, laboratory models and field observations. But predicting the future of deltas relies on field observations where for most deltas data are still lacking. Moreover, empirical and theoretical scaling laws may be influenced by the data used to develop them, while laboratory deltas may be influenced by scaling issues. Anthropogenic crevasses in the MRD are large enough to overcome limitations of laboratory deltas, and small enough to allow for rapid channel and wetland development, providing an ideal setting to investigate delta development mechanics. Here we assessed growth laws of sub-delta crevasses (SDC) in the MRD, in two experimental laboratory deltas (LD - weakly and strongly cohesive) and compared them to river dominated deltas worldwide. Channel and delta geometry metrics for each system were obtained using geospatial tools, bathymetric datasets, sediment size, and hydrodynamic observations. Results show that SDC follow growth laws similar to large river dominated deltas, with the exception of some that exhibit anomalous behavior with respect to the frequency and distance to a bifurcation and the fraction of wetted delta shoreline (allometry metrics). Most SDC exhibit a systematic decrease of non-dimensional channel geometries with increased bifurcation order, indicating that channels are adjusting to decreased flow after bifurcations occur, and exhibit linear trends for land allometry and width-depth ratio, although geometries decrease more rapidly per bifurcation order. Measured distance to bifurcations in SDC

Non linear dynamics of magnetic islands in fusion plasmas

International Nuclear Information System (INIS)

Meshcheriakov, D.

2012-10-01

In this thesis we investigate the issues of linear stability of the tearing modes in a presence of both curvature and diamagnetic rotation using the non linear full-MHD toroidal code XTOR-2F, which includes anisotropic heat transport, diamagnetic and geometrical effects. This analysis is applied to one of the fully non-inductive discharges on Tore-Supra. Such experiments are crucially important to demonstrate reactor scale steady state operation for the tokamak. The possibility of a full linear stabilization of the tearing modes by diamagnetic rotation in the presence of toroidal curvature is shown. The stabilization threshold does not follow the classical scaling law connecting the growth rate of islands to plasma conductivity, measured here by the Lundquist number (S). However, for numerical reasons, the conductivity used in the simulations is lower than that of the experiment, which raises the question of extrapolation of the obtained results to the experimental situation. The extrapolation of the obtained results requires simulations with several different conductivities. It predicts that the mode at q = 2 surface to be stable at value of diamagnetic frequency consistent with the experimental one at S = S(exp). In the linearly stable domain, the mode is metastable: saturation level depends on the seed island size. In the non linear regime, the saturation of n=1, m=2 mode is found to be strongly reduced by diamagnetic rotation and by Lundquist number. However, the extrapolation to the experimental situation shows that if the island is destabilized, it will saturate at a detectable level for the Tore Supra diagnostic. For a large plasma aspect ratio (i.e. weak curvature effects), the reduction of the saturated width by diamagnetic frequency takes the form of a jump reminiscent of multiple states evidenced in slab geometry case. The question of extrapolation of the obtained results towards future generation of fusion devices is also addressed. In particular, for

Data adaptive control parameter estimation for scaling laws

Energy Technology Data Exchange (ETDEWEB)

Dinklage, Andreas [Max-Planck-Institut fuer Plasmaphysik, Teilinstitut Greifswald, Wendelsteinstrasse 1, D-17491 Greifswald (Germany); Dose, Volker [Max-Planck- Institut fuer Plasmaphysik, Boltzmannstrasse 2, D-85748 Garching (Germany)

2007-07-01

Bayesian experimental design quantifies the utility of data expressed by the information gain. Data adaptive exploration determines the expected utility of a single new measurement using existing data and a data descriptive model. In other words, the method can be used for experimental planning. As an example for a multivariate linear case, we apply this method for constituting scaling laws of fusion devices. In detail, the scaling of the stellarator W7-AS is examined for a subset of {iota}=1/3 data. The impact of the existing data on the scaling exponents is presented. Furthermore, in control parameter space regions of high utility are identified which improve the accuracy of the scaling law. This approach is not restricted to the presented example only, but can also be extended to non-linear models.

On Faraday's law in the presence of extended conductors

Science.gov (United States)

Bilbao, Luis

2018-06-01

The use of Faraday's Law of induction for calculating the induced currents in an extended conducting body is discussed. In a general case with arbitrary geometry, the solution to the problem of a moving metal object in the presence of a magnetic field is difficult and implies solving Maxwell's equations in a time-dependent situation. In many cases, including cases with good conductors (but not superconductors) Ampère's Law can be neglected and a simpler solution based solely in Faraday's law can be obtained. The integral form of Faraday's Law along any loop in the conducting body is equivalent to a Kirkhhoff's voltage law of a circuit. Therefore, a numerical solution can be obtained by solving a linear system of equations corresponding to a discrete number of loops in the body.

Law enforcement officer versus non-law enforcement officer status as a longitudinal predictor of traditional and emerging cardiovascular risk factors

NARCIS (Netherlands)

Wright, Bruce R; Barbosa-Leiker, Celestina; Hoekstra, T.

Objective: To determine whether law enforcement officer (LEO) status and perceived stress are longitudinal predictors of traditional and inflammatory cardiovascular (CV) risk factors. Method: Linear hierarchical regression was employed to investigate the longitudinal (more than 7 years) relationship

Baeklund transformations, conservation laws and linearization of the self-dual Yang-Mills and chiral fields

International Nuclear Information System (INIS)

Wang, L.C.

1980-01-01

Baecklund Transformations (BT) and the derivation of local conservation laws are first reviewed in the classic case of the Sine-Gordon equation. The BT, conservation laws (local and nonlocal), and the inverse-scattering formulation are discussed for the chiral and the self-dual Yang-Mills fields. Their possible applications to the loop formulation for the Yang-Mills fields are mentioned. 55 references, 1 figure

Linear Multivariable Regression Models for Prediction of Eddy Dissipation Rate from Available Meteorological Data

Science.gov (United States)

MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.

2005-01-01

Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.

Chaos and loss of predictability in the periodically kicked linear oscillator

International Nuclear Information System (INIS)

Luna-Acosta, G.A.; Cantoral, E.

1989-01-01

Chernikov et.al. [2] have discovered new features in the dynamics of a periodically kicked LHO x'' + ω 0 2 x = ( K/ k 0 T 2 ) sin (k 0 x) x Σ n δ (t / T - n). They report that its phase space motion under exact resonance (p ω 0 = (2 π / T) q; p, q integers), and with initial conditions on the separatrix of the average Hamiltonian , accelerates unboundedly along a fractal stochastic web with q-fold symmetry. Here we investigate with numerical experiments the effects of small deviations from exact resonance on the diffusion and symmetry patterns. We show graphically that the stochastic webs are (topologically) unstable and thus the unbounded motion becomes considerably truncated. Moreover, we analyze numerically and analytically a simpler (integrable) version. We give its exact closed-form solution in complex numbers, realize that it accelerates unboundedly only when ω 0 = (2 π/T) q (q = ± 1,2,...), and show that for small uncertainties in these frequencies, total predictability is lost as time evolves. That is, trajectories of a set of systems, initially described by close neighboring points in phase space strongly diverge in a non-linear way. The great loss of predictability in the integrable model is due to the combination of translational and rotational symmetries, inherent in these systems. (Author)

Chaos and loss of predictability in the periodically kicked linear oscillator

Energy Technology Data Exchange (ETDEWEB)

Luna-Acosta, G A [Universidad Autonoma de Puebla (Mexico). Inst. de Ciencias; Cantoral, E [Universidad Autonoma de Puebla (Mexico). Escuela de Fisica

1989-01-01

Chernikov et.al. [2] have discovered new features in the dynamics of a periodically kicked LHO x'' + [omega] [sub 0] [sup 2] x = ( K/ k[sub 0] T [sup 2]) sin (k[sub 0] x) x [Sigma][sub n] [delta] (t / T - n). They report that its phase space motion under exact resonance (p [omega] [sub 0] = (2 [pi] / T) q; p, q integers), and with initial conditions on the separatrix of the average Hamiltonian , accelerates unboundedly along a fractal stochastic web with q-fold symmetry. Here we investigate with numerical experiments the effects of small deviations from exact resonance on the diffusion and symmetry patterns. We show graphically that the stochastic webs are (topologically) unstable and thus the unbounded motion becomes considerably truncated. Moreover, we analyze numerically and analytically a simpler (integrable) version. We give its exact closed-form solution in complex numbers, realize that it accelerates unboundedly only when [omega][sub 0] = (2 [pi]/T) q (q = [+-] 1,2,...), and show that for small uncertainties in these frequencies, total predictability is lost as time evolves. That is, trajectories of a set of systems, initially described by close neighboring points in phase space strongly diverge in a non-linear way. The great loss of predictability in the integrable model is due to the combination of translational and rotational symmetries, inherent in these systems. (Author).

Consequences of nonlinear heat transport laws on expected plasma profiles

International Nuclear Information System (INIS)

Lackner, K.

1987-03-01

The expected variation of plasma pressure profiles against changes in power deposition is investigated by using a simple linear heat transport law as well as a quadratic one. Applying the quadratic transport law it can be shown that the stiffening of the resulting profiles is sufficient to understand the experimentally measured phenomenon of 'profile consistence' without further assumptions of nonlocal effects. (orig.) [de

Flow discharge prediction in compound channels using linear genetic programming

Science.gov (United States)

Azamathulla, H. Md.; Zahiri, A.

2012-08-01

SummaryFlow discharge determination in rivers is one of the key elements in mathematical modelling in the design of river engineering projects. Because of the inundation of floodplains and sudden changes in river geometry, flow resistance equations are not applicable for compound channels. Therefore, many approaches have been developed for modification of flow discharge computations. Most of these methods have satisfactory results only in laboratory flumes. Due to the ability to model complex phenomena, the artificial intelligence methods have recently been employed for wide applications in various fields of water engineering. Linear genetic programming (LGP), a branch of artificial intelligence methods, is able to optimise the model structure and its components and to derive an explicit equation based on the variables of the phenomena. In this paper, a precise dimensionless equation has been derived for prediction of flood discharge using LGP. The proposed model was developed using published data compiled for stage-discharge data sets for 394 laboratories, and field of 30 compound channels. The results indicate that the LGP model has a better performance than the existing models.

Predictive inference for best linear combination of biomarkers subject to limits of detection.

Science.gov (United States)

Coolen-Maturi, Tahani

2017-08-15

Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine, machine learning and credit scoring. The receiver operating characteristic (ROC) curve is a useful tool to assess the ability of a diagnostic test to discriminate between two classes or groups. In practice, multiple diagnostic tests or biomarkers are combined to improve diagnostic accuracy. Often, biomarker measurements are undetectable either below or above the so-called limits of detection (LoD). In this paper, nonparametric predictive inference (NPI) for best linear combination of two or more biomarkers subject to limits of detection is presented. NPI is a frequentist statistical method that is explicitly aimed at using few modelling assumptions, enabled through the use of lower and upper probabilities to quantify uncertainty. The NPI lower and upper bounds for the ROC curve subject to limits of detection are derived, where the objective function to maximize is the area under the ROC curve. In addition, the paper discusses the effect of restriction on the linear combination's coefficients on the analysis. Examples are provided to illustrate the proposed method. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Non-linear friction in reciprocating hydraulic rod seals: Simulation and measurement

International Nuclear Information System (INIS)

Bullock, A K; Tilley, D G; Johnston, D N; Bowen, C R; Keogh, P S

2009-01-01

Non-linear seal friction can impede the performance of hydraulic actuation systems designed for high precision positioning with favourable dynamic response. Methods for predicting seal friction are required to help develop sealing systems for this type of application. Recent simulation techniques have claimed progress, although have yet to be validated experimentally. A conventional reciprocating rod seal is analysed using established elastohydrodynamic theory and the mixed lubrication Greenwood-Williamson-average Reynolds model. A test rig was used to assess the accuracy of the simulation results for both instroke and outstroke. Inverse hydrodynamic theory is shown to predict a U 0.5 power law between rod speed and friction. Comparison with experimental data shows the theory to be qualitatively inaccurate and to predict friction levels an order of magnitude lower than those measured. It was not possible to model the regions very close to the inlet and outlet due to the high pressure gradients at the edges of the contact. The mixed lubrication model produces friction levels within the correct order of magnitude, although incorrectly predicts higher friction during instroke than outstroke. Previous experiments have reported higher friction during instroke than outstroke for rectangular seals, suggesting that the mixed lubrication model used could possibly be suitable for symmetric seals, although not for seal tribology in general.

Strong laws for L- and U-statistics

NARCIS (Netherlands)

Aaronson, J; Burton, R; Dehling, H; Gilat, D; Hill, T; Weiss, B

Strong laws of large numbers are given for L-statistics (linear combinations of order statistics) and for U-statistics (averages of kernels of random samples) for ergodic stationary processes, extending classical theorems; of Hoeffding and of Helmers for lid sequences. Examples are given to show

Physics overview: Introduction to international linear collider physics

Indian Academy of Sciences (India)

Linear collider; Higgs boson; unified theory; dark matter. PACS Nos 29.17. ... to confidence that gauge symmetry is a guiding principle of the law of elementary ... physics beyond the standard model, and each model offers different scenario for.

Predicting microRNA-disease associations using label propagation based on linear neighborhood similarity.

Science.gov (United States)

Li, Guanghui; Luo, Jiawei; Xiao, Qiu; Liang, Cheng; Ding, Pingjian

2018-05-12

Interactions between microRNAs (miRNAs) and diseases can yield important information for uncovering novel prognostic markers. Since experimental determination of disease-miRNA associations is time-consuming and costly, attention has been given to designing efficient and robust computational techniques for identifying undiscovered interactions. In this study, we present a label propagation model with linear neighborhood similarity, called LPLNS, to predict unobserved miRNA-disease associations. Additionally, a preprocessing step is performed to derive new interaction likelihood profiles that will contribute to the prediction since new miRNAs and diseases lack known associations. Our results demonstrate that the LPLNS model based on the known disease-miRNA associations could achieve impressive performance with an AUC of 0.9034. Furthermore, we observed that the LPLNS model based on new interaction likelihood profiles could improve the performance to an AUC of 0.9127. This was better than other comparable methods. In addition, case studies also demonstrated our method's outstanding performance for inferring undiscovered interactions between miRNAs and diseases, especially for novel diseases. Copyright © 2018. Published by Elsevier Inc.

Post-Newtonian conservation laws in rigid quasilocal frames

International Nuclear Information System (INIS)

McGrath, Paul L; Chanona, Melanie; Epp, Richard J; Mann, Robert B; Koop, Michael J

2014-01-01

In recent work we constructed completely general conservation laws for energy (McGrath et al 2012 Class. Quantum Grav. 29 215012) and linear and angular momentum (Epp et al 2013 Class. Quantum Grav. 30 195019) of extended systems in general relativity based on the notion of a rigid quasilocal frame (RQF). We argued at a fundamental level that these RQF conservation laws are superior to conservation laws based on the local stress–energy–momentum tensor of matter because (1) they do not rely on spacetime symmetries and (2) they properly account for both matter and gravitational effects. Moreover, they provide simple, exact, operational expressions for fluxes of gravitational energy and linear and angular momentum. In this paper we derive the form of these laws in a general first post-Newtonian (1PN) approximation, and then apply these approximate laws to the problem of gravitational tidal interactions. We obtain formulas for tidal heating and tidal torque that agree with the literature, but without resorting to the use of pseudotensors. We describe the physical mechanism of these tidal interactions not in the traditional terms of a Newtonian gravitational force, but in terms of a much simpler and universal mechanism that is an exact, quasilocal manifestation of the equivalence principle in general relativity. As concrete examples, we look at the tidal heating of Jupiter’s moon Io and angular momentum transfer in the Earth–Moon system that causes a gradual spin-down of the Earth and recession of the Moon. In both examples we find agreement with observation. (paper)

Modeling and Model Predictive Power and Rate Control of Wireless Communication Networks

Directory of Open Access Journals (Sweden)

Cunwu Han

2014-01-01

Full Text Available A novel power and rate control system model for wireless communication networks is presented, which includes uncertainties, input constraints, and time-varying delays in both state and control input. A robust delay-dependent model predictive power and rate control method is proposed, and the state feedback control law is obtained by solving an optimization problem that is derived by using linear matrix inequality (LMI techniques. Simulation results are given to illustrate the effectiveness of the proposed method.

First law of entanglement rates from holography

Science.gov (United States)

O'Bannon, Andy; Probst, Jonas; Rodgers, Ronnie; Uhlemann, Christoph F.

2017-09-01

For a perturbation of the state of a conformal field theory (CFT), the response of the entanglement entropy is governed by the so-called "first law" of entanglement entropy, in which the change in entanglement entropy is proportional to the change in energy. Whether such a first law holds for other types of perturbations, such as a change to the CFT Lagrangian, remains an open question. We use holography to study the evolution in time t of entanglement entropy for a CFT driven by a t -linear source for a conserved U (1 ) current or marginal scalar operator. We find that although the usual first law of entanglement entropy may be violated, a first law for the rates of change of entanglement entropy and energy still holds. More generally, we prove that this first law for rates holds in holography for any asymptotically (d +1 )-dimensional anti-de Sitter metric perturbation whose t dependence first appears at order zd in the Fefferman-Graham expansion about the boundary at z =0 .

Linear models with R

CERN Document Server

Faraway, Julian J

2014-01-01

A Hands-On Way to Learning Data AnalysisPart of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the first edition.New to the Second EditionReorganiz

Enhancement of Visual Field Predictions with Pointwise Exponential Regression (PER) and Pointwise Linear Regression (PLR).

