Computer-based personality judgments are more accurate than those made by humans

PubMed Central

Youyou, Wu; Kosinski, Michal; Stillwell, David

2015-01-01

Judging others’ personalities is an essential skill in successful social living, as personality is a key driver behind people’s interactions, behaviors, and emotions. Although accurate personality judgments stem from social-cognitive skills, developments in machine learning show that computer models can also make valid judgments. This study compares the accuracy of human and computer-based personality judgments, using a sample of 86,220 volunteers who completed a 100-item personality questionnaire. We show that (i) computer predictions based on a generic digital footprint (Facebook Likes) are more accurate (r = 0.56) than those made by the participants’ Facebook friends using a personality questionnaire (r = 0.49); (ii) computer models show higher interjudge agreement; and (iii) computer personality judgments have higher external validity when predicting life outcomes such as substance use, political attitudes, and physical health; for some outcomes, they even outperform the self-rated personality scores. Computers outpacing humans in personality judgment presents significant opportunities and challenges in the areas of psychological assessment, marketing, and privacy. PMID:25583507

Computer-based personality judgments are more accurate than those made by humans.

PubMed

Youyou, Wu; Kosinski, Michal; Stillwell, David

2015-01-27

Judging others' personalities is an essential skill in successful social living, as personality is a key driver behind people's interactions, behaviors, and emotions. Although accurate personality judgments stem from social-cognitive skills, developments in machine learning show that computer models can also make valid judgments. This study compares the accuracy of human and computer-based personality judgments, using a sample of 86,220 volunteers who completed a 100-item personality questionnaire. We show that (i) computer predictions based on a generic digital footprint (Facebook Likes) are more accurate (r = 0.56) than those made by the participants' Facebook friends using a personality questionnaire (r = 0.49); (ii) computer models show higher interjudge agreement; and (iii) computer personality judgments have higher external validity when predicting life outcomes such as substance use, political attitudes, and physical health; for some outcomes, they even outperform the self-rated personality scores. Computers outpacing humans in personality judgment presents significant opportunities and challenges in the areas of psychological assessment, marketing, and privacy.

Non-invasive brain stimulation and computational models in post-stroke aphasic patients: single session of transcranial magnetic stimulation and transcranial direct current stimulation. A randomized clinical trial.

PubMed

Santos, Michele Devido Dos; Cavenaghi, Vitor Breseghello; Mac-Kay, Ana Paula Machado Goyano; Serafim, Vitor; Venturi, Alexandre; Truong, Dennis Quangvinh; Huang, Yu; Boggio, Paulo Sérgio; Fregni, Felipe; Simis, Marcel; Bikson, Marom; Gagliardi, Rubens José

2017-01-01

Patients undergoing the same neuromodulation protocol may present different responses. Computational models may help in understanding such differences. The aims of this study were, firstly, to compare the performance of aphasic patients in naming tasks before and after one session of transcranial direct current stimulation (tDCS), transcranial magnetic stimulation (TMS) and sham, and analyze the results between these neuromodulation techniques; and secondly, through computational model on the cortex and surrounding tissues, to assess current flow distribution and responses among patients who received tDCS and presented different levels of results from naming tasks. Prospective, descriptive, qualitative and quantitative, double blind, randomized and placebo-controlled study conducted at Faculdade de Ciências Médicas da Santa Casa de São Paulo. Patients with aphasia received one session of tDCS, TMS or sham stimulation. The time taken to name pictures and the response time were evaluated before and after neuromodulation. Selected patients from the first intervention underwent a computational model stimulation procedure that simulated tDCS. The results did not indicate any statistically significant differences from before to after the stimulation.The computational models showed different current flow distributions. The present study did not show any statistically significant difference between tDCS, TMS and sham stimulation regarding naming tasks. The patients'responses to the computational model showed different patterns of current distribution.

BIOCOMPUTATION: some history and prospects.

PubMed

Cull, Paul

2013-06-01

At first glance, biology and computer science are diametrically opposed sciences. Biology deals with carbon based life forms shaped by evolution and natural selection. Computer Science deals with electronic machines designed by engineers and guided by mathematical algorithms. In this brief paper, we review biologically inspired computing. We discuss several models of computation which have arisen from various biological studies. We show what these have in common, and conjecture how biology can still suggest answers and models for the next generation of computing problems. We discuss computation and argue that these biologically inspired models do not extend the theoretical limits on computation. We suggest that, in practice, biological models may give more succinct representations of various problems, and we mention a few cases in which biological models have proved useful. We also discuss the reciprocal impact of computer science on biology and cite a few significant contributions to biological science. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Coupling of EIT with computational lung modeling for predicting patient-specific ventilatory responses.

PubMed

Roth, Christian J; Becher, Tobias; Frerichs, Inéz; Weiler, Norbert; Wall, Wolfgang A

2017-04-01

Providing optimal personalized mechanical ventilation for patients with acute or chronic respiratory failure is still a challenge within a clinical setting for each case anew. In this article, we integrate electrical impedance tomography (EIT) monitoring into a powerful patient-specific computational lung model to create an approach for personalizing protective ventilatory treatment. The underlying computational lung model is based on a single computed tomography scan and able to predict global airflow quantities, as well as local tissue aeration and strains for any ventilation maneuver. For validation, a novel "virtual EIT" module is added to our computational lung model, allowing to simulate EIT images based on the patient's thorax geometry and the results of our numerically predicted tissue aeration. Clinically measured EIT images are not used to calibrate the computational model. Thus they provide an independent method to validate the computational predictions at high temporal resolution. The performance of this coupling approach has been tested in an example patient with acute respiratory distress syndrome. The method shows good agreement between computationally predicted and clinically measured airflow data and EIT images. These results imply that the proposed framework can be used for numerical prediction of patient-specific responses to certain therapeutic measures before applying them to an actual patient. In the long run, definition of patient-specific optimal ventilation protocols might be assisted by computational modeling. NEW & NOTEWORTHY In this work, we present a patient-specific computational lung model that is able to predict global and local ventilatory quantities for a given patient and any selected ventilation protocol. For the first time, such a predictive lung model is equipped with a virtual electrical impedance tomography module allowing real-time validation of the computed results with the patient measurements. First promising results obtained in an acute respiratory distress syndrome patient show the potential of this approach for personalized computationally guided optimization of mechanical ventilation in future. Copyright © 2017 the American Physiological Society.

A parallel computational model for GATE simulations.