Science.gov (United States)

Morales, Esteban; de Leon, John Mark S; Abdollahi, Niloufar; Yu, Fei; Nouri-Mahdavi, Kouros; Caprioli, Joseph

2016-03-01

The study was conducted to evaluate threshold smoothing algorithms to enhance prediction of the rates of visual field (VF) worsening in glaucoma. We studied 798 patients with primary open-angle glaucoma and 6 or more years of follow-up who underwent 8 or more VF examinations. Thresholds at each VF location for the first 4 years or first half of the follow-up time (whichever was greater) were smoothed with clusters defined by the nearest neighbor (NN), Garway-Heath, Glaucoma Hemifield Test (GHT), and weighting by the correlation of rates at all other VF locations. Thresholds were regressed with a pointwise exponential regression (PER) model and a pointwise linear regression (PLR) model. Smaller root mean square error (RMSE) values of the differences between the observed and the predicted thresholds at last two follow-ups indicated better model predictions. The mean (SD) follow-up times for the smoothing and prediction phase were 5.3 (1.5) and 10.5 (3.9) years. The mean RMSE values for the PER and PLR models were unsmoothed data, 6.09 and 6.55; NN, 3.40 and 3.42; Garway-Heath, 3.47 and 3.48; GHT, 3.57 and 3.74; and correlation of rates, 3.59 and 3.64. Smoothed VF data predicted better than unsmoothed data. Nearest neighbor provided the best predictions; PER also predicted consistently more accurately than PLR. Smoothing algorithms should be used when forecasting VF results with PER or PLR. The application of smoothing algorithms on VF data can improve forecasting in VF points to assist in treatment decisions.

Multifractal Omori law for earthquake triggering: new tests on the California, Japan and worldwide catalogues

Science.gov (United States)

Ouillon, G.; Sornette, D.; Ribeiro, E.

2009-07-01

The Multifractal Stress-Activated model is a statistical model of triggered seismicity based on mechanical and thermodynamic principles. It predicts that, above a triggering magnitude cut-off M0, the exponent p of the Omori law for the time decay of the rate of aftershocks is a linear increasing function p(M) = a0M + b0 of the main shock magnitude M. We previously reported empirical support for this prediction, using the Southern California Earthquake Center (SCEC) catalogue. Here, we confirm this observation using an updated, longer version of the same catalogue, as well as new methods to estimate p. One of this methods is the newly defined Scaling Function Analysis (SFA), adapted from the wavelet transform. This method is able to measure a mathematical singularity (hence a p-value), erasing the possible regular part of a time-series. The SFA also proves particularly efficient to reveal the coexistence and superposition of several types of relaxation laws (typical Omori sequences and short-lived swarms sequences) which can be mixed within the same catalogue. Another new method consists in monitoring the largest aftershock magnitude observed in successive time intervals, and thus shortcuts the problem of missing events with small magnitudes in aftershock catalogues. The same methods are used on data from the worldwide Harvard Centroid Moment Tensor (CMT) catalogue and show results compatible with those of Southern California. For the Japan Meteorological Agency (JMA) catalogue, we still observe a linear dependence of p on M, but with a smaller slope. The SFA shows however that results for this catalogue may be biased by numerous swarm sequences, despite our efforts to remove them before the analysis.

Explorative methods in linear models

DEFF Research Database (Denmark)

Høskuldsson, Agnar

2004-01-01

The author has developed the H-method of mathematical modeling that builds up the model by parts, where each part is optimized with respect to prediction. Besides providing with better predictions than traditional methods, these methods provide with graphic procedures for analyzing different feat...... features in data. These graphic methods extend the well-known methods and results of Principal Component Analysis to any linear model. Here the graphic procedures are applied to linear regression and Ridge Regression....

A comparison between linear and non-linear analysis of flexible pavements

Energy Technology Data Exchange (ETDEWEB)

Soleymani, H.R.; Berthelot, C.F.; Bergan, A.T. [Saskatchewan Univ., Saskatoon, SK (Canada). Dept. of Mechanical Engineering

1995-12-31

Computer pavement analysis programs, which are based on mathematical simulation models, were compared. The programs included in the study were: ELSYM5, an Elastic Linear (EL) pavement analysis program, MICH-PAVE, a Finite Element Non-Linear (FENL) and Finite Element Linear (FEL) pavement analysis program. To perform the analysis different tire pressures, pavement material properties and asphalt layer thicknesses were selected. Evaluation criteria used in the analysis were tensile strain in bottom of the asphalt layer, vertical compressive strain at the top of the subgrade and surface displacement. Results showed that FENL methods predicted more strain and surface deflection than the FEL and EL analysis methods. Analyzing pavements with FEL does not offer many advantages over the EL method. Differences in predicted strains between the three methods of analysis in some cases was found to be close to 100% It was suggested that these programs require more calibration and validation both theoretically and empirically to accurately correlate with field observations. 19 refs., 4 tabs., 9 figs.

Mathematical problems in non-linear Physics: some results

International Nuclear Information System (INIS)

1979-01-01

The basic results presented in this report are the following: 1) Characterization of the range and Kernel of the variational derivative. 2) Determination of general conservation laws in linear evolution equations, as well as bounds for the number of polynomial conserved densities in non-linear evolution equations in two independent variables of even order. 3) Construction of the most general evolution equation which has a given family of conserved densities. 4) Regularity conditions for the validity of the Lie invariance method. 5) A simple class of perturbations in non-linear wave equations. 6) Soliton solutions in generalized KdV equations. (author)

COSMOLOGY OF CHAMELEONS WITH POWER-LAW COUPLINGS

International Nuclear Information System (INIS)

Mota, David F.; Winther, Hans A.

2011-01-01

In chameleon field theories, a scalar field can couple to matter with gravitational strength and still evade local gravity constraints due to a combination of self-interactions and the couplings to matter. Originally, these theories were proposed with a constant coupling to matter; however, the chameleon mechanism also extends to the case where the coupling becomes field dependent. We study the cosmology of chameleon models with power-law couplings and power-law potentials. It is found that these generalized chameleons, when viable, have a background expansion very close to ΛCDM, but can in some special cases enhance the growth of the linear perturbations at low redshifts. For the models we consider, it is found that this region of the parameter space is ruled out by local gravity constraints. Imposing a coupling to dark matter only, the local constraints are avoided, and it is possible to have observable signatures on the linear matter perturbations.

Area under the curve predictions of dalbavancin, a new lipoglycopeptide agent, using the end of intravenous infusion concentration data point by regression analyses such as linear, log-linear and power models.

Science.gov (United States)

Bhamidipati, Ravi Kanth; Syed, Muzeeb; Mullangi, Ramesh; Srinivas, Nuggehally

2018-02-01

1. Dalbavancin, a lipoglycopeptide, is approved for treating gram-positive bacterial infections. Area under plasma concentration versus time curve (AUC inf ) of dalbavancin is a key parameter and AUC inf /MIC ratio is a critical pharmacodynamic marker. 2. Using end of intravenous infusion concentration (i.e. C max ) C max versus AUC inf relationship for dalbavancin was established by regression analyses (i.e. linear, log-log, log-linear and power models) using 21 pairs of subject data. 3. The predictions of the AUC inf were performed using published C max data by application of regression equations. The quotient of observed/predicted values rendered fold difference. The mean absolute error (MAE)/root mean square error (RMSE) and correlation coefficient (r) were used in the assessment. 4. MAE and RMSE values for the various models were comparable. The C max versus AUC inf exhibited excellent correlation (r > 0.9488). The internal data evaluation showed narrow confinement (0.84-1.14-fold difference) with a RMSE models predicted AUC inf with a RMSE of 3.02-27.46% with fold difference largely contained within 0.64-1.48. 5. Regardless of the regression models, a single time point strategy of using C max (i.e. end of 30-min infusion) is amenable as a prospective tool for predicting AUC inf of dalbavancin in patients.

Effective stress law for anisotropic elastic deformation

International Nuclear Information System (INIS)

Carroll, M.M.

1979-01-01

An effective stress law is derived analytically to describe the effect of pore fluid pressure on the linearly elastic response of saturated porous rocks which exhibit anisotropy. For general anisotropy the difference between the effective stress and the applied stress is not hydrostatic. The effective stress law involves two constants for transversely isotropic response and three constants for orthotropic response; these constants can be expressed in terms of the moduli of the porous material and of the solid material. These expressions simplify considerably when the anisotropy is structural rather than intrinsic, i.e., in the case of an isotropic solid material with an anisotropic pore structure. In this case the effective stress law involves the solid or grain bulk modulus and two or three moduli of the porous material, for transverse isotropy and orthotropy, respectively. The law reduces, in the case of isotropic response, to that suggested by Geertsma (1957) and by Skempton (1961) and derived analytically by Nur and Byerlee

Comparison of Damage Models for Predicting the Non-Linear Response of Laminates Under Matrix Dominated Loading Conditions

Science.gov (United States)

Schuecker, Clara; Davila, Carlos G.; Rose, Cheryl A.

2010-01-01

Five models for matrix damage in fiber reinforced laminates are evaluated for matrix-dominated loading conditions under plane stress and are compared both qualitatively and quantitatively. The emphasis of this study is on a comparison of the response of embedded plies subjected to a homogeneous stress state. Three of the models are specifically designed for modeling the non-linear response due to distributed matrix cracking under homogeneous loading, and also account for non-linear (shear) behavior prior to the onset of cracking. The remaining two models are localized damage models intended for predicting local failure at stress concentrations. The modeling approaches of distributed vs. localized cracking as well as the different formulations of damage initiation and damage progression are compared and discussed.

BFLCRM: A BAYESIAN FUNCTIONAL LINEAR COX REGRESSION MODEL FOR PREDICTING TIME TO CONVERSION TO ALZHEIMER'S DISEASE.

Science.gov (United States)

Lee, Eunjee; Zhu, Hongtu; Kong, Dehan; Wang, Yalin; Giovanello, Kelly Sullivan; Ibrahim, Joseph G

2015-12-01

The aim of this paper is to develop a Bayesian functional linear Cox regression model (BFLCRM) with both functional and scalar covariates. This new development is motivated by establishing the likelihood of conversion to Alzheimer's disease (AD) in 346 patients with mild cognitive impairment (MCI) enrolled in the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1) and the early markers of conversion. These 346 MCI patients were followed over 48 months, with 161 MCI participants progressing to AD at 48 months. The functional linear Cox regression model was used to establish that functional covariates including hippocampus surface morphology and scalar covariates including brain MRI volumes, cognitive performance (ADAS-Cog), and APOE status can accurately predict time to onset of AD. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of BFLCRM.

Prediction of SO2 pollution incidents near a power station using partially linear models and an historical matrix of predictor-response vectors

International Nuclear Information System (INIS)

Prada-Sanchez, J.M.; Febrero-Bande, M.; Gonzalez-Manteiga, W.; Costos-Yanez, T.; Bermudez-Cela, J.L.; Lucas-Dominguez, T.

2000-01-01

Atmospheric SO 2 concentrations at sampling stations near the fossil fuel fired power station at As Pontes (La Coruna, Spain) were predicted using a model for the corresponding time series consisting of a self-explicative term and a linear combination of exogenous variables. In a supplementary simulation study, models of this kind behaved better than the corresponding pure self-explicative or pure linear regression models. (Author)

Dynamics of warm power-law plateau inflation with a generalized inflaton decay rate: predictions and constraints after Planck 2015

Energy Technology Data Exchange (ETDEWEB)

Jawad, Abdul [COMSATS Institute of Information Technology, Department of Mathematics, Lahore (Pakistan); Videla, Nelson [FCFM, Universidad de Chile, Departamento de Fisica, Santiago (Chile); Gulshan, Faiza [Lahore Leads University, Department of Mathematics, Lahore (Pakistan)

2017-05-15

In the present work, we study the consequences of considering a new family of single-field inflation models, called power-law plateau inflation, in the warm inflation framework. We consider the inflationary expansion is driven by a standard scalar field with a decay ratio Γ having a generic power-law dependence with the scalar field φ and the temperature of the thermal bath T given by Γ(φ,T) = C{sub φ}(T{sup a})/(φ{sup a-1}). Assuming that our model evolves according to the strong dissipative regime, we study the background and perturbative dynamics, obtaining the most relevant inflationary observable as the scalar power spectrum, the scalar spectral index and its running and the tensor-to-scalar ratio. The free parameters characterizing our model are constrained by considering the essential condition for warm inflation, the conditions for the model evolves according to the strong dissipative regime and the 2015 Planck results through the n{sub s}-r plane. For completeness, we study the predictions in the n{sub s}-dn{sub s}/d ln k plane. The model is consistent with a strong dissipative dynamics and predicts values for the tensor-to-scalar ratio and for the running of the scalar spectral index consistent with current bounds imposed by Planck and we conclude that the model is viable. (orig.)

Dynamics of warm power-law plateau inflation with a generalized inflaton decay rate: predictions and constraints after Planck 2015

International Nuclear Information System (INIS)

Jawad, Abdul; Videla, Nelson; Gulshan, Faiza

2017-01-01

In the present work, we study the consequences of considering a new family of single-field inflation models, called power-law plateau inflation, in the warm inflation framework. We consider the inflationary expansion is driven by a standard scalar field with a decay ratio Γ having a generic power-law dependence with the scalar field φ and the temperature of the thermal bath T given by Γ(φ,T) = C_φ(T"a)/(φ"a"-"1). Assuming that our model evolves according to the strong dissipative regime, we study the background and perturbative dynamics, obtaining the most relevant inflationary observable as the scalar power spectrum, the scalar spectral index and its running and the tensor-to-scalar ratio. The free parameters characterizing our model are constrained by considering the essential condition for warm inflation, the conditions for the model evolves according to the strong dissipative regime and the 2015 Planck results through the n_s-r plane. For completeness, we study the predictions in the n_s-dn_s/d ln k plane. The model is consistent with a strong dissipative dynamics and predicts values for the tensor-to-scalar ratio and for the running of the scalar spectral index consistent with current bounds imposed by Planck and we conclude that the model is viable. (orig.)

The Integration of Social-Ecological Resilience and Law

Science.gov (United States)

Growing recognition of the inherent uncertainty associated with the dynamics of ecological systems and their often non-linear and surprising behavior, however, presents a set of problems outside the scope of classic environmental law, and has lead to a fundamental understanding a...

The estimation and prediction of the inventories for the liquid and gaseous radwaste systems using the linear regression analysis

International Nuclear Information System (INIS)

Kim, J. Y.; Shin, C. H.; Kim, J. K.; Lee, J. K.; Park, Y. J.

2003-01-01

The variation transitions of the inventories for the liquid radwaste system and the radioactive gas have being released in containment, and their predictive values according to the operation histories of Yonggwang(YGN) 3 and 4 were analyzed by linear regression analysis methodology. The results show that the variation transitions of the inventories for those systems are linearly increasing according to the operation histories but the inventories released to the environment are considerably lower than the recommended values based on the FSAR suggestions. It is considered that some conservation were presented in the estimation methodology in preparing stage of FSAR

Law Enforcement Use of Threat Assessments to Predict Violence

Science.gov (United States)

Wood, Tracey Michelle

2016-01-01

The purpose of this qualitative, descriptive multiple case study was to explore what process, policies and procedures, or set of empirically supported norms governed law enforcement officers in a selected county in the southwest region of the United States when threat assessments were conducted on potentially violent subjects threatening mass…

Earthquake cycle simulations with rate-and-state friction and power-law viscoelasticity

Science.gov (United States)

Allison, Kali L.; Dunham, Eric M.

2018-05-01

We simulate earthquake cycles with rate-and-state fault friction and off-fault power-law viscoelasticity for the classic 2D antiplane shear problem of a vertical, strike-slip plate boundary fault. We investigate the interaction between fault slip and bulk viscous flow with experimentally-based flow laws for quartz-diorite and olivine for the crust and mantle, respectively. Simulations using three linear geotherms (dT/dz = 20, 25, and 30 K/km) produce different deformation styles at depth, ranging from significant interseismic fault creep to purely bulk viscous flow. However, they have almost identical earthquake recurrence interval, nucleation depth, and down-dip coseismic slip limit. Despite these similarities, variations in the predicted surface deformation might permit discrimination of the deformation mechanism using geodetic observations. Additionally, in the 25 and 30 K/km simulations, the crust drags the mantle; the 20 K/km simulation also predicts this, except within 10 km of the fault where the reverse occurs. However, basal tractions play a minor role in the overall force balance of the lithosphere, at least for the flow laws used in our study. Therefore, the depth-integrated stress on the fault is balanced primarily by shear stress on vertical, fault-parallel planes. Because strain rates are higher directly below the fault than far from it, stresses are also higher. Thus, the upper crust far from the fault bears a substantial part of the tectonic load, resulting in unrealistically high stresses. In the real Earth, this might lead to distributed plastic deformation or formation of subparallel faults. Alternatively, fault pore pressures in excess of hydrostatic and/or weakening mechanisms such as grain size reduction and thermo-mechanical coupling could lower the strength of the ductile fault root in the lower crust and, concomitantly, off-fault upper crustal stresses.

Ensemble Linear Neighborhood Propagation for Predicting Subchloroplast Localization of Multi-Location Proteins.

Science.gov (United States)

Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan

2016-12-02

In the postgenomic era, the number of unreviewed protein sequences is remarkably larger and grows tremendously faster than that of reviewed ones. However, existing methods for protein subchloroplast localization often ignore the information from these unlabeled proteins. This paper proposes a multi-label predictor based on ensemble linear neighborhood propagation (LNP), namely, LNP-Chlo, which leverages hybrid sequence-based feature information from both labeled and unlabeled proteins for predicting localization of both single- and multi-label chloroplast proteins. Experimental results on a stringent benchmark dataset and a novel independent dataset suggest that LNP-Chlo performs at least 6% (absolute) better than state-of-the-art predictors. This paper also demonstrates that ensemble LNP significantly outperforms LNP based on individual features. For readers' convenience, the online Web server LNP-Chlo is freely available at http://bioinfo.eie.polyu.edu.hk/LNPChloServer/ .