PubMed

Rannou, F R; Vega-Acevedo, N; El Bitar, Z

2013-12-01

GATE/Geant4 Monte Carlo simulations are computationally demanding applications, requiring thousands of processor hours to produce realistic results. The classical strategy of distributing the simulation of individual events does not apply efficiently for Positron Emission Tomography (PET) experiments, because it requires a centralized coincidence processing and large communication overheads. We propose a parallel computational model for GATE that handles event generation and coincidence processing in a simple and efficient way by decentralizing event generation and processing but maintaining a centralized event and time coordinator. The model is implemented with the inclusion of a new set of factory classes that can run the same executable in sequential or parallel mode. A Mann-Whitney test shows that the output produced by this parallel model in terms of number of tallies is equivalent (but not equal) to its sequential counterpart. Computational performance evaluation shows that the software is scalable and well balanced. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

AMITIS: A 3D GPU-Based Hybrid-PIC Model for Space and Plasma Physics

NASA Astrophysics Data System (ADS)

Fatemi, Shahab; Poppe, Andrew R.; Delory, Gregory T.; Farrell, William M.

2017-05-01

We have developed, for the first time, an advanced modeling infrastructure in space simulations (AMITIS) with an embedded three-dimensional self-consistent grid-based hybrid model of plasma (kinetic ions and fluid electrons) that runs entirely on graphics processing units (GPUs). The model uses NVIDIA GPUs and their associated parallel computing platform, CUDA, developed for general purpose processing on GPUs. The model uses a single CPU-GPU pair, where the CPU transfers data between the system and GPU memory, executes CUDA kernels, and writes simulation outputs on the disk. All computations, including moving particles, calculating macroscopic properties of particles on a grid, and solving hybrid model equations are processed on a single GPU. We explain various computing kernels within AMITIS and compare their performance with an already existing well-tested hybrid model of plasma that runs in parallel using multi-CPU platforms. We show that AMITIS runs ∼10 times faster than the parallel CPU-based hybrid model. We also introduce an implicit solver for computation of Faraday’s Equation, resulting in an explicit-implicit scheme for the hybrid model equation. We show that the proposed scheme is stable and accurate. We examine the AMITIS energy conservation and show that the energy is conserved with an error < 0.2% after 500,000 timesteps, even when a very low number of particles per cell is used.

Computing relative plate velocities: a primer

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

Bevis, M.

1987-08-01

Standard models of present-day plate motions are framed in terms of rates and poles of rotation, in accordance with the well-known theorem due to Euler. This article shows how computation of relative plate velocities from such models can be viewed as a simple problem in spherical trigonometry. A FORTRAN subroutine is provided to perform the necessary computations.

Computational complexity of symbolic dynamics at the onset of chaos

NASA Astrophysics Data System (ADS)

Lakdawala, Porus

1996-05-01

In a variety of studies of dynamical systems, the edge of order and chaos has been singled out as a region of complexity. It was suggested by Wolfram, on the basis of qualitative behavior of cellular automata, that the computational basis for modeling this region is the universal Turing machine. In this paper, following a suggestion of Crutchfield, we try to show that the Turing machine model may often be too powerful as a computational model to describe the boundary of order and chaos. In particular we study the region of the first accumulation of period doubling in unimodal and bimodal maps of the interval, from the point of view of language theory. We show that in relation to the ``extended'' Chomsky hierarchy, the relevant computational model in the unimodal case is the nested stack automaton or the related indexed languages, while the bimodal case is modeled by the linear bounded automaton or the related context-sensitive languages.

Investigations for Supersonic Transports at Transonic and Supersonic Conditions

NASA Technical Reports Server (NTRS)

Rivers, S. Melissa B.; Owens, Lewis R.; Wahls, Richard A.

2007-01-01

Several computational studies were conducted as part of NASA s High Speed Research Program. Results of turbulence model comparisons from two studies on supersonic transport configurations performed during the NASA High-Speed Research program are given. The effects of grid topology and the representation of the actual wind tunnel model geometry are also investigated. Results are presented for both transonic conditions at Mach 0.90 and supersonic conditions at Mach 2.48. A feature of these two studies was the availability of higher Reynolds number wind tunnel data with which to compare the computational results. The transonic wind tunnel data was obtained in the National Transonic Facility at NASA Langley, and the supersonic data was obtained in the Boeing Polysonic Wind Tunnel. The computational data was acquired using a state of the art Navier-Stokes flow solver with a wide range of turbulence models implemented. The results show that the computed forces compare reasonably well with the experimental data, with the Baldwin-Lomax with Degani-Schiff modifications and the Baldwin-Barth models showing the best agreement for the transonic conditions and the Spalart-Allmaras model showing the best agreement for the supersonic conditions. The transonic results were more sensitive to the choice of turbulence model than were the supersonic results.

New insights into faster computation of uncertainties

NASA Astrophysics Data System (ADS)

Bhattacharya, Atreyee

2012-11-01

Heavy computation power, lengthy simulations, and an exhaustive number of model runs—often these seem like the only statistical tools that scientists have at their disposal when computing uncertainties associated with predictions, particularly in cases of environmental processes such as groundwater movement. However, calculation of uncertainties need not be as lengthy, a new study shows. Comparing two approaches—the classical Bayesian “credible interval” and a less commonly used regression-based “confidence interval” method—Lu et al. show that for many practical purposes both methods provide similar estimates of uncertainties. The advantage of the regression method is that it demands 10-1000 model runs, whereas the classical Bayesian approach requires 10,000 to millions of model runs.

Highly fault-tolerant parallel computation

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

Spielman, D.A.

We re-introduce the coded model of fault-tolerant computation in which the input and output of a computational device are treated as words in an error-correcting code. A computational device correctly computes a function in the coded model if its input and output, once decoded, are a valid input and output of the function. In the coded model, it is reasonable to hope to simulate all computational devices by devices whose size is greater by a constant factor but which are exponentially reliable even if each of their components can fail with some constant probability. We consider fine-grained parallel computations inmore » which each processor has a constant probability of producing the wrong output at each time step. We show that any parallel computation that runs for time t on w processors can be performed reliably on a faulty machine in the coded model using w log{sup O(l)} w processors and time t log{sup O(l)} w. The failure probability of the computation will be at most t {center_dot} exp(-w{sup 1/4}). The codes used to communicate with our fault-tolerant machines are generalized Reed-Solomon codes and can thus be encoded and decoded in O(n log{sup O(1)} n) sequential time and are independent of the machine they are used to communicate with. We also show how coded computation can be used to self-correct many linear functions in parallel with arbitrarily small overhead.« less

The super-Turing computational power of plastic recurrent neural networks.

PubMed

Cabessa, Jérémie; Siegelmann, Hava T

2014-12-01

We study the computational capabilities of a biologically inspired neural model where the synaptic weights, the connectivity pattern, and the number of neurons can evolve over time rather than stay static. Our study focuses on the mere concept of plasticity of the model so that the nature of the updates is assumed to be not constrained. In this context, we show that the so-called plastic recurrent neural networks (RNNs) are capable of the precise super-Turing computational power--as the static analog neural networks--irrespective of whether their synaptic weights are modeled by rational or real numbers, and moreover, irrespective of whether their patterns of plasticity are restricted to bi-valued updates or expressed by any other more general form of updating. Consequently, the incorporation of only bi-valued plastic capabilities in a basic model of RNNs suffices to break the Turing barrier and achieve the super-Turing level of computation. The consideration of more general mechanisms of architectural plasticity or of real synaptic weights does not further increase the capabilities of the networks. These results support the claim that the general mechanism of plasticity is crucially involved in the computational and dynamical capabilities of biological neural networks. They further show that the super-Turing level of computation reflects in a suitable way the capabilities of brain-like models of computation.

A COMPUTER MODELING STUDY OF BINDING PROPERTIES OF CHIRAL NUCLEOPEPTIDE FOR BIOMEDICAL APPLICATIONS.

PubMed

Pirtskhalava, M; Egoyan, A; Mirtskhulava, M; Roviello, G

2017-12-01

Nucleopeptides often show interesting properties of molecular binding that render them good candidates for development of innovative drugs for anticancer and antiviral therapies. In this work we present results of computer modeling of interactions between the molecules of hexathymine nucleopeptide (T6) and poly rA RNA (A18). The results of geometry optimization calculated using Hyperchem software and our own computer program for molecular docking show that molecules establish stable complexes due to the complementary-nucleobase interaction and the electrostatic interaction between the negative phosphate group of poly rA and the positively-charged residues present in the cationic nucleopeptide structure. Computer modeling makes it possible to find the optimal binding configuration of the molecules of a nucleopeptide and poly rA RNA and to estimate the binding energy between the molecules.

Objectively Determining the Educational Potential of Computer and Video-Based Courseware; or, Producing Reliable Evaluations Despite the Dog and Pony Show.

ERIC Educational Resources Information Center

Barrett, Andrew J.; And Others

The Center for Interactive Technology, Applications, and Research at the College of Engineering of the University of South Florida (Tampa) has developed objective and descriptive evaluation models to assist in determining the educational potential of computer and video courseware. The computer-based courseware evaluation model and the video-based…

Combat Simulation Using Breach Computer Language

DTIC Science & Technology

1979-09-01

simulation and weapon system analysis computer language Two types of models were constructed: a stochastic duel and a dynamic engagement model The... duel model validates the BREACH approach by comparing results with mathematical solutions. The dynamic model shows the capability of the BREACH...BREACH 2 Background 2 The Language 3 Static Duel 4 Background and Methodology 4 Validation 5 Results 8 Tank Duel Simulation 8 Dynamic Assault Model

The study of human venous system dynamics using hybrid computer modeling

NASA Technical Reports Server (NTRS)

Snyder, M. F.; Rideout, V. C.

1972-01-01

A computer-based model of the cardiovascular system was created emphasizing effects on the systemic venous system. Certain physiological aspects were emphasized: effects of heart rate, tilting, changes in respiration, and leg muscular contractions. The results from the model showed close correlation with findings previously reported in the literature.

Validation of a computational knee joint model using an alignment method for the knee laxity test and computed tomography.

PubMed

Kang, Kyoung-Tak; Kim, Sung-Hwan; Son, Juhyun; Lee, Young Han; Koh, Yong-Gon

2017-01-01

Computational models have been identified as efficient techniques in the clinical decision-making process. However, computational model was validated using published data in most previous studies, and the kinematic validation of such models still remains a challenge. Recently, studies using medical imaging have provided a more accurate visualization of knee joint kinematics. The purpose of the present study was to perform kinematic validation for the subject-specific computational knee joint model by comparison with subject's medical imaging under identical laxity condition. The laxity test was applied to the anterior-posterior drawer under 90° flexion and the varus-valgus under 20° flexion with a series of stress radiographs, a Telos device, and computed tomography. The loading condition in the computational subject-specific knee joint model was identical to the laxity test condition in the medical image. Our computational model showed knee laxity kinematic trends that were consistent with the computed tomography images, except for negligible differences because of the indirect application of the subject's in vivo material properties. Medical imaging based on computed tomography with the laxity test allowed us to measure not only the precise translation but also the rotation of the knee joint. This methodology will be beneficial in the validation of laxity tests for subject- or patient-specific computational models.

Turbulence Model Comparisons for Supersonic Transports at Transonic and Supersonic Conditions

NASA Technical Reports Server (NTRS)

Rivers, S. M. B.; Wahls, R. A.

2003-01-01

Results of turbulence model comparisons from two studies on supersonic transport configurations performed during the NASA High-speed Research program are given. Results are presented for both transonic conditions at Mach 0.90 and supersonic conditions at Mach 2.48. A feature of these two studies was the availability of higher Reynolds number wind tunnel data with which to compare the computational results. The transonic wind tunnel data was obtained in the National Transonic Facility at NASA Langley, and the supersonic data was obtained in the Boeing Polysonic Wind Tunnel. The computational data was acquired using a state of the art Navier-Stokes flow solver with a wide range of turbulence models implemented. The results show that the computed forces compare reasonably well with the experimental data, with the Baldwin- Lomax with Degani-Schiff modifications and the Baldwin-Barth models showing the best agreement for the transonic conditions and the Spalart-Allmaras model showing the best agreement for the supersonic conditions. The transonic results were more sensitive to the choice of turbulence model than were the supersonic results.

Molecular Sticker Model Stimulation on Silicon for a Maximum Clique Problem

PubMed Central

Ning, Jianguo; Li, Yanmei; Yu, Wen

2015-01-01

Molecular computers (also called DNA computers), as an alternative to traditional electronic computers, are smaller in size but more energy efficient, and have massive parallel processing capacity. However, DNA computers may not outperform electronic computers owing to their higher error rates and some limitations of the biological laboratory. The stickers model, as a typical DNA-based computer, is computationally complete and universal, and can be viewed as a bit-vertically operating machine. This makes it attractive for silicon implementation. Inspired by the information processing method on the stickers computer, we propose a novel parallel computing model called DEM (DNA Electronic Computing Model) on System-on-a-Programmable-Chip (SOPC) architecture. Except for the significant difference in the computing medium—transistor chips rather than bio-molecules—the DEM works similarly to DNA computers in immense parallel information processing. Additionally, a plasma display panel (PDP) is used to show the change of solutions, and helps us directly see the distribution of assignments. The feasibility of the DEM is tested by applying it to compute a maximum clique problem (MCP) with eight vertices. Owing to the limited computing sources on SOPC architecture, the DEM could solve moderate-size problems in polynomial time. PMID:26075867

Evidence Accumulation and Change Rate Inference in Dynamic Environments.