CHAOS THEORY OF LAW: PENJELASAN ATAS KETERATURAN DAN KETIDAKTERATURAN DALAM HUKUM

Directory of Open Access Journals (Sweden)

. Sudjito

2015-02-01

Full Text Available It is of no posibilitiy to understand the complex reality of law by means of linear-mechanistic approach used to be ulitilized in rechtsdogmatiek or legal-positivism which is still dominant in the teaching of law. It needs our readiness to see the world of law not as in order but in chaos; and this is the basic reason to present the Chaos theory of law. It is hoped that this theory will enable us to explore and explain the law throroughly. Thus the law science will be the total science which does not limit itself to the positive law, the state law or the lawyer’s law. Furthermore this chaos theory is expected to give better description and comprehension of law. Order and disorder are not opposant, or white-black dichotomy, but they are interrelated, interwoven and having mutual fulfilment in a sustainably and continually change process. The Chaos theory of law, thus, constitutes a theory which is qualified to give good explanation of the complex reality of law and provide the best solution to the critical condition of law in our country.

Straight line fitting and predictions: On a marginal likelihood approach to linear regression and errors-in-variables models

Science.gov (United States)

Christiansen, Bo

2015-04-01

Linear regression methods are without doubt the most used approaches to describe and predict data in the physical sciences. They are often good first order approximations and they are in general easier to apply and interpret than more advanced methods. However, even the properties of univariate regression can lead to debate over the appropriateness of various models as witnessed by the recent discussion about climate reconstruction methods. Before linear regression is applied important choices have to be made regarding the origins of the noise terms and regarding which of the two variables under consideration that should be treated as the independent variable. These decisions are often not easy to make but they may have a considerable impact on the results. We seek to give a unified probabilistic - Bayesian with flat priors - treatment of univariate linear regression and prediction by taking, as starting point, the general errors-in-variables model (Christiansen, J. Clim., 27, 2014-2031, 2014). Other versions of linear regression can be obtained as limits of this model. We derive the likelihood of the model parameters and predictands of the general errors-in-variables model by marginalizing over the nuisance parameters. The resulting likelihood is relatively simple and easy to analyze and calculate. The well known unidentifiability of the errors-in-variables model is manifested as the absence of a well-defined maximum in the likelihood. However, this does not mean that probabilistic inference can not be made; the marginal likelihoods of model parameters and the predictands have, in general, well-defined maxima. We also include a probabilistic version of classical calibration and show how it is related to the errors-in-variables model. The results are illustrated by an example from the coupling between the lower stratosphere and the troposphere in the Northern Hemisphere winter.

Mamdani-Fuzzy Modeling Approach for Quality Prediction of Non-Linear Laser Lathing Process

Science.gov (United States)

Sivaraos; Khalim, A. Z.; Salleh, M. S.; Sivakumar, D.; Kadirgama, K.

2018-03-01

Lathing is a process to fashioning stock materials into desired cylindrical shapes which usually performed by traditional lathe machine. But, the recent rapid advancements in engineering materials and precision demand gives a great challenge to the traditional method. The main drawback of conventional lathe is its mechanical contact which brings to the undesirable tool wear, heat affected zone, finishing, and dimensional accuracy especially taper quality in machining of stock with high length to diameter ratio. Therefore, a novel approach has been devised to investigate in transforming a 2D flatbed CO2 laser cutting machine into 3D laser lathing capability as an alternative solution. Three significant design parameters were selected for this experiment, namely cutting speed, spinning speed, and depth of cut. Total of 24 experiments were performed with eight (8) sequential runs where they were then replicated three (3) times. The experimental results were then used to establish Mamdani - Fuzzy predictive model where it yields the accuracy of more than 95%. Thus, the proposed Mamdani - Fuzzy modelling approach is found very much suitable and practical for quality prediction of non-linear laser lathing process for cylindrical stocks of 10mm diameter.

Spreading dynamics of power-law fluid droplets

International Nuclear Information System (INIS)

Liang Zhanpeng; Peng Xiaofeng; Wang Xiaodong; Lee, D-J; Su Ay

2009-01-01

This paper aims at providing a summary of the theoretical models available for non-Newtonian fluid spreading dynamics. Experimental findings and model predictions for a Newtonian fluid spreading test are briefly reviewed. Then how the complete wetting and partial wetting power-law fluids spread over a solid substrate is examined. The possible extension of Newtonian fluid models to power-law fluids is also discussed.

Frequency prediction by linear stability analysis around mean flow

Science.gov (United States)

Bengana, Yacine; Tuckerman, Laurette

2017-11-01

The frequency of certain limit cycles resulting from a Hopf bifurcation, such as the von Karman vortex street, can be predicted by linear stability analysis around their mean flows. Barkley (2006) has shown this to yield an eigenvalue whose real part is zero and whose imaginary part matches the nonlinear frequency. This property was named RZIF by Turton et al. (2015); moreover they found that the traveling waves (TW) of thermosolutal convection have the RZIF property. They explained this as a consequence of the fact that the temporal Fourier spectrum is dominated by the mean flow and first harmonic. We could therefore consider that only the first mode is important in the saturation of the mean flow as presented in the Self-Consistent Model (SCM) of Mantic-Lugo et al. (2014). We have implemented a full Newton's method to solve the SCM for thermosolutal convection. We show that while the RZIF property is satisfied far from the threshold, the SCM model reproduces the exact frequency only very close to the threshold. Thus, the nonlinear interaction of only the first mode with itself is insufficiently accurate to estimate the mean flow. Our next step will be to take into account higher harmonics and to apply this analysis to the standing waves, for which RZIF does not hold.

High-Alpha Research Vehicle Lateral-Directional Control Law Description, Analyses, and Simulation Results

Science.gov (United States)

Davidson, John B.; Murphy, Patrick C.; Lallman, Frederick J.; Hoffler, Keith D.; Bacon, Barton J.

1998-01-01

This report contains a description of a lateral-directional control law designed for the NASA High-Alpha Research Vehicle (HARV). The HARV is a F/A-18 aircraft modified to include a research flight computer, spin chute, and thrust-vectoring in the pitch and yaw axes. Two separate design tools, CRAFT and Pseudo Controls, were integrated to synthesize the lateral-directional control law. This report contains a description of the lateral-directional control law, analyses, and nonlinear simulation (batch and piloted) results. Linear analysis results include closed-loop eigenvalues, stability margins, robustness to changes in various plant parameters, and servo-elastic frequency responses. Step time responses from nonlinear batch simulation are presented and compared to design guidelines. Piloted simulation task scenarios, task guidelines, and pilot subjective ratings for the various maneuvers are discussed. Linear analysis shows that the control law meets the stability margin guidelines and is robust to stability and control parameter changes. Nonlinear batch simulation analysis shows the control law exhibits good performance and meets most of the design guidelines over the entire range of angle-of-attack. This control law (designated NASA-1A) was flight tested during the Summer of 1994 at NASA Dryden Flight Research Center.

Periodic feedback stabilization for linear periodic evolution equations

CERN Document Server

Wang, Gengsheng

2016-01-01

This book introduces a number of recent advances regarding periodic feedback stabilization for linear and time periodic evolution equations. First, it presents selected connections between linear quadratic optimal control theory and feedback stabilization theory for linear periodic evolution equations. Secondly, it identifies several criteria for the periodic feedback stabilization from the perspective of geometry, algebra and analyses respectively. Next, it describes several ways to design periodic feedback laws. Lastly, the book introduces readers to key methods for designing the control machines. Given its coverage and scope, it offers a helpful guide for graduate students and researchers in the areas of control theory and applied mathematics.

Clock ambiguity and the emergence of physical laws

International Nuclear Information System (INIS)

Albrecht, Andreas; Iglesias, Alberto

2008-01-01

The process of identifying a time variable in time-reparameterization invariant theories results in great ambiguities about the actual laws of physics described by a given theory. A theory set up to describe one set of physical laws can equally well be interpreted as describing any other laws of physics by making a different choice of time variable or clock. In this article we demonstrate how this 'clock ambiguity' arises and then discuss how one might still hope to extract specific predictions about the laws of physics even when the clock ambiguity is present. We argue that a requirement of quasiseparability should play a critical role in such an analysis. As a step in this direction, we compare the Hamiltonian of a local quantum field theory with a completely random Hamiltonian. We find that any random Hamiltonian (constructed in a sufficiently large space) can yield a 'good enough' approximation to a local field theory. Based on this result we argue that theories that suffer from the clock ambiguity may in the end provide a viable fundamental framework for physics in which locality can be seen as a strongly favored (or predicted) emergent behavior. We also speculate on how other key aspects of known physics such as gauge symmetries and Poincare invariance might be predicted to emerge in this framework.

Effect of Process Parameters on Friction Model in Computer Simulation of Linear Friction Welding

Directory of Open Access Journals (Sweden)

A. Yamileva

2014-07-01

Full Text Available The friction model is important part of a numerical model of linear friction welding. Its selection determines the accuracy of the results. Existing models employ the classical law of Amonton-Coulomb where the friction coefficient is either constant or linearly dependent on a single parameter. Determination of the coefficient of friction is a time consuming process that requires a lot of experiments. So the feasibility of determinating the complex dependence should be assessing by analysis of effect of approximating law for friction model on simulation results.

Newton's law of cooling revisited

International Nuclear Information System (INIS)

Vollmer, M

2009-01-01

The cooling of objects is often described by a law, attributed to Newton, which states that the temperature difference of a cooling body with respect to the surroundings decreases exponentially with time. Such behaviour has been observed for many laboratory experiments, which led to a wide acceptance of this approach. However, the heat transfer from any object to its surrounding is not only due to conduction and convection but also due to radiation. The latter does not vary linearly with temperature difference, which leads to deviations from Newton's law. This paper presents a theoretical analysis of the cooling of objects with a small Biot number. It is shown that Newton's law of cooling, i.e. simple exponential behaviour, is mostly valid if temperature differences are below a certain threshold which depends on the experimental conditions. For any larger temperature differences appreciable deviations occur which need the complete nonlinear treatment. This is demonstrated by results of some laboratory experiments which use IR imaging to measure surface temperatures of solid cooling objects with temperature differences of up to 300 K.

Some optimal considerations in attitude control systems. [evaluation of value of relative weighting between time and fuel for relay control law

Science.gov (United States)

Boland, J. S., III

1973-01-01

The conventional six-engine reaction control jet relay attitude control law with deadband is shown to be a good linear approximation to a weighted time-fuel optimal control law. Techniques for evaluating the value of the relative weighting between time and fuel for a particular relay control law is studied along with techniques to interrelate other parameters for the two control laws. Vehicle attitude control laws employing control moment gyros are then investigated. Steering laws obtained from the expression for the reaction torque of the gyro configuration are compared to a total optimal attitude control law that is derived from optimal linear regulator theory. This total optimal attitude control law has computational disadvantages in the solving of the matrix Riccati equation. Several computational algorithms for solving the matrix Riccati equation are investigated with respect to accuracy, computational storage requirements, and computational speed.

Linear drag law for high-Reynolds-number flow past an oscillating body

Science.gov (United States)

Agre, Natalie; Childress, Stephen; Zhang, Jun; Ristroph, Leif

2016-07-01

An object immersed in a fast flow typically experiences fluid forces that increase with the square of speed. Here we explore how this high-Reynolds-number force-speed relationship is affected by unsteady motions of a body. Experiments on disks that are driven to oscillate while progressing through air reveal two distinct regimes: a conventional quadratic relationship for slow oscillations and an anomalous scaling for fast flapping in which the time-averaged drag increases linearly with flow speed. In the linear regime, flow visualization shows that a pair of counterrotating vortices is shed with each oscillation and a model that views a train of such dipoles as a momentum jet reproduces the linearity. We also show that appropriate scaling variables collapse the experimental data from both regimes and for different oscillatory motions into a single drag-speed relationship. These results could provide insight into the aerodynamic resistance incurred by oscillating wings in flight and they suggest that vibrations can be an effective means to actively control the drag on an object.

Performance Prediction Modelling for Flexible Pavement on Low Volume Roads Using Multiple Linear Regression Analysis

Directory of Open Access Journals (Sweden)

C. Makendran

2015-01-01

Full Text Available Prediction models for low volume village roads in India are developed to evaluate the progression of different types of distress such as roughness, cracking, and potholes. Even though the Government of India is investing huge quantum of money on road construction every year, poor control over the quality of road construction and its subsequent maintenance is leading to the faster road deterioration. In this regard, it is essential that scientific maintenance procedures are to be evolved on the basis of performance of low volume flexible pavements. Considering the above, an attempt has been made in this research endeavor to develop prediction models to understand the progression of roughness, cracking, and potholes in flexible pavements exposed to least or nil routine maintenance. Distress data were collected from the low volume rural roads covering about 173 stretches spread across Tamil Nadu state in India. Based on the above collected data, distress prediction models have been developed using multiple linear regression analysis. Further, the models have been validated using independent field data. It can be concluded that the models developed in this study can serve as useful tools for the practicing engineers maintaining flexible pavements on low volume roads.

Paper-cutting operations using scissors in Drury's law tasks.

Science.gov (United States)

Yamanaka, Shota; Miyashita, Homei

2018-05-01

Human performance modeling is a core topic in ergonomics. In addition to deriving models, it is important to verify the kinds of tasks that can be modeled. Drury's law is promising for path tracking tasks such as navigating a path with pens or driving a car. We conducted an experiment based on the observation that paper-cutting tasks using scissors resemble such tasks. The results showed that cutting arc-like paths (1/4 of a circle) showed an excellent fit with Drury's law (R 2  > 0.98), whereas cutting linear paths showed a worse fit (R 2  > 0.87). Since linear paths yielded better fits when path amplitudes were divided (R 2  > 0.99 for all amplitudes), we discuss the characteristics of paper-cutting operations using scissors. Copyright © 2018 Elsevier Ltd. All rights reserved.

The interface of the civil and criminal law of suicide at common law (1194-1845).

Science.gov (United States)

Mendelson, Danuta; Freckelton, Ian

2013-01-01

Nowadays, suicide is considered essentially a private act, although what constitutes suicide for epidemiological and even clinical purposes in not wholly resolved. Historically, however, at common law, the act of self-killing was a felony with significant religious and legal consequences that impacted upon the deceased person as well as upon his or her whole family. This article identifies the influence of Christian theology, legal theory, and social and medical developments upon attitudes to the felony of self-murder and its definition. It focuses upon the start of more psychologically informed attitudes manifested in landmark court judgments involving exclusion clauses in English mid-nineteenth century insurance contracts. The article illustrates that the law in respect of socially controversial matters does not necessarily develops in a linear progression, nor does it accurately reflect public sentiments. More specifically, the article describes an ongoing definitional conundrum with suicide--whether it should be designated as committed by persons of significantly impaired mental state. The authors observe that in spite of reform to the criminal law of suicide, the civil law relating to suicide has continued to be characterised by ambivalence, ambiguity and significant vestiges of counter-therapeutic moralising. Copyright © 2013 Elsevier Ltd. All rights reserved.

Development of Bundle Position-Wise Linear Model for Predicting the Pressure Tube Diametral Creep in CANDU Reactors

International Nuclear Information System (INIS)

Lee, Jae Yong; Na, Man Gyun

2011-01-01

Diametral creep of the pressure tube (PT) is one of the principal aging mechanisms governing the heat transfer and hydraulic degradation of a heat transport system. PT diametral creep leads to diametral expansion that affects the thermal hydraulic characteristics of the coolant channels and the critical heat flux. Therefore, it is essential to predict the PT diametral creep in CANDU reactors, which is caused mainly by fast neutron irradiation, reactor coolant temperature and so forth. The currently used PT diametral creep prediction model considers the complex interactions between the effects of temperature and fast neutron flux on the deformation of PT zirconium alloys. The model assumes that long-term steady-state deformation consists of separable, additive components from thermal creep, irradiation creep and irradiation growth. This is a mechanistic model based on measured data. However, this model has high prediction uncertainty. Recently, a statistical error modeling method was developed using plant inspection data from the Bruce B CANDU reactor. The aim of this study was to develop a bundle position-wise linear model (BPLM) to predict PT diametral creep employing previously measured PT diameters and HTS operating conditions. There are twelve bundles in a fuel channel and for each bundle, a linear model was developed by using the dependent variables, such as the fast neutron fluxes and the bundle temperatures. The training data set was selected using the subtractive clustering method. The data of 39 channels that consist of 80 percent of a total of 49 measured channels from Units 2, 3 and 4 were used to develop the BPLM models. The remaining 10 channels' data were used to test the developed BPLM models. The BPLM was optimized by the maximum likelihood estimation method. The developed BPLM to predict PT diametral creep was verified using the operating data gathered from the Units 2,3 and 4 in Korea. Two error components for the BPLM, which are the epistemic

Performance prediction of mechanical excavators from linear cutter tests on Yucca Mountain welded tuffs

International Nuclear Information System (INIS)

Gertsch, R.; Ozdemir, L.

1992-09-01

The performances of mechanical excavators are predicted for excavations in welded tuff. Emphasis is given to tunnel boring machine evaluations based on linear cutting machine test data obtained on samples of Topopah Spring welded tuff. The tests involve measurement of forces as cutters are applied to the rock surface at certain spacing and penetrations. Two disc and two point-attack cutters representing currently available technology are thus evaluated. The performance predictions based on these direct experimental measurements are believed to be more accurate than any previous values for mechanical excavation of welded tuff. The calculations of performance are predicated on minimizing the amount of energy required to excavate the welded tuff. Specific energy decreases with increasing spacing and penetration, and reaches its lowest at the widest spacing and deepest penetration used in this test program. Using the force, spacing, and penetration data from this experimental program, the thrust, torque, power, and rate of penetration are calculated for several types of mechanical excavators. The results of this study show that the candidate excavators will require higher torque and power than heretofore estimated

Deviations from Newton's law in supersymmetric large extra dimensions

International Nuclear Information System (INIS)

Callin, P.; Burgess, C.P.