PubMed

Radillo, Adrian E; Veliz-Cuba, Alan; Josić, Krešimir; Kilpatrick, Zachary P

2017-06-01

In a constantly changing world, animals must account for environmental volatility when making decisions. To appropriately discount older, irrelevant information, they need to learn the rate at which the environment changes. We develop an ideal observer model capable of inferring the present state of the environment along with its rate of change. Key to this computation is an update of the posterior probability of all possible change point counts. This computation can be challenging, as the number of possibilities grows rapidly with time. However, we show how the computations can be simplified in the continuum limit by a moment closure approximation. The resulting low-dimensional system can be used to infer the environmental state and change rate with accuracy comparable to the ideal observer. The approximate computations can be performed by a neural network model via a rate-correlation-based plasticity rule. We thus show how optimal observers accumulate evidence in changing environments and map this computation to reduced models that perform inference using plausible neural mechanisms.

Simple, efficient allocation of modelling runs on heterogeneous clusters with MPI

USGS Publications Warehouse

Donato, David I.

2017-01-01

In scientific modelling and computation, the choice of an appropriate method for allocating tasks for parallel processing depends on the computational setting and on the nature of the computation. The allocation of independent but similar computational tasks, such as modelling runs or Monte Carlo trials, among the nodes of a heterogeneous computational cluster is a special case that has not been specifically evaluated previously. A simulation study shows that a method of on-demand (that is, worker-initiated) pulling from a bag of tasks in this case leads to reliably short makespans for computational jobs despite heterogeneity both within and between cluster nodes. A simple reference implementation in the C programming language with the Message Passing Interface (MPI) is provided.

Studying an Eulerian Computer Model on Different High-performance Computer Platforms and Some Applications

NASA Astrophysics Data System (ADS)

Georgiev, K.; Zlatev, Z.

2010-11-01

The Danish Eulerian Model (DEM) is an Eulerian model for studying the transport of air pollutants on large scale. Originally, the model was developed at the National Environmental Research Institute of Denmark. The model computational domain covers Europe and some neighbour parts belong to the Atlantic Ocean, Asia and Africa. If DEM model is to be applied by using fine grids, then its discretization leads to a huge computational problem. This implies that such a model as DEM must be run only on high-performance computer architectures. The implementation and tuning of such a complex large-scale model on each different computer is a non-trivial task. Here, some comparison results of running of this model on different kind of vector (CRAY C92A, Fujitsu, etc.), parallel computers with distributed memory (IBM SP, CRAY T3E, Beowulf clusters, Macintosh G4 clusters, etc.), parallel computers with shared memory (SGI Origin, SUN, etc.) and parallel computers with two levels of parallelism (IBM SMP, IBM BlueGene/P, clusters of multiprocessor nodes, etc.) will be presented. The main idea in the parallel version of DEM is domain partitioning approach. Discussions according to the effective use of the cache and hierarchical memories of the modern computers as well as the performance, speed-ups and efficiency achieved will be done. The parallel code of DEM, created by using MPI standard library, appears to be highly portable and shows good efficiency and scalability on different kind of vector and parallel computers. Some important applications of the computer model output are presented in short.

Role of Statistical Random-Effects Linear Models in Personalized Medicine.

PubMed

Diaz, Francisco J; Yeh, Hung-Wen; de Leon, Jose

2012-03-01

Some empirical studies and recent developments in pharmacokinetic theory suggest that statistical random-effects linear models are valuable tools that allow describing simultaneously patient populations as a whole and patients as individuals. This remarkable characteristic indicates that these models may be useful in the development of personalized medicine, which aims at finding treatment regimes that are appropriate for particular patients, not just appropriate for the average patient. In fact, published developments show that random-effects linear models may provide a solid theoretical framework for drug dosage individualization in chronic diseases. In particular, individualized dosages computed with these models by means of an empirical Bayesian approach may produce better results than dosages computed with some methods routinely used in therapeutic drug monitoring. This is further supported by published empirical and theoretical findings that show that random effects linear models may provide accurate representations of phase III and IV steady-state pharmacokinetic data, and may be useful for dosage computations. These models have applications in the design of clinical algorithms for drug dosage individualization in chronic diseases; in the computation of dose correction factors; computation of the minimum number of blood samples from a patient that are necessary for calculating an optimal individualized drug dosage in therapeutic drug monitoring; measure of the clinical importance of clinical, demographic, environmental or genetic covariates; study of drug-drug interactions in clinical settings; the implementation of computational tools for web-site-based evidence farming; design of pharmacogenomic studies; and in the development of a pharmacological theory of dosage individualization.

Mathematical modeling and computer simulation of isoelectric focusing with electrochemically defined ampholytes

NASA Technical Reports Server (NTRS)

Palusinski, O. A.; Allgyer, T. T.; Mosher, R. A.; Bier, M.; Saville, D. A.

1981-01-01

A mathematical model of isoelectric focusing at the steady state has been developed for an M-component system of electrochemically defined ampholytes. The model is formulated from fundamental principles describing the components' chemical equilibria, mass transfer resulting from diffusion and electromigration, and electroneutrality. The model consists of ordinary differential equations coupled with a system of algebraic equations. The model is implemented on a digital computer using FORTRAN-based simulation software. Computer simulation data are presented for several two-component systems showing the effects of varying the isoelectric points and dissociation constants of the constituents.

Learning Universal Computations with Spikes

PubMed Central

Thalmeier, Dominik; Uhlmann, Marvin; Kappen, Hilbert J.; Memmesheimer, Raoul-Martin

2016-01-01

Providing the neurobiological basis of information processing in higher animals, spiking neural networks must be able to learn a variety of complicated computations, including the generation of appropriate, possibly delayed reactions to inputs and the self-sustained generation of complex activity patterns, e.g. for locomotion. Many such computations require previous building of intrinsic world models. Here we show how spiking neural networks may solve these different tasks. Firstly, we derive constraints under which classes of spiking neural networks lend themselves to substrates of powerful general purpose computing. The networks contain dendritic or synaptic nonlinearities and have a constrained connectivity. We then combine such networks with learning rules for outputs or recurrent connections. We show that this allows to learn even difficult benchmark tasks such as the self-sustained generation of desired low-dimensional chaotic dynamics or memory-dependent computations. Furthermore, we show how spiking networks can build models of external world systems and use the acquired knowledge to control them. PMID:27309381

68Ga/177Lu-labeled DOTA-TATE shows similar imaging and biodistribution in neuroendocrine tumor model.

PubMed

Liu, Fei; Zhu, Hua; Yu, Jiangyuan; Han, Xuedi; Xie, Qinghua; Liu, Teli; Xia, Chuanqin; Li, Nan; Yang, Zhi

2017-06-01

Somatostatin receptors are overexpressed in neuroendocrine tumors, whose endogenous ligands are somatostatin. DOTA-TATE is an analogue of somatostatin, which shows high binding affinity to somatostatin receptors. We aim to evaluate the 68 Ga/ 177 Lu-labeling DOTA-TATE kit in neuroendocrine tumor model for molecular imaging and to try human-positron emission tomography/computed tomography imaging of 68 Ga-DOTA-TATE in neuroendocrine tumor patients. DOTA-TATE kits were formulated and radiolabeled with 68 Ga/ 177 Lu for 68 Ga/ 177 Lu-DOTA-TATE (M-DOTA-TATE). In vitro and in vivo stability of 177 Lu-DOTA-TATE were performed. Nude mice bearing human tumors were injected with 68 Ga-DOTA-TATE or 177 Lu-DOTA-TATE for micro-positron emission tomography and micro-single-photon emission computed tomography/computed tomography imaging separately, and clinical positron emission tomography/computed tomography images of 68 Ga-DOTA-TATE were obtained at 1 h post-intravenous injection from patients with neuroendocrine tumors. Micro-positron emission tomography and micro-single-photon emission computed tomography/computed tomography imaging of 68 Ga-DOTA-TATE and 177 Lu-DOTA-TATE both showed clear tumor uptake which could be blocked by excess DOTA-TATE. In addition, 68 Ga-DOTA-TATE-positron emission tomography/computed tomography imaging in neuroendocrine tumor patients could show primary and metastatic lesions. 68 Ga-DOTA-TATE and 177 Lu-DOTA-TATE could accumulate in tumors in animal models, paving the way for better clinical peptide receptor radionuclide therapy for neuroendocrine tumor patients in Asian population.

Collisional transport across the magnetic field in drift-fluid models

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

Madsen, J., E-mail: jmad@fysik.dtu.dk; Naulin, V.; Nielsen, A. H.

2016-03-15

Drift ordered fluid models are widely applied in studies of low-frequency turbulence in the edge and scrape-off layer regions of magnetically confined plasmas. Here, we show how collisional transport across the magnetic field is self-consistently incorporated into drift-fluid models without altering the drift-fluid energy integral. We demonstrate that the inclusion of collisional transport in drift-fluid models gives rise to diffusion of particle density, momentum, and pressures in drift-fluid turbulence models and, thereby, obviates the customary use of artificial diffusion in turbulence simulations. We further derive a computationally efficient, two-dimensional model, which can be time integrated for several turbulence de-correlation timesmore » using only limited computational resources. The model describes interchange turbulence in a two-dimensional plane perpendicular to the magnetic field located at the outboard midplane of a tokamak. The model domain has two regions modeling open and closed field lines. The model employs a computational expedient model for collisional transport. Numerical simulations show good agreement between the full and the simplified model for collisional transport.« less

The effects of nutrition labeling on consumer food choice: a psychological experiment and computational model.

PubMed

Helfer, Peter; Shultz, Thomas R

2014-12-01

The widespread availability of calorie-dense food is believed to be a contributing cause of an epidemic of obesity and associated diseases throughout the world. One possible countermeasure is to empower consumers to make healthier food choices with useful nutrition labeling. An important part of this endeavor is to determine the usability of existing and proposed labeling schemes. Here, we report an experiment on how four different labeling schemes affect the speed and nutritional value of food choices. We then apply decision field theory, a leading computational model of human decision making, to simulate the experimental results. The psychology experiment shows that quantitative, single-attribute labeling schemes have greater usability than multiattribute and binary ones, and that they remain effective under moderate time pressure. The computational model simulates these psychological results and provides explanatory insights into them. This work shows how experimental psychology and computational modeling can contribute to the evaluation and improvement of nutrition-labeling schemes. © 2014 New York Academy of Sciences.

Limits on efficient computation in the physical world

NASA Astrophysics Data System (ADS)

Aaronson, Scott Joel

More than a speculative technology, quantum computing seems to challenge our most basic intuitions about how the physical world should behave. In this thesis I show that, while some intuitions from classical computer science must be jettisoned in the light of modern physics, many others emerge nearly unscathed; and I use powerful tools from computational complexity theory to help determine which are which. In the first part of the thesis, I attack the common belief that quantum computing resembles classical exponential parallelism, by showing that quantum computers would face serious limitations on a wider range of problems than was previously known. In particular, any quantum algorithm that solves the collision problem---that of deciding whether a sequence of n integers is one-to-one or two-to-one---must query the sequence O (n1/5) times. This resolves a question that was open for years; previously no lower bound better than constant was known. A corollary is that there is no "black-box" quantum algorithm to break cryptographic hash functions or solve the Graph Isomorphism problem in polynomial time. I also show that relative to an oracle, quantum computers could not solve NP-complete problems in polynomial time, even with the help of nonuniform "quantum advice states"; and that any quantum algorithm needs O (2n/4/n) queries to find a local minimum of a black-box function on the n-dimensional hypercube. Surprisingly, the latter result also leads to new classical lower bounds for the local search problem. Finally, I give new lower bounds on quantum one-way communication complexity, and on the quantum query complexity of total Boolean functions and recursive Fourier sampling. The second part of the thesis studies the relationship of the quantum computing model to physical reality. I first examine the arguments of Leonid Levin, Stephen Wolfram, and others who believe quantum computing to be fundamentally impossible. I find their arguments unconvincing without a "Sure/Shor separator"---a criterion that separates the already-verified quantum states from those that appear in Shor's factoring algorithm. I argue that such a separator should be based on a complexity classification of quantum states, and go on to create such a classification. Next I ask what happens to the quantum computing model if we take into account that the speed of light is finite---and in particular, whether Grover's algorithm still yields a quadratic speedup for searching a database. Refuting a claim by Benioff, I show that the surprising answer is yes. Finally, I analyze hypothetical models of computation that go even beyond quantum computing. I show that many such models would be as powerful as the complexity class PP, and use this fact to give a simple, quantum computing based proof that PP is closed under intersection. On the other hand, I also present one model---wherein we could sample the entire history of a hidden variable---that appears to be more powerful than standard quantum computing, but only slightly so.

Efficient quantum circuits for one-way quantum computing.

PubMed

Tanamoto, Tetsufumi; Liu, Yu-Xi; Hu, Xuedong; Nori, Franco

2009-03-13

While Ising-type interactions are ideal for implementing controlled phase flip gates in one-way quantum computing, natural interactions between solid-state qubits are most often described by either the XY or the Heisenberg models. We show an efficient way of generating cluster states directly using either the imaginary SWAP (iSWAP) gate for the XY model, or the sqrt[SWAP] gate for the Heisenberg model. Our approach thus makes one-way quantum computing more feasible for solid-state devices.