2006-01-01

Deviations from Newton's inverse-squared law at the micron length scale are smoking-gun signals for models containing supersymmetric large extra dimensions (SLEDs), which have been proposed as approaches for resolving the cosmological constant problem. Just like their non-supersymmetric counterparts, SLED models predict gravity to deviate from the inverse-square law because of the advent of new dimensions at sub-millimeter scales. However SLED models differ from their non-supersymmetric counterparts in three important ways: (i) the size of the extra dimensions is fixed by the observed value of the dark energy density, making it impossible to shorten the range over which new deviations from Newton's law must be seen; (ii) supersymmetry predicts there to be more fields in the extra dimensions than just gravity, implying different types of couplings to matter and the possibility of repulsive as well as attractive interactions; and (iii) the same mechanism which is purported to keep the cosmological constant naturally small also keeps the extra-dimensional moduli effectively massless, leading to deviations from general relativity in the far infrared of the scalar-tensor form. We here explore the deviations from Newton's law which are predicted over micron distances, and show the ways in which they differ and resemble those in the non-supersymmetric case

Cayley number and conservation laws for elementary particles

International Nuclear Information System (INIS)

Vollendorf, F.

1975-01-01

It is shown that the five conservation laws of charge, hyper-charge, barion number and the two lepton numbers lead to the construction of a commutative non-associative 24 dimensional linear algebra. Each element of the algebra is an ordered set of three Cayley numbers. (orig.) [de

Assessing peak aerobic capacity in Dutch law enforcement officers

NARCIS (Netherlands)

Wittink, Harriet; Takken, Tim; de Groot, Janke; Reneman, Michiel; Peters, Roelof; Vanhees, Luc

2015-01-01

Objectives: To cross-validate the existing peak rate of oxygen consumption (VO2peak) prediction equations in Dutch law enforcement officers and to determine whether these prediction equations can be used to predict VO2peak for groups and in a single individual. A further objective was to report

Assessing peak aerobic capacity in Dutch law enforcement officers.

NARCIS (Netherlands)

Wittink, H.; Takken, T.; Groot, J.F. de; Reneman, M.; Peters, R.; Vanhees, L.

2015-01-01

Objectives: To cross-validate the existing peak rate of oxygen consumption (VO2peak) prediction equations in Dutch law enforcement officers and to determine whether these prediction equations can be used to predict VO2peak for groups and in a single individual. A further objective was to report

Strength of smoke-free air laws and indoor air quality.

Science.gov (United States)

Lee, Kiyoung; Hahn, Ellen J; Robertson, Heather E; Lee, Seongjik; Vogel, Suzann L; Travers, Mark J

2009-04-01

Smoke-free air laws have been implemented in many Kentucky communities to protect the public from the harmful effects of secondhand smoke exposure. The impact of different strengths of smoke-free air laws on indoor air quality was assessed. Indoor air quality in hospitality venues was assessed in seven communities before and after comprehensive smoke-free air laws and in two communities only after partial smoke-free air laws. One community was measured three times: before any smoke-free air law, after the initial partial law, and after the law was strengthened to cover all workplaces and public places with few exemptions. Real-time measurements of particulate matters with 2.5 mum aerodynamic diameter or smaller (PM(2.5)) were obtained. When comprehensive smoke-free air laws were implemented, indoor PM(2.5) concentrations decreased significantly from 161 to 20 microg/m3. In one community that implemented a comprehensive smoke-free law after initially passing a partial law, indoor PM(2.5) concentrations were 304 microg/m3 before the law, 338 microg/m3 after the partial law, and 9 microg/m3 after the comprehensive law. The study clearly demonstrated that partial smoke-free air laws do not improve indoor air quality. A significant linear trend indicated that PM(2.5) levels in the establishments decreased with fewer numbers of burning cigarettes. Only comprehensive smoke-free air laws are effective in reducing indoor air pollution from secondhand tobacco smoke.

Predicting sintering deformation of ceramic film constrained by rigid substrate using anisotropic constitutive law

International Nuclear Information System (INIS)

Li Fan; Pan Jingzhe; Guillon, Olivier; Cocks, Alan

2010-01-01

Sintering of ceramic films on a solid substrate is an important technology for fabricating a range of products, including solid oxide fuel cells, micro-electronic PZT films and protective coatings. There is clear evidence that the constrained sintering process is anisotropic in nature. This paper presents a study of the constrained sintering deformation using an anisotropic constitutive law. The state of the material is described using the sintering strains rather than the relative density. In the limiting case of free sintering, the constitutive law reduces to a conventional isotropic constitutive law. The anisotropic constitutive law is used to calculate sintering deformation of a constrained film bonded to a rigid substrate and the compressive stress required in a sinter-forging experiment to achieve zero lateral shrinkage. The results are compared with experimental data in the literature. It is shown that the anisotropic constitutive law can capture the behaviour of the materials observed in the sintering experiments.

Linear and non-linear autoregressive models for short-term wind speed forecasting

International Nuclear Information System (INIS)

Lydia, M.; Suresh Kumar, S.; Immanuel Selvakumar, A.; Edwin Prem Kumar, G.

2016-01-01

Highlights: • Models for wind speed prediction at 10-min intervals up to 1 h built on time-series wind speed data. • Four different multivariate models for wind speed built based on exogenous variables. • Non-linear models built using three data mining algorithms outperform the linear models. • Autoregressive models based on wind direction perform better than other models. - Abstract: Wind speed forecasting aids in estimating the energy produced from wind farms. The soaring energy demands of the world and minimal availability of conventional energy sources have significantly increased the role of non-conventional sources of energy like solar, wind, etc. Development of models for wind speed forecasting with higher reliability and greater accuracy is the need of the hour. In this paper, models for predicting wind speed at 10-min intervals up to 1 h have been built based on linear and non-linear autoregressive moving average models with and without external variables. The autoregressive moving average models based on wind direction and annual trends have been built using data obtained from Sotavento Galicia Plc. and autoregressive moving average models based on wind direction, wind shear and temperature have been built on data obtained from Centre for Wind Energy Technology, Chennai, India. While the parameters of the linear models are obtained using the Gauss–Newton algorithm, the non-linear autoregressive models are developed using three different data mining algorithms. The accuracy of the models has been measured using three performance metrics namely, the Mean Absolute Error, Root Mean Squared Error and Mean Absolute Percentage Error.

Effective and Robust Generalized Predictive Speed Control of Induction Motor

Directory of Open Access Journals (Sweden)

Patxi Alkorta

2013-01-01

Full Text Available This paper presents and validates a new proposal for effective speed vector control of induction motors based on linear Generalized Predictive Control (GPC law. The presented GPC-PI cascade configuration simplifies the design with regard to GPC-GPC cascade configuration, maintaining the advantages of the predictive control algorithm. The robust stability of the closed loop system is demonstrated by the poles placement method for several typical cases of uncertainties in induction motors. The controller has been tested using several simulations and experiments and has been compared with Proportional Integral Derivative (PID and Sliding Mode (SM control schemes, obtaining outstanding results in speed tracking even in the presence of parameter uncertainties, unknown load disturbance, and measurement noise in the loop signals, suggesting its use in industrial applications.

Prediction of SO{sub 2} pollution incidents near a power station using partially linear models and an historical matrix of predictor-response vectors

Energy Technology Data Exchange (ETDEWEB)

Prada-Sanchez, J.M.; Febrero-Bande, M.; Gonzalez-Manteiga, W. [Universidad de Santiago de Compostela, Dept. de Estadistica e Investigacion Operativa, Santiago de Compostela (Spain); Costos-Yanez, T. [Universidad de Vigo, Dept. de Estadistica e Investigacion Operativa, Orense (Spain); Bermudez-Cela, J.L.; Lucas-Dominguez, T. [Laboratorio, Central Termica de As Pontes, La Coruna (Spain)

2000-07-01

Atmospheric SO{sub 2} concentrations at sampling stations near the fossil fuel fired power station at As Pontes (La Coruna, Spain) were predicted using a model for the corresponding time series consisting of a self-explicative term and a linear combination of exogenous variables. In a supplementary simulation study, models of this kind behaved better than the corresponding pure self-explicative or pure linear regression models. (Author)

Resolving Actuator Redundancy - Control Allocation vs. Linear Quadratic Control

OpenAIRE

Härkegård, Ola

2004-01-01

When designing control laws for systems with more inputs than controlled variables, one issue to consider is how to deal with actuator redundancy. Two tools for distributing the control effort among a redundant set of actuators are control allocation and linear quadratic control design. In this paper, we investigate the relationship between these two design tools when a quadratic performance index is used for control allocation. We show that for a particular class of linear systems, they give...

Predicting the multi-domain progression of Parkinson's disease: a Bayesian multivariate generalized linear mixed-effect model.

Science.gov (United States)

Wang, Ming; Li, Zheng; Lee, Eun Young; Lewis, Mechelle M; Zhang, Lijun; Sterling, Nicholas W; Wagner, Daymond; Eslinger, Paul; Du, Guangwei; Huang, Xuemei

2017-09-25

It is challenging for current statistical models to predict clinical progression of Parkinson's disease (PD) because of the involvement of multi-domains and longitudinal data. Past univariate longitudinal or multivariate analyses from cross-sectional trials have limited power to predict individual outcomes or a single moment. The multivariate generalized linear mixed-effect model (GLMM) under the Bayesian framework was proposed to study multi-domain longitudinal outcomes obtained at baseline, 18-, and 36-month. The outcomes included motor, non-motor, and postural instability scores from the MDS-UPDRS, and demographic and standardized clinical data were utilized as covariates. The dynamic prediction was performed for both internal and external subjects using the samples from the posterior distributions of the parameter estimates and random effects, and also the predictive accuracy was evaluated based on the root of mean square error (RMSE), absolute bias (AB) and the area under the receiver operating characteristic (ROC) curve. First, our prediction model identified clinical data that were differentially associated with motor, non-motor, and postural stability scores. Second, the predictive accuracy of our model for the training data was assessed, and improved prediction was gained in particularly for non-motor (RMSE and AB: 2.89 and 2.20) compared to univariate analysis (RMSE and AB: 3.04 and 2.35). Third, the individual-level predictions of longitudinal trajectories for the testing data were performed, with ~80% observed values falling within the 95% credible intervals. Multivariate general mixed models hold promise to predict clinical progression of individual outcomes in PD. The data was obtained from Dr. Xuemei Huang's NIH grant R01 NS060722 , part of NINDS PD Biomarker Program (PDBP). All data was entered within 24 h of collection to the Data Management Repository (DMR), which is publically available ( https://pdbp.ninds.nih.gov/data-management ).

Model-Checking of Linear-Time Properties in Multi-Valued Systems

OpenAIRE

Li, Yongming; Droste, Manfred; Lei, Lihui

2012-01-01

In this paper, we study model-checking of linear-time properties in multi-valued systems. Safety property, invariant property, liveness property, persistence and dual-persistence properties in multi-valued logic systems are introduced. Some algorithms related to the above multi-valued linear-time properties are discussed. The verification of multi-valued regular safety properties and multi-valued $\\omega$-regular properties using lattice-valued automata are thoroughly studied. Since the law o...

Modified circular velocity law

Science.gov (United States)

Djeghloul, Nazim

2018-05-01

A modified circular velocity law is presented for a test body orbiting around a spherically symmetric mass. This law exhibits a distance scale parameter and allows to recover both usual Newtonian behaviour for lower distances and a constant velocity limit at large scale. Application to the Galaxy predicts the known behaviour and also leads to a galactic mass in accordance with the measured visible stellar mass so that additional dark matter inside the Galaxy can be avoided. It is also shown that this circular velocity law can be embedded in a geometrical description of spacetime within the standard general relativity framework upon relaxing the usual asymptotic flatness condition. This formulation allows to redefine the introduced Newtonian scale limit in term of the central mass exclusively. Moreover, a satisfactory answer to the galactic escape speed problem can be provided indicating the possibility that one can also get rid of dark matter halo outside the Galaxy.

The field-induced laws of thermodynamic properties in the two-dimensional spin-1 ferromagnetic Heisenberg model with the exchange and single-ion anisotropies

International Nuclear Information System (INIS)

Pu Qiurong; Chen Yuan

2013-01-01

Green's function method is applied to investigate the two-dimensional spin-1 ferromagnetic Heisenberg model with the exchange and single-ion anisotropies. In the presence of the magnetic field, the effects of the anisotropies and field on the thermodynamic properties are obtained within the random phase approximation combining with Anderson-Callen approximation. The field-induced laws are found for the thermodynamic properties. Field dependences of heights of the susceptibility maximum and specific heat maximum fit well to power laws. The linear increase at high fields is shown for positions of the susceptibility maximum and specific heat maximum. A power law at low fields occurs for the position of the susceptibility maximum. At the positions of the maxima, the magnetization and internal energy display the power-law increase and linear decrease with the field, respectively. The exponents of the power laws are dependent of the anisotropies, as well as the slopes of the linear laws. Our results do not support the 2/3 power law which was obtained by the Landau theory.

A State-Space Approach to Optimal Level-Crossing Prediction for Linear Gaussian Processes

Science.gov (United States)

Martin, Rodney Alexander

2009-01-01

In many complex engineered systems, the ability to give an alarm prior to impending critical events is of great importance. These critical events may have varying degrees of severity, and in fact they may occur during normal system operation. In this article, we investigate approximations to theoretically optimal methods of designing alarm systems for the prediction of level-crossings by a zero-mean stationary linear dynamic system driven by Gaussian noise. An optimal alarm system is designed to elicit the fewest false alarms for a fixed detection probability. This work introduces the use of Kalman filtering in tandem with the optimal level-crossing problem. It is shown that there is a negligible loss in overall accuracy when using approximations to the theoretically optimal predictor, at the advantage of greatly reduced computational complexity. I

Nuclear Energy Law and Arbo Law/Safety Law

International Nuclear Information System (INIS)

Eijnde, J.G. van den

1986-01-01

The legal aspects of radiation protection in the Netherlands are described. Radiation protection is regulated mainly in the Nuclear Energy Law. The Arbo Law also has some sections about radiation protection. The interaction between both laws is discussed. (Auth.)

The laws of conservation of physics and the β-decay of atomic nuclei

International Nuclear Information System (INIS)

Bagge, E.R.

1976-01-01

The laws of conservation of energy, the momentum of translation and the angular momentum of a system form a closed unit according to Noether's theorem. A generalisation of these laws taking into account the states of negative energies must therefore comprise all laws of conservation. A new interpretation of the β-decay without neutrinos should thus take the law of conservation of energy at the β-continuum for the world and anti-world as motivation to demand corresponding laws of conservation for the linear momentum and the spin and it will be shown that this new interpretation of the laws of conservation exactly suffices to interpret all characteristic phenomena of β-decay in a manner free of contradiction. (orig.) [de

Dual-Source Linear Energy Prediction (LINE-P) Model in the Context of WSNs.

Science.gov (United States)

Ahmed, Faisal; Tamberg, Gert; Le Moullec, Yannick; Annus, Paul

2017-07-20

Energy harvesting technologies such as miniature power solar panels and micro wind turbines are increasingly used to help power wireless sensor network nodes. However, a major drawback of energy harvesting is its varying and intermittent characteristic, which can negatively affect the quality of service. This calls for careful design and operation of the nodes, possibly by means of, e.g., dynamic duty cycling and/or dynamic frequency and voltage scaling. In this context, various energy prediction models have been proposed in the literature; however, they are typically compute-intensive or only suitable for a single type of energy source. In this paper, we propose Linear Energy Prediction "LINE-P", a lightweight, yet relatively accurate model based on approximation and sampling theory; LINE-P is suitable for dual-source energy harvesting. Simulations and comparisons against existing similar models have been conducted with low and medium resolutions (i.e., 60 and 22 min intervals/24 h) for the solar energy source (low variations) and with high resolutions (15 min intervals/24 h) for the wind energy source. The results show that the accuracy of the solar-based and wind-based predictions is up to approximately 98% and 96%, respectively, while requiring a lower complexity and memory than the other models. For the cases where LINE-P's accuracy is lower than that of other approaches, it still has the advantage of lower computing requirements, making it more suitable for embedded implementation, e.g., in wireless sensor network coordinator nodes or gateways.

Energy Decay Laws in Strongly Anisotropic Magnetohydrodynamic Turbulence

International Nuclear Information System (INIS)

Bigot, Barbara; Galtier, Sebastien; Politano, Helene

2008-01-01

We investigate the influence of a uniform magnetic field B 0 =B 0 e parallel on energy decay laws in incompressible magnetohydrodynamic (MHD) turbulence. The nonlinear transfer reduction along B 0 is included in a model that distinguishes parallel and perpendicular directions, following a phenomenology of Kraichnan. We predict a slowing down of the energy decay due to anisotropy in the limit of strong B 0 , with distinct power laws for energy decay of shear- and pseudo-Alfven waves. Numerical results from the kinetic equations of Alfven wave turbulence recover these predictions, and MHD numerical results clearly tend to follow them in the lowest perpendicular planes

Professor Eleanor Fox New York University School of Law

International Development Research Centre (IDRC) Digital Library (Canada)

dzavalamora

The Global Administrative Law project. ▣ Our sample jurisdictions. ▫ Mature. · United States. · Canada. · Australia/New Zealand. · European Union. · Japan. ▫ Newer and evolving: China, Chile, South Africa. ▫ International. ▣ The norms. ▫ E.g. Rule of law, predictability, timeliness, expertise, transparency, reason-giving, right ...

Linear Unlearning for Cross-Validation

DEFF Research Database (Denmark)

Hansen, Lars Kai; Larsen, Jan

1996-01-01

The leave-one-out cross-validation scheme for generalization assessment of neural network models is computationally expensive due to replicated training sessions. In this paper we suggest linear unlearning of examples as an approach to approximative cross-validation. Further, we discuss...... time series prediction benchmark demonstrate the potential of the linear unlearning technique...

Linear feedback control, adaptive feedback control and their combination for chaos (lag) synchronization of LC chaotic systems

International Nuclear Information System (INIS)

Yan Zhenya; Yu Pei

2007-01-01

In this paper, we study chaos (lag) synchronization of a new LC chaotic system, which can exhibit not only a two-scroll attractor but also two double-scroll attractors for different parameter values, via three types of state feedback controls: (i) linear feedback control; (ii) adaptive feedback control; and (iii) a combination of linear feedback and adaptive feedback controls. As a consequence, ten families of new feedback control laws are designed to obtain global chaos lag synchronization for τ < 0 and global chaos synchronization for τ = 0 of the LC system. Numerical simulations are used to illustrate these theoretical results. Each family of these obtained feedback control laws, including two linear (adaptive) functions or one linear function and one adaptive function, is added to two equations of the LC system. This is simpler than the known synchronization controllers, which apply controllers to all equations of the LC system. Moreover, based on the obtained results of the LC system, we also derive the control laws for chaos (lag) synchronization of another new type of chaotic system

From coastal barriers to mountain belts - commonalities in fundamental geomorphic scaling laws

Science.gov (United States)

Lazarus, E.