Using Video Modeling via Handheld Computers to Improve Geometry Skills for High School Students with Learning Disabilities

ERIC Educational Resources Information Center

Cihak, David F.; Bowlin, Tammy

2009-01-01

The researchers examined the use of video modeling by means of a handheld computer as an alternative instructional delivery system for learning basic geometry skills. Three high school students with learning disabilities participated in this study. Through video modeling, teacher-developed video clips showing step-by-step problem solving processes…

Computational models of an inductive power transfer system for electric vehicle battery charge

NASA Astrophysics Data System (ADS)

Anele, A. O.; Hamam, Y.; Chassagne, L.; Linares, J.; Alayli, Y.; Djouani, K.

2015-09-01

One of the issues to be solved for electric vehicles (EVs) to become a success is the technical solution of its charging system. In this paper, computational models of an inductive power transfer (IPT) system for EV battery charge are presented. Based on the fundamental principles behind IPT systems, 3 kW single phase and 22 kW three phase IPT systems for Renault ZOE are designed in MATLAB/Simulink. The results obtained based on the technical specifications of the lithium-ion battery and charger type of Renault ZOE show that the models are able to provide the total voltage required by the battery. Also, considering the charging time for each IPT model, they are capable of delivering the electricity needed to power the ZOE. In conclusion, this study shows that the designed computational IPT models may be employed as a support structure needed to effectively power any viable EV.

Model-Based Knowing: How Do Students Ground Their Understanding About Climate Systems in Agent-Based Computer Models?

NASA Astrophysics Data System (ADS)

Markauskaite, Lina; Kelly, Nick; Jacobson, Michael J.

2017-12-01

This paper gives a grounded cognition account of model-based learning of complex scientific knowledge related to socio-scientific issues, such as climate change. It draws on the results from a study of high school students learning about the carbon cycle through computational agent-based models and investigates two questions: First, how do students ground their understanding about the phenomenon when they learn and solve problems with computer models? Second, what are common sources of mistakes in students' reasoning with computer models? Results show that students ground their understanding in computer models in five ways: direct observation, straight abstraction, generalisation, conceptualisation, and extension. Students also incorporate into their reasoning their knowledge and experiences that extend beyond phenomena represented in the models, such as attitudes about unsustainable carbon emission rates, human agency, external events, and the nature of computational models. The most common difficulties of the students relate to seeing the modelled scientific phenomenon and connecting results from the observations with other experiences and understandings about the phenomenon in the outside world. An important contribution of this study is the constructed coding scheme for establishing different ways of grounding, which helps to understand some challenges that students encounter when they learn about complex phenomena with agent-based computer models.

An agent-based computational model for tuberculosis spreading on age-structured populations

NASA Astrophysics Data System (ADS)

Graciani Rodrigues, C. C.; Espíndola, Aquino L.; Penna, T. J. P.

2015-06-01

In this work we present an agent-based computational model to study the spreading of the tuberculosis (TB) disease on age-structured populations. The model proposed is a merge of two previous models: an agent-based computational model for the spreading of tuberculosis and a bit-string model for biological aging. The combination of TB with the population aging, reproduces the coexistence of health states, as seen in real populations. In addition, the universal exponential behavior of mortalities curves is still preserved. Finally, the population distribution as function of age shows the prevalence of TB mostly in elders, for high efficacy treatments.

High-performance biocomputing for simulating the spread of contagion over large contact networks

PubMed Central

2012-01-01

Background Many important biological problems can be modeled as contagion diffusion processes over interaction networks. This article shows how the EpiSimdemics interaction-based simulation system can be applied to the general contagion diffusion problem. Two specific problems, computational epidemiology and human immune system modeling, are given as examples. We then show how the graphics processing unit (GPU) within each compute node of a cluster can effectively be used to speed-up the execution of these types of problems. Results We show that a single GPU can accelerate the EpiSimdemics computation kernel by a factor of 6 and the entire application by a factor of 3.3, compared to the execution time on a single core. When 8 CPU cores and 2 GPU devices are utilized, the speed-up of the computational kernel increases to 9.5. When combined with effective techniques for inter-node communication, excellent scalability can be achieved without significant loss of accuracy in the results. Conclusions We show that interaction-based simulation systems can be used to model disparate and highly relevant problems in biology. We also show that offloading some of the work to GPUs in distributed interaction-based simulations can be an effective way to achieve increased intra-node efficiency. PMID:22537298

A computational continuum model of poroelastic beds

PubMed Central

Zampogna, G. A.

2017-01-01

Despite the ubiquity of fluid flows interacting with porous and elastic materials, we lack a validated non-empirical macroscale method for characterizing the flow over and through a poroelastic medium. We propose a computational tool to describe such configurations by deriving and validating a continuum model for the poroelastic bed and its interface with the above free fluid. We show that, using stress continuity condition and slip velocity condition at the interface, the effective model captures the effects of small changes in the microstructure anisotropy correctly and predicts the overall behaviour in a physically consistent and controllable manner. Moreover, we show that the performance of the effective model is accurate by validating with fully microscopic resolved simulations. The proposed computational tool can be used in investigations in a wide range of fields, including mechanical engineering, bio-engineering and geophysics. PMID:28413355

Modeling and simulation of ocean wave propagation using lattice Boltzmann method

NASA Astrophysics Data System (ADS)

Nuraiman, Dian

2017-10-01

In this paper, we present on modeling and simulation of ocean wave propagation from the deep sea to the shoreline. This requires high computational cost for simulation with large domain. We propose to couple a 1D shallow water equations (SWE) model with a 2D incompressible Navier-Stokes equations (NSE) model in order to reduce the computational cost. The coupled model is solved using the lattice Boltzmann method (LBM) with the lattice Bhatnagar-Gross-Krook (BGK) scheme. Additionally, a special method is implemented to treat the complex behavior of free surface close to the shoreline. The result shows the coupled model can reduce computational cost significantly compared to the full NSE model.

A cortical edge-integration model of object-based lightness computation that explains effects of spatial context and individual differences

PubMed Central

Rudd, Michael E.

2014-01-01

Previous work has demonstrated that perceived surface reflectance (lightness) can be modeled in simple contexts in a quantitatively exact way by assuming that the visual system first extracts information about local, directed steps in log luminance, then spatially integrates these steps along paths through the image to compute lightness (Rudd and Zemach, 2004, 2005, 2007). This method of computing lightness is called edge integration. Recent evidence (Rudd, 2013) suggests that human vision employs a default strategy to integrate luminance steps only along paths from a common background region to the targets whose lightness is computed. This implies a role for gestalt grouping in edge-based lightness computation. Rudd (2010) further showed the perceptual weights applied to edges in lightness computation can be influenced by the observer's interpretation of luminance steps as resulting from either spatial variation in surface reflectance or illumination. This implies a role for top-down factors in any edge-based model of lightness (Rudd and Zemach, 2005). Here, I show how the separate influences of grouping and attention on lightness can be modeled in tandem by a cortical mechanism that first employs top-down signals to spatially select regions of interest for lightness computation. An object-based network computation, involving neurons that code for border-ownership, then automatically sets the neural gains applied to edge signals surviving the earlier spatial selection stage. Only the borders that survive both processing stages are spatially integrated to compute lightness. The model assumptions are consistent with those of the cortical lightness model presented earlier by Rudd (2010, 2013), and with neurophysiological data indicating extraction of local edge information in V1, network computations to establish figure-ground relations and border ownership in V2, and edge integration to encode lightness and darkness signals in V4. PMID:25202253

A cortical edge-integration model of object-based lightness computation that explains effects of spatial context and individual differences.