2016-12-01

Overwash is a sediment-transport process essential to the form and resilience of coastal barrier landscapes. Driven by storm events, overwash leaves behind distinctive sedimentary features that, although intensively studied, have lacked unifying quantitative descriptions with which to compare their morphological attributes across documented examples or relate them to other morphodynamic phenomena. Geomorphic scaling laws quantify how measures of shape and size change with respect to another - information that helps to constrain predictions of future change and reconstructions of past environmental conditions. Here, a physical model of erosional and depositional overwash morphology yields intrinsic, allometric scaling laws involving length, width, area, volume, and alongshore spacing. Corroborative comparisons with natural washover morphology indicate scale invariance spanning several orders of magnitude. Several observers of the physical model remarked that the overwashed barrier resembled a dissected linear mountain front with an alluvial apron - an intriguing reimagining of the intended analog. Indeed, that resemblance is reflected quantitatively in these new scaling relationships, which align with canonical scaling laws for terrestrial and marine drainage basins and alluvial fans on Earth and Mars. This finding suggests disparate geomorphic systems that share common allometric properties may be related dynamically, perhaps by an influence more fundamental than characteristic erosion and deposition processes. Such an influence could come from emergent behavior at the intersection of advection and diffusion. Geomorphic behaviors at advection-diffusion transitions (and vice versa), specifically, could be the key to disentangling mechanistic causality from acausality in physical landscape patterns.

Foundations of the non-linear mechanics of continua

CERN Document Server

Sedov, L I

1966-01-01

International Series of Monographs on Interdisciplinary and Advanced Topics in Science and Engineering, Volume 1: Foundations of the Non-Linear Mechanics of Continua deals with the theoretical apparatus, principal concepts, and principles used in the construction of models of material bodies that fill space continuously. This book consists of three chapters. Chapters 1 and 2 are devoted to the theory of tensors and kinematic applications, focusing on the little-known theory of non-linear tensor functions. The laws of dynamics and thermodynamics are covered in Chapter 3.This volume is suitable

Overall mass-transfer coefficients in non-linear chromatography

DEFF Research Database (Denmark)

Mollerup, Jørgen; Hansen, Ernst

1998-01-01

In case of mass transfer where concentration differences in both phases must be taken into account, one may define an over-all mass-transfer coefficient basd on the apparent over-all concentration difference. If the equilibrium relationship is linear, i.e. in cases where a Henry´s law relationshi...

A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis.

Science.gov (United States)

Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Koutentis, Panayiotis A; Markopoulos, John; Igglessi-Markopoulou, Olga

2006-08-01

A quantitative-structure activity relationship was obtained by applying Multiple Linear Regression Analysis to a series of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine (HEPT) derivatives with significant anti-HIV activity. For the selection of the best among 37 different descriptors, the Elimination Selection Stepwise Regression Method (ES-SWR) was utilized. The resulting QSAR model (R (2) (CV) = 0.8160; S (PRESS) = 0.5680) proved to be very accurate both in training and predictive stages.

Do Pre-Entry Tests Predict Competencies Required to Excel Academically in Law School?: An Empirical Investigation

Science.gov (United States)

Wamala, Robert

2016-01-01

Purpose: Prospective students of law are required to demonstrate competence in certain disciplines to attain admission to law school. The grounding in the disciplines is expected to demonstrate competencies required to excel academically in law school. The purpose of this study is to investigate the relevance of the law school admission test to…

Discovery of Hubble's Law as a Series of Type III Errors

Science.gov (United States)

Belenkiy, Ari

2015-01-01

Recently much attention has been paid to the history of the discovery of Hubble's law--the linear relation between the rate of recession of the remote galaxies and distance to them from Earth. Though historians of cosmology now mention several names associated with this law instead of just one, the motivation of each actor of that remarkable…

Is Fitts' law continuous in discrete aiming?

Directory of Open Access Journals (Sweden)

Rita Sleimen-Malkoun

Full Text Available The lawful continuous linear relation between movement time and task difficulty (i.e., index of difficulty; ID in a goal-directed rapid aiming task (Fitts' law has been recently challenged in reciprocal performance. Specifically, a discontinuity was observed at critical ID and was attributed to a transition between two distinct dynamic regimes that occurs with increasing difficulty. In the present paper, we show that such a discontinuity is also present in discrete aiming when ID is manipulated via target width (experiment 1 but not via target distance (experiment 2. Fitts' law's discontinuity appears, therefore, to be a suitable indicator of the underlying functional adaptations of the neuro-muscular-skeletal system to task properties/requirements, independently of reciprocal or discrete nature of the task. These findings open new perspectives to the study of dynamic regimes involved in discrete aiming and sensori-motor mechanisms underlying the speed-accuracy trade-off.

Order-constrained linear optimization.

Science.gov (United States)

Tidwell, Joe W; Dougherty, Michael R; Chrabaszcz, Jeffrey S; Thomas, Rick P

2017-11-01

Despite the fact that data and theories in the social, behavioural, and health sciences are often represented on an ordinal scale, there has been relatively little emphasis on modelling ordinal properties. The most common analytic framework used in psychological science is the general linear model, whose variants include ANOVA, MANOVA, and ordinary linear regression. While these methods are designed to provide the best fit to the metric properties of the data, they are not designed to maximally model ordinal properties. In this paper, we develop an order-constrained linear least-squares (OCLO) optimization algorithm that maximizes the linear least-squares fit to the data conditional on maximizing the ordinal fit based on Kendall's τ. The algorithm builds on the maximum rank correlation estimator (Han, 1987, Journal of Econometrics, 35, 303) and the general monotone model (Dougherty & Thomas, 2012, Psychological Review, 119, 321). Analyses of simulated data indicate that when modelling data that adhere to the assumptions of ordinary least squares, OCLO shows minimal bias, little increase in variance, and almost no loss in out-of-sample predictive accuracy. In contrast, under conditions in which data include a small number of extreme scores (fat-tailed distributions), OCLO shows less bias and variance, and substantially better out-of-sample predictive accuracy, even when the outliers are removed. We show that the advantages of OCLO over ordinary least squares in predicting new observations hold across a variety of scenarios in which researchers must decide to retain or eliminate extreme scores when fitting data. © 2017 The British Psychological Society.

Assessing peak aerobic capacity in Dutch law enforcement officers

Directory of Open Access Journals (Sweden)

Harriet Wittink

2015-06-01

Full Text Available Objectives: To cross-validate the existing peak rate of oxygen consumption (VO2peak prediction equations in Dutch law enforcement officers and to determine whether these prediction equations can be used to predict VO2peak for groups and in a single individual. A further objective was to report normative absolute and relative VO2peak values of a sample of law enforcement officers in the Netherlands. Material and Methods: The peak rate of oxygen consumption (ml×kg–1×min–1 was measured using a maximal incremental bicycle test in 1530 subjects, including 1068 male and 461 female police officers. Validity of the prediction equations for groups was assessed by comparing predicted VO2peak with measured VO2peak using paired t-tests. For individual differences limits of agreement (LoA were calculated. Equations were considered valid for individuals when the difference between measured and predicted VO2peak did not exceed ±1 metabolic equivalent (MET in 95% of individuals. Results: None of the equations met the validity criterion of 95% of individuals having ±1 MET difference or less than the measured value. Limits of agreement (LoAs were large in all predictions. At the individual level, none of the equations were valid predictors of VO2peak (ml×kg–1×min–1. Normative values for Dutch law enforcement officers were presented. Conclusions: Substantial differences between measured and predicted VO2peak (ml×kg–1×min–1 were found. Most tested equations were invalid predictors of VO2peak at group level and all were invalid at individual levels.

Chaos in balance: non-linear measures of postural control predict individual variations in visual illusions of motion.

Directory of Open Access Journals (Sweden)

Deborah Apthorp

Full Text Available Visually-induced illusions of self-motion (vection can be compelling for some people, but they are subject to large individual variations in strength. Do these variations depend, at least in part, on the extent to which people rely on vision to maintain their postural stability? We investigated by comparing physical posture measures to subjective vection ratings. Using a Bertec balance plate in a brightly-lit room, we measured 13 participants' excursions of the centre of foot pressure (CoP over a 60-second period with eyes open and with eyes closed during quiet stance. Subsequently, we collected vection strength ratings for large optic flow displays while seated, using both verbal ratings and online throttle measures. We also collected measures of postural sway (changes in anterior-posterior CoP in response to the same visual motion stimuli while standing on the plate. The magnitude of standing sway in response to expanding optic flow (in comparison to blank fixation periods was predictive of both verbal and throttle measures for seated vection. In addition, the ratio between eyes-open and eyes-closed CoP excursions during quiet stance (using the area of postural sway significantly predicted seated vection for both measures. Interestingly, these relationships were weaker for contracting optic flow displays, though these produced both stronger vection and more sway. Next we used a non-linear analysis (recurrence quantification analysis, RQA of the fluctuations in anterior-posterior position during quiet stance (both with eyes closed and eyes open; this was a much stronger predictor of seated vection for both expanding and contracting stimuli. Given the complex multisensory integration involved in postural control, our study adds to the growing evidence that non-linear measures drawn from complexity theory may provide a more informative measure of postural sway than the conventional linear measures.

A constitutive law for degrading bioresorbable polymers.

Science.gov (United States)

Samami, Hassan; Pan, Jingzhe

2016-06-01

This paper presents a constitutive law that predicts the changes in elastic moduli, Poisson's ratio and ultimate tensile strength of bioresorbable polymers due to biodegradation. During biodegradation, long polymer chains are cleaved by hydrolysis reaction. For semi-crystalline polymers, the chain scissions also lead to crystallisation. Treating each scission as a cavity and each new crystal as a solid inclusion, a degrading semi-crystalline polymer can be modelled as a continuum solid containing randomly distributed cavities and crystal inclusions. The effective elastic properties of a degrading polymer are calculated using existing theories for such solid and the tensile strength of the degrading polymer is predicted using scaling relations that were developed for porous materials. The theoretical model for elastic properties and the scaling law for strength form a complete constitutive relation for the degrading polymers. It is shown that the constitutive law can capture the trend of the experimental data in the literature for a range of biodegradable polymers fairly well. Copyright © 2016 Elsevier Ltd. All rights reserved.

Patient-specific non-linear finite element modelling for predicting soft organ deformation in real-time: application to non-rigid neuroimage registration.

Science.gov (United States)

Wittek, Adam; Joldes, Grand; Couton, Mathieu; Warfield, Simon K; Miller, Karol

2010-12-01

Long computation times of non-linear (i.e. accounting for geometric and material non-linearity) biomechanical models have been regarded as one of the key factors preventing application of such models in predicting organ deformation for image-guided surgery. This contribution presents real-time patient-specific computation of the deformation field within the brain for six cases of brain shift induced by craniotomy (i.e. surgical opening of the skull) using specialised non-linear finite element procedures implemented on a graphics processing unit (GPU). In contrast to commercial finite element codes that rely on an updated Lagrangian formulation and implicit integration in time domain for steady state solutions, our procedures utilise the total Lagrangian formulation with explicit time stepping and dynamic relaxation. We used patient-specific finite element meshes consisting of hexahedral and non-locking tetrahedral elements, together with realistic material properties for the brain tissue and appropriate contact conditions at the boundaries. The loading was defined by prescribing deformations on the brain surface under the craniotomy. Application of the computed deformation fields to register (i.e. align) the preoperative and intraoperative images indicated that the models very accurately predict the intraoperative deformations within the brain. For each case, computing the brain deformation field took less than 4 s using an NVIDIA Tesla C870 GPU, which is two orders of magnitude reduction in computation time in comparison to our previous study in which the brain deformation was predicted using a commercial finite element solver executed on a personal computer. Copyright © 2010 Elsevier Ltd. All rights reserved.

The Constructal Law of ``Designedness'' in Nature

Science.gov (United States)

Bejan, Adrian

2008-08-01

The laws of classical thermodynamics refer to systems as black boxes, without configuration. Nature is different: it has "designedness" everywhere and at all scales (pattern, configuration, rhythm). The generation of configuration is a phenomenon of all physics, and it is covered by the constructal law: for a finite-size flow system to persist in time (to live) it must evolve such that it provides easier access to its currents. The constructal law is predictive across the board, in inanimate, animate and human flow systems. Examples are the scaling laws of all river basins, the speeds and frequencies of all kinds of animal locomotion, and the zipfian distribution of hierarchical city sizes and numbers on the globe. The constructal law accounts for the numerous and often contradictory ad-hoc statements of self-optimization, e.g. minimization and maximization of entropy generation, minimization and maximization of flow resistance, minimization of time and cost, maximization of utility, and the axiom of uniform stresses in animal bones and botanical trees.

The development of a practical and uncomplicated predictive equation to determine liver volume from simple linear ultrasound measurements of the liver

International Nuclear Information System (INIS)

Childs, Jessie T.; Thoirs, Kerry A.; Esterman, Adrian J.

2016-01-01

This study sought to develop a practical and uncomplicated predictive equation that could accurately calculate liver volumes, using multiple simple linear ultrasound measurements combined with measurements of body size. Penalized (lasso) regression was used to develop a new model and compare it to the ultrasonic linear measurements currently used clinically. A Bland–Altman analysis showed that the large limits of agreement of the new model render it too inaccurate to be of clinical use for estimating liver volume per se, but it holds value in tracking disease progress or response to treatment over time in individuals, and is certainly substantially better as an indicator of overall liver size than the ultrasonic linear measurements currently being used clinically. - Highlights: • A new model to calculate liver volumes from simple linear ultrasound measurements. • This model was compared to the linear measurements currently used clinically. • The new model holds value in tracking disease progress or response to treatment. • This model is better as an indicator of overall liver size.

Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran.

Science.gov (United States)

Azadi, Sama; Karimi-Jashni, Ayoub

2016-02-01

Predicting the mass of solid waste generation plays an important role in integrated solid waste management plans. In this study, the performance of two predictive models, Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) was verified to predict mean Seasonal Municipal Solid Waste Generation (SMSWG) rate. The accuracy of the proposed models is illustrated through a case study of 20 cities located in Fars Province, Iran. Four performance measures, MAE, MAPE, RMSE and R were used to evaluate the performance of these models. The MLR, as a conventional model, showed poor prediction performance. On the other hand, the results indicated that the ANN model, as a non-linear model, has a higher predictive accuracy when it comes to prediction of the mean SMSWG rate. As a result, in order to develop a more cost-effective strategy for waste management in the future, the ANN model could be used to predict the mean SMSWG rate. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Spike-and-Slab Lasso Generalized Linear Models for Prediction and Associated Genes Detection.

Science.gov (United States)

Tang, Zaixiang; Shen, Yueping; Zhang, Xinyan; Yi, Nengjun

2017-01-01

Large-scale "omics" data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, there are considerable challenges in analyzing high-dimensional molecular data, including the large number of potential molecular predictors, limited number of samples, and small effect of each predictor. We propose new Bayesian hierarchical generalized linear models, called spike-and-slab lasso GLMs, for prognostic prediction and detection of associated genes using large-scale molecular data. The proposed model employs a spike-and-slab mixture double-exponential prior for coefficients that can induce weak shrinkage on large coefficients, and strong shrinkage on irrelevant coefficients. We have developed a fast and stable algorithm to fit large-scale hierarchal GLMs by incorporating expectation-maximization (EM) steps into the fast cyclic coordinate descent algorithm. The proposed approach integrates nice features of two popular methods, i.e., penalized lasso and Bayesian spike-and-slab variable selection. The performance of the proposed method is assessed via extensive simulation studies. The results show that the proposed approach can provide not only more accurate estimates of the parameters, but also better prediction. We demonstrate the proposed procedure on two cancer data sets: a well-known breast cancer data set consisting of 295 tumors, and expression data of 4919 genes; and the ovarian cancer data set from TCGA with 362 tumors, and expression data of 5336 genes. Our analyses show that the proposed procedure can generate powerful models for predicting outcomes and detecting associated genes. The methods have been implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). Copyright © 2017 by the Genetics Society of America.

Forecasting the EMU inflation rate: Linear econometric vs. non-linear computational models using genetic neural fuzzy systems

DEFF Research Database (Denmark)

Kooths, Stefan; Mitze, Timo Friedel; Ringhut, Eric

2004-01-01

This paper compares the predictive power of linear econometric and non-linear computational models for forecasting the inflation rate in the European Monetary Union (EMU). Various models of both types are developed using different monetary and real activity indicators. They are compared according...

Metode Linear Predictive Coding (LPC Pada klasifikasi Hidden Markov Model (HMM Untuk Kata Arabic pada penutur Indonesia

Directory of Open Access Journals (Sweden)

Ririn Kusumawati

2016-05-01

In the classification, using Hidden Markov Model, voice signal is analyzed and searched the maximum possible value that can be recognized. The modeling results obtained parameters are used to compare with the sound of Arabic speakers. From the test results' Classification, Hidden Markov Models with Linear Predictive Coding extraction average accuracy of 78.6% for test data sampling frequency of 8,000 Hz, 80.2% for test data sampling frequency of 22050 Hz, 79% for frequencies sampling test data at 44100 Hz.