PubMed

Rudd, Michael E

2014-01-01

Previous work has demonstrated that perceived surface reflectance (lightness) can be modeled in simple contexts in a quantitatively exact way by assuming that the visual system first extracts information about local, directed steps in log luminance, then spatially integrates these steps along paths through the image to compute lightness (Rudd and Zemach, 2004, 2005, 2007). This method of computing lightness is called edge integration. Recent evidence (Rudd, 2013) suggests that human vision employs a default strategy to integrate luminance steps only along paths from a common background region to the targets whose lightness is computed. This implies a role for gestalt grouping in edge-based lightness computation. Rudd (2010) further showed the perceptual weights applied to edges in lightness computation can be influenced by the observer's interpretation of luminance steps as resulting from either spatial variation in surface reflectance or illumination. This implies a role for top-down factors in any edge-based model of lightness (Rudd and Zemach, 2005). Here, I show how the separate influences of grouping and attention on lightness can be modeled in tandem by a cortical mechanism that first employs top-down signals to spatially select regions of interest for lightness computation. An object-based network computation, involving neurons that code for border-ownership, then automatically sets the neural gains applied to edge signals surviving the earlier spatial selection stage. Only the borders that survive both processing stages are spatially integrated to compute lightness. The model assumptions are consistent with those of the cortical lightness model presented earlier by Rudd (2010, 2013), and with neurophysiological data indicating extraction of local edge information in V1, network computations to establish figure-ground relations and border ownership in V2, and edge integration to encode lightness and darkness signals in V4.

Modelling rollover behaviour of exacavator-based forest machines

Treesearch

M.W. Veal; S.E. Taylor; Robert B. Rummer

2003-01-01

This poster presentation provides results from analytical and computer simulation models of rollover behaviour of hydraulic excavators. These results are being used as input to the operator protective structure standards development process. Results from rigid body mechanics and computer simulation methods agree well with field rollover test data. These results show...

SCS-CN based time-distributed sediment yield model

NASA Astrophysics Data System (ADS)

Tyagi, J. V.; Mishra, S. K.; Singh, Ranvir; Singh, V. P.

2008-05-01

SummaryA sediment yield model is developed to estimate the temporal rates of sediment yield from rainfall events on natural watersheds. The model utilizes the SCS-CN based infiltration model for computation of rainfall-excess rate, and the SCS-CN-inspired proportionality concept for computation of sediment-excess. For computation of sedimentographs, the sediment-excess is routed to the watershed outlet using a single linear reservoir technique. Analytical development of the model shows the ratio of the potential maximum erosion (A) to the potential maximum retention (S) of the SCS-CN method is constant for a watershed. The model is calibrated and validated on a number of events using the data of seven watersheds from India and the USA. Representative values of the A/S ratio computed for the watersheds from calibration are used for the validation of the model. The encouraging results of the proposed simple four parameter model exhibit its potential in field application.

Gender Differences in Attitudes toward Computers and Performance in the Accounting Information Systems Class

ERIC Educational Resources Information Center

Lenard, Mary Jane; Wessels, Susan; Khanlarian, Cindi

2010-01-01

Using a model developed by Young (2000), this paper explores the relationship between performance in the Accounting Information Systems course, self-assessed computer skills, and attitudes toward computers. Results show that after taking the AIS course, students experience a change in perception about their use of computers. Females'…

A Review of Hemolysis Prediction Models for Computational Fluid Dynamics.

PubMed

Yu, Hai; Engel, Sebastian; Janiga, Gábor; Thévenin, Dominique

2017-07-01

Flow-induced hemolysis is a crucial issue for many biomedical applications; in particular, it is an essential issue for the development of blood-transporting devices such as left ventricular assist devices, and other types of blood pumps. In order to estimate red blood cell (RBC) damage in blood flows, many models have been proposed in the past. Most models have been validated by their respective authors. However, the accuracy and the validity range of these models remains unclear. In this work, the most established hemolysis models compatible with computational fluid dynamics of full-scale devices are described and assessed by comparing two selected reference experiments: a simple rheometric flow and a more complex hemodialytic flow through a needle. The quantitative comparisons show very large deviations concerning hemolysis predictions, depending on the model and model parameter. In light of the current results, two simple power-law models deliver the best compromise between computational efficiency and obtained accuracy. Finally, hemolysis has been computed in an axial blood pump. The reconstructed geometry of a HeartMate II shows that hemolysis occurs mainly at the tip and leading edge of the rotor blades, as well as at the leading edge of the diffusor vanes. © 2017 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Nonequilibrium scheme for computing the flux of the convection-diffusion equation in the framework of the lattice Boltzmann method.

PubMed

Chai, Zhenhua; Zhao, T S

2014-07-01

In this paper, we propose a local nonequilibrium scheme for computing the flux of the convection-diffusion equation with a source term in the framework of the multiple-relaxation-time (MRT) lattice Boltzmann method (LBM). Both the Chapman-Enskog analysis and the numerical results show that, at the diffusive scaling, the present nonequilibrium scheme has a second-order convergence rate in space. A comparison between the nonequilibrium scheme and the conventional second-order central-difference scheme indicates that, although both schemes have a second-order convergence rate in space, the present nonequilibrium scheme is more accurate than the central-difference scheme. In addition, the flux computation rendered by the present scheme also preserves the parallel computation feature of the LBM, making the scheme more efficient than conventional finite-difference schemes in the study of large-scale problems. Finally, a comparison between the single-relaxation-time model and the MRT model is also conducted, and the results show that the MRT model is more accurate than the single-relaxation-time model, both in solving the convection-diffusion equation and in computing the flux.

Interactions of spatial strategies producing generalization gradient and blocking: A computational approach

PubMed Central

Dollé, Laurent; Chavarriaga, Ricardo

2018-01-01

We present a computational model of spatial navigation comprising different learning mechanisms in mammals, i.e., associative, cognitive mapping and parallel systems. This model is able to reproduce a large number of experimental results in different variants of the Morris water maze task, including standard associative phenomena (spatial generalization gradient and blocking), as well as navigation based on cognitive mapping. Furthermore, we show that competitive and cooperative patterns between different navigation strategies in the model allow to explain previous apparently contradictory results supporting either associative or cognitive mechanisms for spatial learning. The key computational mechanism to reconcile experimental results showing different influences of distal and proximal cues on the behavior, different learning times, and different abilities of individuals to alternatively perform spatial and response strategies, relies in the dynamic coordination of navigation strategies, whose performance is evaluated online with a common currency through a modular approach. We provide a set of concrete experimental predictions to further test the computational model. Overall, this computational work sheds new light on inter-individual differences in navigation learning, and provides a formal and mechanistic approach to test various theories of spatial cognition in mammals. PMID:29630600

A computational model of selection by consequences: log survivor plots.