A Comparison of Curing Process-Induced Residual Stresses and Cure Shrinkage in Micro-Scale Composite Structures with Different Constitutive Laws

Science.gov (United States)

Li, Dongna; Li, Xudong; Dai, Jianfeng; Xi, Shangbin

2018-02-01

In this paper, three kinds of constitutive laws, elastic, "cure hardening instantaneously linear elastic (CHILE)" and viscoelastic law, are used to predict curing process-induced residual stress for the thermoset polymer composites. A multi-physics coupling finite element analysis (FEA) model implementing the proposed three approaches is established in COMSOL Multiphysics-Version 4.3b. The evolution of thermo-physical properties with temperature and degree of cure (DOC), which improved the accuracy of numerical simulations, and cure shrinkage are taken into account for the three models. Subsequently, these three proposed constitutive models are implemented respectively in a 3D micro-scale composite laminate structure. Compared the differences between these three numerical results, it indicates that big error in residual stress and cure shrinkage generates by elastic model, but the results calculated by the modified CHILE model are in excellent agreement with those estimated by the viscoelastic model.

Hall magnetohydrodynamics: Conservation laws and Lyapunov stability

International Nuclear Information System (INIS)

Holm, D.D.

1987-01-01

Hall electric fields produce circulating mass flow in confined ideal-fluid plasmas. The conservation laws, Hamiltonian structure, equilibrium state relations, and Lyapunov stability conditions are presented here for ideal Hall magnetohydrodynamics (HMHD) in two and three dimensions. The approach here is to use the remarkable array of nonlinear conservation laws for HMHD that follow from its Hamiltonian structure in order to construct explicit Lyapunov functionals for the HMHD equilibrium states. In this way, the Lyapunov stability analysis provides classes of HMHD equilibria that are stable and whose linearized initial-value problems are well posed (in the sense of possessing continuous dependence on initial conditions). Several examples are discussed in both two and three dimensions

Bayesian techniques for fatigue life prediction and for inference in linear time dependent PDEs

KAUST Repository

Scavino, Marco

2016-01-08

In this talk we introduce first the main characteristics of a systematic statistical approach to model calibration, model selection and model ranking when stress-life data are drawn from a collection of records of fatigue experiments. Focusing on Bayesian prediction assessment, we consider fatigue-limit models and random fatigue-limit models under different a priori assumptions. In the second part of the talk, we present a hierarchical Bayesian technique for the inference of the coefficients of time dependent linear PDEs, under the assumption that noisy measurements are available in both the interior of a domain of interest and from boundary conditions. We present a computational technique based on the marginalization of the contribution of the boundary parameters and apply it to inverse heat conduction problems.

Multiple Linear Regression and Artificial Neural Network to Predict Blood Glucose in Overweight Patients.

Science.gov (United States)

Wang, J; Wang, F; Liu, Y; Xu, J; Lin, H; Jia, B; Zuo, W; Jiang, Y; Hu, L; Lin, F

2016-01-01

Overweight individuals are at higher risk for developing type II diabetes than the general population. We conducted this study to analyze the correlation between blood glucose and biochemical parameters, and developed a blood glucose prediction model tailored to overweight patients. A total of 346 overweight Chinese people patients ages 18-81 years were involved in this study. Their levels of fasting glucose (fs-GLU), blood lipids, and hepatic and renal functions were measured and analyzed by multiple linear regression (MLR). Based the MLR results, we developed a back propagation artificial neural network (BP-ANN) model by selecting tansig as the transfer function of the hidden layers nodes, and purelin for the output layer nodes, with training goal of 0.5×10(-5). There was significant correlation between fs-GLU with age, BMI, and blood biochemical indexes (P<0.05). The results of MLR analysis indicated that age, fasting alanine transaminase (fs-ALT), blood urea nitrogen (fs-BUN), total protein (fs-TP), uric acid (fs-BUN), and BMI are 6 independent variables related to fs-GLU. Based on these parameters, the BP-ANN model was performed well and reached high prediction accuracy when training 1 000 epoch (R=0.9987). The level of fs-GLU was predictable using the proposed BP-ANN model based on 6 related parameters (age, fs-ALT, fs-BUN, fs-TP, fs-UA and BMI) in overweight patients. © Georg Thieme Verlag KG Stuttgart · New York.

The log-linear return approximation, bubbles, and predictability

DEFF Research Database (Denmark)

Engsted, Tom; Pedersen, Thomas Quistgaard; Tanggaard, Carsten

We study in detail the log-linear return approximation introduced by Campbell and Shiller (1988a). First, we derive an upper bound for the mean approximation error, given stationarity of the log dividendprice ratio. Next, we simulate various rational bubbles which have explosive conditional expec...

A hybrid genetic algorithm and linear regression for prediction of NOx emission in power generation plant

International Nuclear Information System (INIS)

Bunyamin, Muhammad Afif; Yap, Keem Siah; Aziz, Nur Liyana Afiqah Abdul; Tiong, Sheih Kiong; Wong, Shen Yuong; Kamal, Md Fauzan

2013-01-01

This paper presents a new approach of gas emission estimation in power generation plant using a hybrid Genetic Algorithm (GA) and Linear Regression (LR) (denoted as GA-LR). The LR is one of the approaches that model the relationship between an output dependant variable, y, with one or more explanatory variables or inputs which denoted as x. It is able to estimate unknown model parameters from inputs data. On the other hand, GA is used to search for the optimal solution until specific criteria is met causing termination. These results include providing good solutions as compared to one optimal solution for complex problems. Thus, GA is widely used as feature selection. By combining the LR and GA (GA-LR), this new technique is able to select the most important input features as well as giving more accurate prediction by minimizing the prediction errors. This new technique is able to produce more consistent of gas emission estimation, which may help in reducing population to the environment. In this paper, the study's interest is focused on nitrous oxides (NOx) prediction. The results of the experiment are encouraging.

Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning

Directory of Open Access Journals (Sweden)

Shandilya Sharad

2012-10-01

.6% and 60.9%, respectively. Conclusion We report the development and first-use of a nontraditional non-linear method of analyzing the VF ECG signal, yielding high predictive accuracies of defibrillation success. Furthermore, incorporation of features from the PetCO2 signal noticeably increased model robustness. These predictive capabilities should further improve with the availability of a larger database.

The Radiometric Bode's law and Extrasolar Planets

National Research Council Canada - National Science Library

Lazio, T. J; Farrell, W. M; Dietrick, Jill; Greenlees, Elizabeth; Hogan, Emily; Jones, Christopher; Hennig, L. A

2004-01-01

We predict the radio flux densities of the extrasolar planets in the current census, making use of an empirical relation the radiometric Bode's law determined from the five "magnetic" planets in the solar system...

Power-Law Template for IR Point Source Clustering

Science.gov (United States)

Addison, Graeme E.; Dunkley, Joanna; Hajian, Amir; Viero, Marco; Bond, J. Richard; Das, Sudeep; Devlin, Mark; Halpern, Mark; Hincks, Adam; Hlozek, Renee;

Power-Law Template for Infrared Point-Source Clustering

Science.gov (United States)

Addison, Graeme E; Dunkley, Joanna; Hajian, Amir; Viero, Marco; Bond, J. Richard; Das, Sudeep; Devlin, Mark J.; Halpern, Mark; Hincks, Adam D; Hlozek, Renee;

The Log-Linear Return Approximation, Bubbles, and Predictability

DEFF Research Database (Denmark)

Engsted, Tom; Pedersen, Thomas Quistgaard; Tanggaard, Carsten

2012-01-01

We study in detail the log-linear return approximation introduced by Campbell and Shiller (1988a). First, we derive an upper bound for the mean approximation error, given stationarity of the log dividend-price ratio. Next, we simulate various rational bubbles which have explosive conditional expe...

Deformation Prediction Using Linear Polynomial Functions ...

African Journals Online (AJOL)

By Deformation, we mean change of shape of any structure from its original shape and by monitoring over time using Geodetic means, the change in shape, size and the overall structural dynamics behaviors of structure can be detected. Prediction is therefor based on the epochs measurement obtained during monitoring, ...

Discussion on Benford's Law and its Application

OpenAIRE

Li, Zhipeng; Cong, Lin; Wang, Huajia

2004-01-01

The probability that a number in many naturally occurring tables of numerical data has first significant digit $d$ is predicted by Benford's Law ${\\rm Prob} (d) = \\log_{10} (1 + {\\displaystyle{1\\over d}}), d = 1, 2 >..., 9$. Illustrations of Benford's Law from both theoretical and real-life sources on both science and social science areas are shown in detail with some novel ideas and generalizations developed solely by the authors of this paper. Three tests, Chi-Square test, total variation d...

Statistical distributions of earthquakes and related non-linear features in seismic waves

International Nuclear Information System (INIS)

Apostol, B.-F.

2006-01-01

A few basic facts in the science of the earthquakes are briefly reviewed. An accumulation, or growth, model is put forward for the focal mechanisms and the critical focal zone of the earthquakes, which relates the earthquake average recurrence time to the released seismic energy. The temporal statistical distribution for average recurrence time is introduced for earthquakes, and, on this basis, the Omori-type distribution in energy is derived, as well as the distribution in magnitude, by making use of the semi-empirical Gutenberg-Richter law relating seismic energy to earthquake magnitude. On geometric grounds, the accumulation model suggests the value r = 1/3 for the Omori parameter in the power-law of energy distribution, which leads to β = 1,17 for the coefficient in the Gutenberg-Richter recurrence law, in fair agreement with the statistical analysis of the empirical data. Making use of this value, the empirical Bath's law is discussed for the average magnitude of the aftershocks (which is 1.2 less than the magnitude of the main seismic shock), by assuming that the aftershocks are relaxation events of the seismic zone. The time distribution of the earthquakes with a fixed average recurrence time is also derived, the earthquake occurrence prediction is discussed by means of the average recurrence time and the seismicity rate, and application of this discussion to the seismic region Vrancea, Romania, is outlined. Finally, a special effect of non-linear behaviour of the seismic waves is discussed, by describing an exact solution derived recently for the elastic waves equation with cubic anharmonicities, its relevance, and its connection to the approximate quasi-plane waves picture. The properties of the seismic activity accompanying a main seismic shock, both like foreshocks and aftershocks, are relegated to forthcoming publications. (author)

Some mathematical problems in non-linear Physics

International Nuclear Information System (INIS)

1983-01-01

The main results contained in this report are the following: I) A general analysis of non-autonomous conserved densities for simple linear evolution systems. II) Partial differential systems within a wide class are converted into Lagrange an form. III) Rigorous criteria for existence of integrating factor matrices. IV) Isolation of all third-order evolution equations with high order symmetries and conservation laws. (Author) 3 refs

FPGA/NIOS Implementation of an Adaptive FIR Filter Using Linear Prediction to Reduce Narrow-Band RFI for Radio Detection of Cosmic Rays

NARCIS (Netherlands)

Szadkowski, Zbigniew; Fraenkel, E. D.; van den Berg, Ad M.

2013-01-01

We present the FPGA/NIOS implementation of an adaptive finite impulse response (FIR) filter based on linear prediction to suppress radio frequency interference (RFI). This technique will be used for experiments that observe coherent radio emission from extensive air showers induced by

Continuing the search for a fundamental law of mortality

Energy Technology Data Exchange (ETDEWEB)

Carnes, B.A.; Grahn, D. [Argonne National Lab., IL (United States); Olshansky, S.J. [Chicago Univ., IL (United States)

1996-03-01

for 170 years, scientists have attempted to explain why consistent temporal patterns of death are observed among individuals within populations. Historical efforts to identify a `law of mortality` from these patterns ended in 1935 when it was declared that such a law did not exist. These empirical tests for a law of mortality were constructed using mortality curves based on all causes of death. We predicted patterns of mortality consistent with the historical concept of a law would be revealed if mortality curves for species were constructed using only senescent causes of death. Using data on senescent mortality for laboratory animals and humans, we demonstrate patterns of mortality overlap when compared on a biologically comparable time scale. The results are consistent with the existence of a law of mortality following sexual maturity. The societal, medical, and research implications of such a law are discussed.

Model Predictive Control Based on Kalman Filter for Constrained Hammerstein-Wiener Systems

Directory of Open Access Journals (Sweden)

Man Hong

2013-01-01

Full Text Available To precisely track the reactor temperature in the entire working condition, the constrained Hammerstein-Wiener model describing nonlinear chemical processes such as in the continuous stirred tank reactor (CSTR is proposed. A predictive control algorithm based on the Kalman filter for constrained Hammerstein-Wiener systems is designed. An output feedback control law regarding the linear subsystem is derived by state observation. The size of reaction heat produced and its influence on the output are evaluated by the Kalman filter. The observation and evaluation results are calculated by the multistep predictive approach. Actual control variables are computed while considering the constraints of the optimal control problem in a finite horizon through the receding horizon. The simulation example of the CSTR tester shows the effectiveness and feasibility of the proposed algorithm.

The conservation laws for deformed classical models

International Nuclear Information System (INIS)

Klimek, M.

1994-01-01

The problem of deriving the conservation laws for deformed linear equations of motion is investigated. The conserved currents are obtained in explicit form and used in the construction of constants of motion. The equations for the set of non-interacting oscillators with arbitrary scale-time as well as the κ-Klein-Gordon equation are considered as an example of application of the method. (author) 9 refs

Another paradox involving the second law of thermodynamics

International Nuclear Information System (INIS)

Sheehan, D.P.

1996-01-01

Recently a paradox has been posed that appears to challenge the second law of thermodynamics in a plasma blackbody environment [D. P. Sheehan, Phys. Plasmas 2, 1893 (1995)]. In this paper another, related paradox is posed in an unmagnetized Q plasma. Laboratory experiments simulating some necessary conditions for the paradoxical system corroborate theoretical predictions and fail to resolve the paradox in favor of the second law. copyright 1996 American Institute of Physics

Graphical reduction of reaction networks by linear elimination of species

DEFF Research Database (Denmark)

Saez Cornellana, Meritxell; Wiuf, Carsten; Feliu, Elisenda

2017-01-01

We give a graphically based procedure to reduce a reaction network to a smaller reaction network with fewer species after linear elimination of a set of noninteracting species. We give a description of the reduced reaction network, its kinetics and conservations laws, and explore properties...

Contractive relaxation systems and interacting particles for scalar conservation laws

International Nuclear Information System (INIS)

Katsoulakis, M.A.; Tzavaras, A.E.

1996-01-01

We consider a class of semi linear hyperbolic systems with relaxation that are contractive in the L 1 -norm and admit invariant regions. We show that, as the relaxation parameter ξ goes to zero, their solutions converge to a weak solution of the scalar multidimensional conversation law that satisfies the Kruzhkov conditions. In the case of one space dimension, we propose certain interacting particle systems, whose mesoscopic limit is the systems with relaxation and their macroscopic dynamics is described by entropy solutions of a scalar conservation law. (author)

Dynamic intersectoral models with power-law memory

Science.gov (United States)

Tarasova, Valentina V.; Tarasov, Vasily E.

2018-01-01

Intersectoral dynamic models with power-law memory are proposed. The equations of open and closed intersectoral models, in which the memory effects are described by the Caputo derivatives of non-integer orders, are derived. We suggest solutions of these equations, which have the form of linear combinations of the Mittag-Leffler functions and which are characterized by different effective growth rates. Examples of intersectoral dynamics with power-law memory are suggested for two sectoral cases. We formulate two principles of intersectoral dynamics with memory: the principle of changing of technological growth rates and the principle of domination change. It has been shown that in the input-output economic dynamics the effects of fading memory can change the economic growth rate and dominant behavior of economic sectors.

Non-linear model predictive supervisory controller for building, air handling unit with recuperator and refrigeration system with heat waste recovery

DEFF Research Database (Denmark)

Minko, Tomasz; Wisniewski, Rafal; Bendtsen, Jan Dimon

2016-01-01

. The retrieved heat excess can be stored in the water tank. For this purpose the charging and the discharging water loops has been designed. We present the non-linear model of the above described system and a non-linear model predictive supervisory controller that according to the received price signal......, occupancy information and ambient temperature minimizes the operation cost of the whole system and distributes set points to local controllers of supermarkets subsystems. We find that when reliable information about the high price period is available, it is profitable to use the refrigeration system...... to generate heat during the low price period, store it and use it to substitute the conventional heater during the high price period....

Comparison between linear quadratic and early time dose models

International Nuclear Information System (INIS)

Chougule, A.A.; Supe, S.J.

1993-01-01

During the 70s, much interest was focused on fractionation in radiotherapy with the aim of improving tumor control rate without producing unacceptable normal tissue damage. To compare the radiobiological effectiveness of various fractionation schedules, empirical formulae such as Nominal Standard Dose, Time Dose Factor, Cumulative Radiation Effect and Tumour Significant Dose, were introduced and were used despite many shortcomings. It has been claimed that a recent linear quadratic model is able to predict the radiobiological responses of tumours as well as normal tissues more accurately. We compared Time Dose Factor and Tumour Significant Dose models with the linear quadratic model for tumour regression in patients with carcinomas of the cervix. It was observed that the prediction of tumour regression estimated by the Tumour Significant Dose and Time Dose factor concepts varied by 1.6% from that of the linear quadratic model prediction. In view of the lack of knowledge of the precise values of the parameters of the linear quadratic model, it should be applied with caution. One can continue to use the Time Dose Factor concept which has been in use for more than a decade as its results are within ±2% as compared to that predicted by the linear quadratic model. (author). 11 refs., 3 figs., 4 tabs

Peak thrust operation of linear induction machines from parameter identification

Energy Technology Data Exchange (ETDEWEB)

Zhang, Z.; Eastham, T.R.; Dawson, G.E. [Queen`s Univ., Kingston, Ontario (Canada). Dept. of Electrical and Computer Engineering

1995-12-31

Various control strategies are being used to achieve high performance operation of linear drives. To maintain minimum volume and weight of the power supply unit on board the transportation vehicle, peak thrust per unit current operation is a desirable objective. True peak thrust per unit current through slip control is difficult to achieve because the parameters of linear induction machines vary during normal operation. This paper first develops a peak thrust per unit current control law based on the per-phase equivalent circuit for linear induction machines. The algorithm for identification of the variable parameters in induction machines is then presented. Application to an operational linear induction machine (LIM) demonstrates the utility of this algorithm. The control strategy is then simulated, based on an operational transit LIM, to show the capability of achieving true peak thrust operation for linear induction machines.

Tikhonov theorem for linear hyperbolic systems

OpenAIRE

Tang , Ying; Prieur , Christophe; Girard , Antoine

2015-01-01

International audience; A class of linear systems of conservation laws with a small perturbation parameter is introduced. By setting the perturbation parameter to zero, two subsystems, the reduced system standing for the slow dynamics and the boundary-layer system representing the fast dynamics, are computed. It is first proved that the exponential stability of the full system implies the stability of both subsystems. Secondly, a counter example is given to indicate that the converse is not t...

Linear Interaction Energy Based Prediction of Cytochrome P450 1A2 Binding Affinities with Reliability Estimation.

Directory of Open Access Journals (Sweden)

Luigi Capoferri

Full Text Available Prediction of human Cytochrome P450 (CYP binding affinities of small ligands, i.e., substrates and inhibitors, represents an important task for predicting drug-drug interactions. A quantitative assessment of the ligand binding affinity towards different CYPs can provide an estimate of inhibitory activity or an indication of isoforms prone to interact with the substrate of inhibitors. However, the accuracy of global quantitative models for CYP substrate binding or inhibition based on traditional molecular descriptors can be limited, because of the lack of information on the structure and flexibility of the catalytic site of CYPs. Here we describe the application of a method that combines protein-ligand docking, Molecular Dynamics (MD simulations and Linear Interaction Energy (LIE theory, to allow for quantitative CYP affinity prediction. Using this combined approach, a LIE model for human CYP 1A2 was developed and evaluated, based on a structurally diverse dataset for which the estimated experimental uncertainty was 3.3 kJ mol-1. For the computed CYP 1A2 binding affinities, the model showed a root mean square error (RMSE of 4.1 kJ mol-1 and a standard error in prediction (SDEP in cross-validation of 4.3 kJ mol-1. A novel approach that includes information on both structural ligand description and protein-ligand interaction was developed for estimating the reliability of predictions, and was able to identify compounds from an external test set with a SDEP for the predicted affinities of 4.6 kJ mol-1 (corresponding to 0.8 pKi units.

Environmental law in Thuringia. Text collection with introduction. Pt. 1. Waste law, nuclear, radiation and energy law, soil protection law and land reparcelling, forestry law, fishing and hunting law

International Nuclear Information System (INIS)

Schneider, Matthias Werner

2015-01-01

The volume 1 of the collection on the Thuringian Environmental Law contains additional to a detailed introduction: - Waste management - Nuclear, radiation and energy law - Soil protection law and land reparcelling - Forestry, fishery and hunting law. [de

Enforcing conservation laws in nonequilibrium cluster perturbation theory

Science.gov (United States)

Gramsch, Christian; Potthoff, Michael

2017-05-01

Using the recently introduced time-local formulation of the nonequilibrium cluster perturbation theory (CPT), we construct a generalization of the approach such that macroscopic conservation laws are respected. This is achieved by exploiting the freedom for the choice of the starting point of the all-order perturbation theory in the intercluster hopping. The proposed conserving CPT is a self-consistent propagation scheme which respects the conservation of energy, particle number, and spin, which treats short-range correlations exactly up to the linear scale of the cluster, and which represents a mean-field-like approach on length scales beyond the cluster size. Using Green's functions, conservation laws are formulated as local constraints on the local spin-dependent particle and the doublon density. We consider them as conditional equations to self-consistently fix the time-dependent intracluster one-particle parameters. Thanks to the intrinsic causality of the CPT, this can be set up as a step-by-step time propagation scheme with a computational effort scaling linearly with the maximum propagation time and exponentially in the cluster size. As a proof of concept, we consider the dynamics of the two-dimensional, particle-hole-symmetric Hubbard model following a weak interaction quench by simply employing two-site clusters only. Conservation laws are satisfied by construction. We demonstrate that enforcing them has strong impact on the dynamics. While the doublon density is strongly oscillating within plain CPT, a monotonic relaxation is observed within the conserving CPT.

Further results on "Robust MPC using Linear Matrix Inequalities"

NARCIS (Netherlands)

Lazar, M.; Heemels, W.P.M.H.; Munoz de la Pena, D.; Alamo, T.

2008-01-01

This paper presents a novel method for designing the terminal cost and the auxiliary control law (ACL) for robust MPC of uncertain linear systems, such that ISS is a priori guaranteed for the closed-loop system. The method is based on the solution of a set of LMIs. An explicit relation is

Elastic-plastic transition: A universal law

Directory of Open Access Journals (Sweden)

Chen Zhong

2016-01-01

Full Text Available Although the initial stress-strain behavior in a tensile test is often characterized as linear elastic up to a yield stress and nonlinear plastic thereafter, the pre-yield transition region is known to exhibit significant curvature and hysteresis. Hundreds of high-precision loading-unloading-loading tensile tests were performed using 26 commercial sheet alloys exhibiting a wide range of strength, ductility and crystal structure. Analysis of the results reveals the following: 1.There is no significant linear elastic region; the proportional limit is ~0 MPa when measured with sufficient sensitivity. 2.Each of the hundreds of measured transitional stress-strain curves can be characterized by a single parameter, here called the “modulus reduction rate.”The corresponding equation captures ~80% of the observed variation, a factor of 3 to 6 better than a one-parameter linear approximation. 3.Most interestingly, the transitional behavior for all alloys follows a “Universal Law” requiring no fit parameters. The law depends only upon the strength of the material and its Young’s modulus, both of which are can be measured by independent tests or adopted from handbooks. The Universal Law captures ~90% of the variation represented by the one-parameter representation and eliminates the need for mechanical testing to implement and apply. The practical and theoretical implications of these results are discussed. The results provide a simple path to significantly improving applied constitutive models in the transitional regime. The consistency of the effect for such a wide range of metals and suggests that the origin of the behavior lies in the pile-up and relaxation of dislocation arrays.

Business Law

DEFF Research Database (Denmark)

Föh, Kennet Fischer; Mandøe, Lene; Tinten, Bjarke

Business Law is a translation of the 2nd edition of Erhvervsjura - videregående uddannelser. It is an educational textbook for the subject of business law. The textbook covers all important topic?s within business law such as the Legal System, Private International Law, Insolvency Law, Contract law......, Instruments of debt and other claims, Sale of Goods and real estate, Charges, mortgages and pledges, Guarantees, Credit agreements, Tort Law, Product liability and Insurance, Company law, Market law, Labour Law, Family Law and Law of Inheritance....

Continuing the search for a fundamental law of mortality

Energy Technology Data Exchange (ETDEWEB)

Carnes, B.A.; Grahn, D. [Argonne National Lab., IL (United States); Olshansky, S.J. [Univ. of Chicago, IL (United States)

1997-08-01

For 170 years, scientists have attempted to explain why consistent temporal patterns of death are observed among individuals within populations. Historical efforts to identify a {open_quotes}law of mortality{close_quotes} from these patterns ended in 1935 when it was declared that such a law did not exist. These empirical tests for a law of mortality were constructed using mortality curves based on all causes of death. We predicted that patterns of mortality consistent with the historical concept of a law would be revealed if mortality curves for species were constructed using only senescent causes of death. Using data on senescent mortality for laboratory animals and humans, we demonstrate that patterns of mortality overlap when compared on a biologically comparable time scale. These results are consistent with the existence of a law of mortality following sexual maturity as asserted by Benjamin Gompertz and Raymond Pearl. The societal, medical, and research implications of such a law are discussed.

Taylor's law and body size in exploited marine ecosystems.

Science.gov (United States)

Cohen, Joel E; Plank, Michael J; Law, Richard

2012-12-01

Taylor's law (TL), which states that variance in population density is related to mean density via a power law, and density-mass allometry, which states that mean density is related to body mass via a power law, are two of the most widely observed patterns in ecology. Combining these two laws predicts that the variance in density is related to body mass via a power law (variance-mass allometry). Marine size spectra are known to exhibit density-mass allometry, but variance-mass allometry has not been investigated. We show that variance and body mass in unexploited size spectrum models are related by a power law, and that this leads to TL with an exponent slightly <2. These simulated relationships are disrupted less by balanced harvesting, in which fishing effort is spread across a wide range of body sizes, than by size-at-entry fishing, in which only fish above a certain size may legally be caught.

Strong Laws of Large Numbers for Arrays of Rowwise NA and LNQD Random Variables

Directory of Open Access Journals (Sweden)

Jiangfeng Wang

2011-01-01

Full Text Available Some strong laws of large numbers and strong convergence properties for arrays of rowwise negatively associated and linearly negative quadrant dependent random variables are obtained. The results obtained not only generalize the result of Hu and Taylor to negatively associated and linearly negative quadrant dependent random variables, but also improve it.

Learning a Nonnegative Sparse Graph for Linear Regression.

Science.gov (United States)

Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung

2015-09-01

Previous graph-based semisupervised learning (G-SSL) methods have the following drawbacks: 1) they usually predefine the graph structure and then use it to perform label prediction, which cannot guarantee an overall optimum and 2) they only focus on the label prediction or the graph structure construction but are not competent in handling new samples. To this end, a novel nonnegative sparse graph (NNSG) learning method was first proposed. Then, both the label prediction and projection learning were integrated into linear regression. Finally, the linear regression and graph structure learning were unified within the same framework to overcome these two drawbacks. Therefore, a novel method, named learning a NNSG for linear regression was presented, in which the linear regression and graph learning were simultaneously performed to guarantee an overall optimum. In the learning process, the label information can be accurately propagated via the graph structure so that the linear regression can learn a discriminative projection to better fit sample labels and accurately classify new samples. An effective algorithm was designed to solve the corresponding optimization problem with fast convergence. Furthermore, NNSG provides a unified perceptiveness for a number of graph-based learning methods and linear regression methods. The experimental results showed that NNSG can obtain very high classification accuracy and greatly outperforms conventional G-SSL methods, especially some conventional graph construction methods.

LETTER TO THE EDITOR: Bicomplexes and conservation laws in non-Abelian Toda models

Science.gov (United States)

Gueuvoghlanian, E. P.

2001-08-01

A bicomplex structure is associated with the Leznov-Saveliev equation of integrable models. The linear problem associated with the zero-curvature condition is derived in terms of the bicomplex linear equation. The explicit example of a non-Abelian conformal affine Toda model is discussed in detail and its conservation laws are derived from the zero-curvature representation of its equation of motion.

Rockburst Disaster Prediction of Isolated Coal Pillar by Electromagnetic Radiation Based on Frictional Effect

Science.gov (United States)

Zhao, Tongbin; Yin, Yanchun; Xiao, Fukun; Tan, Yunliang; Zou, Jianchao

2014-01-01

Based on the understanding that charges generated during coal cracking are due to coal particle friction, a microstructure model was developed by considering four different variation laws of friction coefficient. Firstly, the frictional energy release of coal sample during uniaxial compressive tests was investigated and discussed. Then electromagnetic radiation method was used to predict the potential rockburst disaster in isolated coal pillar mining face, Muchengjian Colliery. The results indicate that the friction coefficient of coal particles decreases linearly with the increase of axial loading force. In predicting the strain-type rockburst, the high stress state of coal must be closely monitored. Field monitoring shows that electromagnetic radiation signal became abnormal before the occurrence of rockburst during isolated coal pillar mining. Furthermore, rockburst tends to occur at the early and ending stages of isolated coal pillar extraction. Mine-site investigation shows the occurrence zone of rockburst is consistent with the prediction, proving the reliability of the electromagnetic radiation method to predict strain-type rockburst disaster. PMID:25054186

Rockburst Disaster Prediction of Isolated Coal Pillar by Electromagnetic Radiation Based on Frictional Effect

Directory of Open Access Journals (Sweden)

Tongbin Zhao

2014-01-01

Full Text Available Based on the understanding that charges generated during coal cracking are due to coal particle friction, a microstructure model was developed by considering four different variation laws of friction coefficient. Firstly, the frictional energy release of coal sample during uniaxial compressive tests was investigated and discussed. Then electromagnetic radiation method was used to predict the potential rockburst disaster in isolated coal pillar mining face, Muchengjian Colliery. The results indicate that the friction coefficient of coal particles decreases linearly with the increase of axial loading force. In predicting the strain-type rockburst, the high stress state of coal must be closely monitored. Field monitoring shows that electromagnetic radiation signal became abnormal before the occurrence of rockburst during isolated coal pillar mining. Furthermore, rockburst tends to occur at the early and ending stages of isolated coal pillar extraction. Mine-site investigation shows the occurrence zone of rockburst is consistent with the prediction, proving the reliability of the electromagnetic radiation method to predict strain-type rockburst disaster.

Micromechanics of Composite Materials Governed by Vector Constitutive Laws

Science.gov (United States)

Bednarcyk, Brett A.; Aboudi, Jacob; Arnold, Steven M.

2017-01-01

The high-fidelity generalized method of cells micromechanics theory has been extended for the prediction of the effective property tensor and the corresponding local field distributions for composites whose constituents are governed by vector constitutive laws. As shown, the shear analogy, which can predict effective transverse properties, is not valid in the general three-dimensional case. Consequently, a general derivation is presented that is applicable to both continuously and discontinuously reinforced composites with arbitrary vector constitutive laws and periodic microstructures. Results are given for thermal and electric problems, effective properties and local field distributions, ordered and random microstructures, as well as complex geometries including woven composites. Comparisons of the theory's predictions are made to test data, numerical analysis, and classical expressions from the literature. Further, classical methods cannot provide the local field distributions in the composite, and it is demonstrated that, as the percolation threshold is approached, their predictions are increasingly unreliable. XXXX It has been observed that the bonding between the fibers and matrix in composite materials can be imperfect. In the context of thermal conductivity, such imperfect interfaces have been investigated in micromechanical models by Dunn and Taya (1993), Duan and Karihaloo (2007), Nan et al. (1997) and Hashin (2001). The present HFGMC micromechanical method, derived for perfectly bonded composite materials governed by vector constitutive laws, can be easily generalized to include the effects of weak bonding between the constituents. Such generalizations, in the context of the mechanical micromechanics problem, involve introduction of a traction-separation law at the fiber/matrix interface and have been presented by Aboudi (1987), Bednarcyk and Arnold (2002), Bednarcyk et al. (2004) and Aboudi et al. (2013) and will be addressed in the future.

Power-law to Power-law Mapping of Blazar Spectra from Intergalactic Absorption

International Nuclear Information System (INIS)

Stecker, F W; Scully, S T

2007-01-01

We have derived a useful analytic approximation for determining the effect of intergalactic absorption on the γ-ray spectra of TeV blazars the energy range 0.2 TeV γ γ ) is approximately logarithmic. The effect of this energy dependence is to steepen intrinsic source spectra such that a source with an approximate power-law spectral index Γ s is converted to one with an observed spectral index Γ o ≅ Γ s + ΔΓ(z) where ΔΓ(z) is a linear function of z in the redshift range 0.05-0.4. We apply this approximation to the spectra of 7 TeV blazars

Confirmation of linear system theory prediction: Rate of change of Herrnstein's κ as a function of response-force requirement

Science.gov (United States)

McDowell, J. J; Wood, Helena M.

1985-01-01

Four human subjects worked on all combinations of five variable-interval schedules and five reinforcer magnitudes (¢/reinforcer) in each of two phases of the experiment. In one phase the force requirement on the operandum was low (1 or 11 N) and in the other it was high (25 or 146 N). Estimates of Herrnstein's κ were obtained at each reinforcer magnitude. The results were: (1) response rate was more sensitive to changes in reinforcement rate at the high than at the low force requirement, (2) κ increased from the beginning to the end of the magnitude range for all subjects at both force requirements, (3) the reciprocal of κ was a linear function of the reciprocal of reinforcer magnitude for seven of the eight data sets, and (4) the rate of change of κ was greater at the high than at the low force requirement by an order of magnitude or more. The second and third findings confirm predictions made by linear system theory, and replicate the results of an earlier experiment (McDowell & Wood, 1984). The fourth finding confirms a further prediction of the theory and supports the theory's interpretation of conflicting data on the constancy of Herrnstein's κ. PMID:16812408

Confirmation of linear system theory prediction: Rate of change of Herrnstein's kappa as a function of response-force requirement.

Science.gov (United States)

McDowell, J J; Wood, H M

1985-01-01

Four human subjects worked on all combinations of five variable-interval schedules and five reinforcer magnitudes ( cent/reinforcer) in each of two phases of the experiment. In one phase the force requirement on the operandum was low (1 or 11 N) and in the other it was high (25 or 146 N). Estimates of Herrnstein's kappa were obtained at each reinforcer magnitude. The results were: (1) response rate was more sensitive to changes in reinforcement rate at the high than at the low force requirement, (2) kappa increased from the beginning to the end of the magnitude range for all subjects at both force requirements, (3) the reciprocal of kappa was a linear function of the reciprocal of reinforcer magnitude for seven of the eight data sets, and (4) the rate of change of kappa was greater at the high than at the low force requirement by an order of magnitude or more. The second and third findings confirm predictions made by linear system theory, and replicate the results of an earlier experiment (McDowell & Wood, 1984). The fourth finding confirms a further prediction of the theory and supports the theory's interpretation of conflicting data on the constancy of Herrnstein's kappa.

FEAST: a two-dimensional non-linear finite element code for calculating stresses

International Nuclear Information System (INIS)

Tayal, M.

1986-06-01

The computer code FEAST calculates stresses, strains, and displacements. The code is two-dimensional. That is, either plane or axisymmetric calculations can be done. The code models elastic, plastic, creep, and thermal strains and stresses. Cracking can also be simulated. The finite element method is used to solve equations describing the following fundamental laws of mechanics: equilibrium; compatibility; constitutive relations; yield criterion; and flow rule. FEAST combines several unique features that permit large time-steps in even severely non-linear situations. The features include a special formulation for permitting many finite elements to simultaneously cross the boundary from elastic to plastic behaviour; accomodation of large drops in yield-strength due to changes in local temperature and a three-step predictor-corrector method for plastic analyses. These features reduce computing costs. Comparisons against twenty analytical solutions and against experimental measurements show that predictions of FEAST are generally accurate to ± 5%

The use of scaling laws for the design of high beta tokamaks

International Nuclear Information System (INIS)

Mauel, M.E.

1987-01-01

Several different empirical scaling laws for the tokamak energy confinement time are used to estimate the auxiliary heating power required for a laboratory experiment capable of testing tokamak confinement at high beta and techniques to access the second stability regime. Since operating experience in the second stability regime does not yet exist, these laws predict a wide range of possible power requirements, especially at large aspect ratios. However, by examining a model DT fusion power reactor with reasonable restrictions on the fusion island weight, neutron loading, and maximum magnetic field of the external coils, only a limited range of operating conditions are found for both first and second regime tokamaks, and only a subset of the scaling laws predict ignition. These particular scaling laws are then used to set confinement goals which if demonstrated by the laboratory experiment would indicate favourable scaling to a reactor. (author)

Comparison between experimental stiffness changes and crack-thickness-dependent predictions on a 1D SiC-SiC composite

Energy Technology Data Exchange (ETDEWEB)

Morvan, J.-M. [Bordeaux-1 Univ., 33 - Talence (France). Lab. de Mecanique Physique; Baste, S. [Bordeaux-1 Univ., 33 - Talence (France). Lab. de Mecanique Physique

1997-08-01

The use of an ultrasonic device gives access to all the stiffness coefficients of materials. With the analytical expressions of the effective compliances of an anisotropic solid containing a crack system, it is possible to predict the compliances variation along a monotonous loading, the cracks being considered as slit cracks. When the crack opening displacement is taken into account, that leads to a good agreement between experimental and predicted compliances. The non linear behaviour of the 1D SiC-SiC composite is then simply described by the constitutive laws of both the various crack densities and cracks opening displacement functions. Furthermore, the comparison between experimental and predicted stiffnesses changes gives access to the schematic geometry of the cracks systems. (orig.)

The application of J integral to measure cohesive laws in materials undergoing large scale yielding

DEFF Research Database (Denmark)

Sørensen, Bent F.; Goutianos, Stergios

2015-01-01

We explore the possibility of determining cohesive laws by the J-integral approach for materials having non-linear stress-strain behaviour (e.g. polymers and composites) by the use of a DCB sandwich specimen, consisting of stiff elastic beams bonded to the non-linear test material, loaded with pure...... bending moments. For a wide range of parameters of the non-linear material, the plastic unloading during crack extension is small, resulting in J integral values (fracture resistance) that deviate maximum 15% from the work of the cohesive traction. Thus the method can be used to extract the cohesive laws...... directly from experiments without any presumption about their shape. Finally, the DCB sandwich specimen was also analysed using the I integral to quantify the overestimation of the steady-state fracture resistance obtained using the J integral based method....

Association Between Connecticut's Permit-to-Purchase Handgun Law and Homicides.

Science.gov (United States)

Rudolph, Kara E; Stuart, Elizabeth A; Vernick, Jon S; Webster, Daniel W

2015-08-01

We sought to estimate the effect of Connecticut's implementation of a handgun permit-to-purchase law in October 1995 on subsequent homicides. Using the synthetic control method, we compared Connecticut's homicide rates after the law's implementation to rates we would have expected had the law not been implemented. To estimate the counterfactual, we used longitudinal data from a weighted combination of comparison states identified based on the ability of their prelaw homicide trends and covariates to predict prelaw homicide trends in Connecticut. We estimated that the law was associated with a 40% reduction in Connecticut's firearm homicide rates during the first 10 years that the law was in place. By contrast, there was no evidence for a reduction in nonfirearm homicides. Consistent with prior research, this study demonstrated that Connecticut's handgun permit-to-purchase law was associated with a subsequent reduction in homicide rates. As would be expected if the law drove the reduction, the policy's effects were only evident for homicides committed with firearms.

On spectral scaling laws for incompressible anisotropic magnetohydrodynamic turbulence

International Nuclear Information System (INIS)

Galtier, Sebastien; Pouquet, Annick; Mangeney, Andre

2005-01-01

A heuristic model is given for anisotropic magnetohydrodynamics turbulence in the presence of a uniform external magnetic field B 0 e parallel . The model is valid for both moderate and strong B 0 and is able to describe both the strong and weak wave turbulence regimes as well as the transition between them. The main ingredient of the model is the assumption of constant ratio at all scales between the linear wave period and the nonlinear turnover time scale. Contrary to the model of critical balance introduced by Goldreich and Sridhar [Astrophys. J. 438, 763 (1995)], it is not assumed, in addition, that this ratio be equal to unity at all scales. This allows us to make use of the Iroshnikov-Kraichnan phenomenology; it is then possible to recover the widely observed anisotropic scaling law k parallel ∝k perpendicular 2/3 between parallel and perpendicular wave numbers (with reference to B 0 e parallel and to obtain for the total-energy spectrum E(k perpendicular ,k parallel )∼k perpendicular -α k parallel -β the universal prediction, 3α+2β=7. In particular, with such a prediction, the weak Alfven wave turbulence constant-flux solution is recovered and, for the first time, a possible explanation to its precursor found numerically by Galtier et al. [J. Plasma Phys. 63, 447 (2000)] is given.

International Investment Law and EU Law

DEFF Research Database (Denmark)

regional economic integration agreements, International Competition Law, International Investment Regulation, International Monetary Law, International Intellectual Property Protection and International Tax Law. In addition to the regular annual volumes, EYIEL Special Issues routinely address specific...... current topics in International Economic Law. The entry into force of the Lisbon Treaty entails sweeping changes with respect to foreign investment regulation. Most prominently, the Treaty on the Functioning of the European Union (TFEU) now contains in its Article 207 an explicit competence...... for the regulation of foreign direct investment as part of the Common Commercial Policy (CCP) chapter. With this new competence, the EU will become an important actor in the field of international investment politics and law. The new empowerment in the field of international investment law prompts a multitude...

Estimating epidemic arrival times using linear spreading theory

Science.gov (United States)

Chen, Lawrence M.; Holzer, Matt; Shapiro, Anne

2018-01-01

We study the dynamics of a spatially structured model of worldwide epidemics and formulate predictions for arrival times of the disease at any city in the network. The model is composed of a system of ordinary differential equations describing a meta-population susceptible-infected-recovered compartmental model defined on a network where each node represents a city and the edges represent the flight paths connecting cities. Making use of the linear determinacy of the system, we consider spreading speeds and arrival times in the system linearized about the unstable disease free state and compare these to arrival times in the nonlinear system. Two predictions are presented. The first is based upon expansion of the heat kernel for the linearized system. The second assumes that the dominant transmission pathway between any two cities can be approximated by a one dimensional lattice or a homogeneous tree and gives a uniform prediction for arrival times independent of the specific network features. We test these predictions on a real network describing worldwide airline traffic.

Aeroelastic scaling laws for gust load alleviation control system

Directory of Open Access Journals (Sweden)

Tang Bo

2016-02-01

Full Text Available Gust load alleviation (GLA tests are widely conducted to study the effectiveness of the control laws and methods. The physical parameters of models in these tests are aeroelastic scaled, while the scaling of GLA control system is always unreached. This paper concentrates on studying the scaling laws of GLA control system. Through theoretical demonstration, the scaling criterion of a classical PID control system has been come up and a scaling methodology is provided and verified. By adopting the scaling laws in this paper, gust response of the scaled model could be directly related to the full-scale aircraft theoretically under both open-loop and closed-loop conditions. Also, the influences of different scaling choices of an important non-dimensional parameter, the Froude number, have been studied in this paper. Furthermore for practical application, a compensating method is given when the theoretical scaled actuators or sensors cannot be obtained. Also, the scaling laws of some non-linear elements in control system such as the rate and amplitude saturations in actuator have been studied and examined by a numerical simulation.

Environmental law

International Nuclear Information System (INIS)

Bender, B.; Sparwasser, R.

1988-01-01

Environmental law is discussed exhaustively in this book. Legal and scientific fundamentals are taken into account, a systematic orientation is given, and hints for further information are presented. The book covers general environmental law, plan approval procedures, protection against nuisances, atomic law and radiation protection law, water protection law, waste management law, laws on chemical substances, conservation law. (HSCH) [de

Stretched exponentials and power laws in granular avalanching

Science.gov (United States)

Head, D. A.; Rodgers, G. J.

1999-02-01

We introduce a model for granular surface flow which exhibits both stretched exponential and power law avalanching over its parameter range. Two modes of transport are incorporated, a rolling layer consisting of individual particles and the overdamped, sliding motion of particle clusters. The crossover in behaviour observed in experiments on piles of rice is attributed to a change in the dominant mode of transport. We predict that power law avalanching will be observed whenever surface flow is dominated by clustered motion.

COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming.

Science.gov (United States)

Zhang, Huiling; Huang, Qingsheng; Bei, Zhendong; Wei, Yanjie; Floudas, Christodoulos A

2016-03-01

In this article, we present COMSAT, a hybrid framework for residue contact prediction of transmembrane (TM) proteins, integrating a support vector machine (SVM) method and a mixed integer linear programming (MILP) method. COMSAT consists of two modules: COMSAT_SVM which is trained mainly on position-specific scoring matrix features, and COMSAT_MILP which is an ab initio method based on optimization models. Contacts predicted by the SVM model are ranked by SVM confidence scores, and a threshold is trained to improve the reliability of the predicted contacts. For TM proteins with no contacts above the threshold, COMSAT_MILP is used. The proposed hybrid contact prediction scheme was tested on two independent TM protein sets based on the contact definition of 14 Å between Cα-Cα atoms. First, using a rigorous leave-one-protein-out cross validation on the training set of 90 TM proteins, an accuracy of 66.8%, a coverage of 12.3%, a specificity of 99.3% and a Matthews' correlation coefficient (MCC) of 0.184 were obtained for residue pairs that are at least six amino acids apart. Second, when tested on a test set of 87 TM proteins, the proposed method showed a prediction accuracy of 64.5%, a coverage of 5.3%, a specificity of 99.4% and a MCC of 0.106. COMSAT shows satisfactory results when compared with 12 other state-of-the-art predictors, and is more robust in terms of prediction accuracy as the length and complexity of TM protein increase. COMSAT is freely accessible at http://hpcc.siat.ac.cn/COMSAT/. © 2016 Wiley Periodicals, Inc.

Virtual Estimator for Piecewise Linear Systems Based on Observability Analysis

Science.gov (United States)

Morales-Morales, Cornelio; Adam-Medina, Manuel; Cervantes, Ilse; Vela-Valdés and, Luis G.; García Beltrán, Carlos Daniel

2013-01-01

This article proposes a virtual sensor for piecewise linear systems based on observability analysis that is in function of a commutation law related with the system's outpu. This virtual sensor is also known as a state estimator. Besides, it presents a detector of active mode when the commutation sequences of each linear subsystem are arbitrary and unknown. For the previous, this article proposes a set of virtual estimators that discern the commutation paths of the system and allow estimating their output. In this work a methodology in order to test the observability for piecewise linear systems with discrete time is proposed. An academic example is presented to show the obtained results. PMID:23447007

Contact angles on a soft solid: from Young's law to Neumann's law.

Science.gov (United States)

Marchand, Antonin; Das, Siddhartha; Snoeijer, Jacco H; Andreotti, Bruno

2012-12-07

The contact angle that a liquid drop makes on a soft substrate does not obey the classical Young's relation, since the solid is deformed elastically by the action of the capillary forces. The finite elasticity of the solid also renders the contact angles differently from those predicted by Neumann's law, which applies when the drop is floating on another liquid. Here, we derive an elastocapillary model for contact angles on a soft solid by coupling a mean-field model for the molecular interactions to elasticity. We demonstrate that the limit of a vanishing elastic modulus yields Neumann's law or a variation thereof, depending on the force transmission in the solid surface layer. The change in contact angle from the rigid limit to the soft limit appears when the length scale defined by the ratio of surface tension to elastic modulus γ/E reaches the range of molecular interactions.

The superspace-translation tensor and linearized N = 1 supergravities

International Nuclear Information System (INIS)

Bedding, S.P.; Lang, W.

1982-01-01

The recently proposed superspace-translation tensor is considered as the source of supergravities in the context of N = 1 supersymmetry. It is shown how the structure of this tensor leads to a complete evaluation of the linearized supervielbein in terms of unconstrained prepotentials with derived transformation laws. Connection with formulations using torsion constraints is made. (orig.)

Comparison of multiple linear regression, partial least squares and artificial neural networks for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids.

Science.gov (United States)

Fragkaki, A G; Farmaki, E; Thomaidis, N; Tsantili-Kakoulidou, A; Angelis, Y S; Koupparis, M; Georgakopoulos, C

2012-09-21

The comparison among different modelling techniques, such as multiple linear regression, partial least squares and artificial neural networks, has been performed in order to construct and evaluate models for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids. The performance of the quantitative structure-retention relationship study, using the multiple linear regression and partial least squares techniques, has been previously conducted. In the present study, artificial neural networks models were constructed and used for the prediction of relative retention times of anabolic androgenic steroids, while their efficiency is compared with that of the models derived from the multiple linear regression and partial least squares techniques. For overall ranking of the models, a novel procedure [Trends Anal. Chem. 29 (2010) 101-109] based on sum of ranking differences was applied, which permits the best model to be selected. The suggested models are considered useful for the estimation of relative retention times of designer steroids for which no analytical data are available. Copyright © 2012 Elsevier B.V. All rights reserved.

Afrika Statistika ISSN 2316-090X Further properties of linear ...

African Journals Online (AJOL)

properties of linear prediction sufficiency and the BLUPs in the linear model with new observations. ...... a wide range of applications, for example, plant variety trials, animal breeding, selection ..... Linear Algebra Appl., 430, 2622–2641. DOI.

Structure-Dependent Water-Induced Linear Reduction Model for Predicting Gas Diffusivity and Tortuosity in Repacked and Intact Soil

DEFF Research Database (Denmark)

Møldrup, Per; Chamindu, T. K. K. Deepagoda; Hamamoto, S.

2013-01-01

The soil-gas diffusion is a primary driver of transport, reactions, emissions, and uptake of vadose zone gases, including oxygen, greenhouse gases, fumigants, and spilled volatile organics. The soil-gas diffusion coefficient, Dp, depends not only on soil moisture content, texture, and compaction...... but also on the local-scale variability of these. Different predictive models have been developed to estimate Dp in intact and repacked soil, but clear guidelines for model choice at a given soil state are lacking. In this study, the water-induced linear reduction (WLR) model for repacked soil is made...... air) in repacked soils containing between 0 and 54% clay. With Cm = 2.1, the SWLR model on average gave excellent predictions for 290 intact soils, performing well across soil depths, textures, and compactions (dry bulk densities). The SWLR model generally outperformed similar, simple Dp/Do models...

Exploiting the atmosphere's memory for monthly, seasonal and interannual temperature forecasting using Scaling LInear Macroweather Model (SLIMM)

Science.gov (United States)

Del Rio Amador, Lenin; Lovejoy, Shaun

2016-04-01

Traditionally, most of the models for prediction of the atmosphere behavior in the macroweather and climate regimes follow a deterministic approach. However, modern ensemble forecasting systems using stochastic parameterizations are in fact deterministic/ stochastic hybrids that combine both elements to yield a statistical distribution of future atmospheric states. Nevertheless, the result is both highly complex (both numerically and theoretically) as well as being theoretically eclectic. In principle, it should be advantageous to exploit higher level turbulence type scaling laws. Concretely, in the case for the Global Circulation Models (GCM's), due to sensitive dependence on initial conditions, there is a deterministic predictability limit of the order of 10 days. When these models are coupled with ocean, cryosphere and other process models to make long range, climate forecasts, the high frequency "weather" is treated as a driving noise in the integration of the modelling equations. Following Hasselman, 1976, this has led to stochastic models that directly generate the noise, and model the low frequencies using systems of integer ordered linear ordinary differential equations, the most well-known are the Linear Inverse Models (LIM). For annual global scale forecasts, they are somewhat superior to the GCM's and have been presented as a benchmark for surface temperature forecasts with horizons up to decades. A key limitation for the LIM approach is that it assumes that the temperature has only short range (exponential) decorrelations. In contrast, an increasing body of evidence shows that - as with the models - the atmosphere respects a scale invariance symmetry leading to power laws with potentially enormous memories so that LIM greatly underestimates the memory of the system. In this talk we show that, due to the relatively low macroweather intermittency, the simplest scaling models - fractional Gaussian noise - can be used for making greatly improved forecasts

The non-linear power spectrum of the Lyman alpha forest

International Nuclear Information System (INIS)

Arinyo-i-Prats, Andreu; Miralda-Escudé, Jordi; Viel, Matteo; Cen, Renyue

2015-01-01

The Lyman alpha forest power spectrum has been measured on large scales by the BOSS survey in SDSS-III at z∼ 2.3, has been shown to agree well with linear theory predictions, and has provided the first measurement of Baryon Acoustic Oscillations at this redshift. However, the power at small scales, affected by non-linearities, has not been well examined so far. We present results from a variety of hydrodynamic simulations to predict the redshift space non-linear power spectrum of the Lyα transmission for several models, testing the dependence on resolution and box size. A new fitting formula is introduced to facilitate the comparison of our simulation results with observations and other simulations. The non-linear power spectrum has a generic shape determined by a transition scale from linear to non-linear anisotropy, and a Jeans scale below which the power drops rapidly. In addition, we predict the two linear bias factors of the Lyα forest and provide a better physical interpretation of their values and redshift evolution. The dependence of these bias factors and the non-linear power on the amplitude and slope of the primordial fluctuations power spectrum, the temperature-density relation of the intergalactic medium, and the mean Lyα transmission, as well as the redshift evolution, is investigated and discussed in detail. A preliminary comparison to the observations shows that the predicted redshift distortion parameter is in good agreement with the recent determination of Blomqvist et al., but the density bias factor is lower than observed. We make all our results publicly available in the form of tables of the non-linear power spectrum that is directly obtained from all our simulations, and parameters of our fitting formula