PubMed

Kulubekova, Saule; McDowell, J J

2008-06-01

[McDowell, J.J, 2004. A computational model of selection by consequences. J. Exp. Anal. Behav. 81, 297-317] instantiated the principle of selection by consequences in a virtual organism with an evolving repertoire of possible behaviors undergoing selection, reproduction, and mutation over many generations. The process is based on the computational approach, which is non-deterministic and rules-based. The model proposes a causal account for operant behavior. McDowell found that the virtual organism consistently showed a hyperbolic relationship between response and reinforcement rates according to the quantitative law of effect. To continue validation of the computational model, the present study examined its behavior on the molecular level by comparing the virtual organism's IRT distributions in the form of log survivor plots to findings from live organisms. Log survivor plots did not show the "broken-stick" feature indicative of distinct bouts and pauses in responding, although the bend in slope of the plots became more defined at low reinforcement rates. The shape of the virtual organism's log survivor plots was more consistent with the data on reinforced responding in pigeons. These results suggest that log survivor plot patterns of the virtual organism were generally consistent with the findings from live organisms providing further support for the computational model of selection by consequences as a viable account of operant behavior.

A comparison of homogeneous equilibrium and relaxation model for CO2 expansion inside the two-phase ejector

NASA Astrophysics Data System (ADS)

Palacz, M.; Haida, M.; Smolka, J.; Nowak, A. J.; Hafner, A.

2016-09-01

In this study, the comparison of the accuracy of the homogeneous equilibrium model (HEM) and homogeneous relaxation model (HRM) is presented. Both models were applied to simulate the CO2 expansion inside the two-phase ejectors. Moreover, the mentioned models were implemented in the robust and efficient computational tool ejectorPL. That tool guarantees the fully automated computational process and the repeatable computations for the various ejector shapes and operating conditions. The simulated motive nozzle mass flow rates were compared to the experimentally measured mass flow rates. That comparison was made for both, HEM and HRM. The results showed the unsatisfying fidelity of the HEM for the operating regimes far from the carbon dioxide critical point. On the other hand, the HRM accuracy for such conditions was slightly higher. The approach presented in this paper, showed the limitation of applicability of both two-phase models for the expansion phenomena inside the ejectors.

Role of Statistical Random-Effects Linear Models in Personalized Medicine

PubMed Central

Diaz, Francisco J; Yeh, Hung-Wen; de Leon, Jose

2012-01-01

Some empirical studies and recent developments in pharmacokinetic theory suggest that statistical random-effects linear models are valuable tools that allow describing simultaneously patient populations as a whole and patients as individuals. This remarkable characteristic indicates that these models may be useful in the development of personalized medicine, which aims at finding treatment regimes that are appropriate for particular patients, not just appropriate for the average patient. In fact, published developments show that random-effects linear models may provide a solid theoretical framework for drug dosage individualization in chronic diseases. In particular, individualized dosages computed with these models by means of an empirical Bayesian approach may produce better results than dosages computed with some methods routinely used in therapeutic drug monitoring. This is further supported by published empirical and theoretical findings that show that random effects linear models may provide accurate representations of phase III and IV steady-state pharmacokinetic data, and may be useful for dosage computations. These models have applications in the design of clinical algorithms for drug dosage individualization in chronic diseases; in the computation of dose correction factors; computation of the minimum number of blood samples from a patient that are necessary for calculating an optimal individualized drug dosage in therapeutic drug monitoring; measure of the clinical importance of clinical, demographic, environmental or genetic covariates; study of drug-drug interactions in clinical settings; the implementation of computational tools for web-site-based evidence farming; design of pharmacogenomic studies; and in the development of a pharmacological theory of dosage individualization. PMID:23467392

Reduced order surrogate modelling (ROSM) of high dimensional deterministic simulations

NASA Astrophysics Data System (ADS)

Mitry, Mina

Often, computationally expensive engineering simulations can prohibit the engineering design process. As a result, designers may turn to a less computationally demanding approximate, or surrogate, model to facilitate their design process. However, owing to the the curse of dimensionality, classical surrogate models become too computationally expensive for high dimensional data. To address this limitation of classical methods, we develop linear and non-linear Reduced Order Surrogate Modelling (ROSM) techniques. Two algorithms are presented, which are based on a combination of linear/kernel principal component analysis and radial basis functions. These algorithms are applied to subsonic and transonic aerodynamic data, as well as a model for a chemical spill in a channel. The results of this thesis show that ROSM can provide a significant computational benefit over classical surrogate modelling, sometimes at the expense of a minor loss in accuracy.

Relative resilience to noise of standard and sequential approaches to measurement-based quantum computation

NASA Astrophysics Data System (ADS)

Gallagher, C. B.; Ferraro, A.

2018-05-01

A possible alternative to the standard model of measurement-based quantum computation (MBQC) is offered by the sequential model of MBQC—a particular class of quantum computation via ancillae. Although these two models are equivalent under ideal conditions, their relative resilience to noise in practical conditions is not yet known. We analyze this relationship for various noise models in the ancilla preparation and in the entangling-gate implementation. The comparison of the two models is performed utilizing both the gate infidelity and the diamond distance as figures of merit. Our results show that in the majority of instances the sequential model outperforms the standard one in regard to a universal set of operations for quantum computation. Further investigation is made into the performance of sequential MBQC in experimental scenarios, thus setting benchmarks for possible cavity-QED implementations.

Computational models of basal-ganglia pathway functions: focus on functional neuroanatomy

PubMed Central

Schroll, Henning; Hamker, Fred H.

2013-01-01

Over the past 15 years, computational models have had a considerable impact on basal-ganglia research. Most of these models implement multiple distinct basal-ganglia pathways and assume them to fulfill different functions. As there is now a multitude of different models, it has become complex to keep track of their various, sometimes just marginally different assumptions on pathway functions. Moreover, it has become a challenge to oversee to what extent individual assumptions are corroborated or challenged by empirical data. Focusing on computational, but also considering non-computational models, we review influential concepts of pathway functions and show to what extent they are compatible with or contradict each other. Moreover, we outline how empirical evidence favors or challenges specific model assumptions and propose experiments that allow testing assumptions against each other. PMID:24416002

Capability of GPGPU for Faster Thermal Analysis Used in Data Assimilation

NASA Astrophysics Data System (ADS)

Takaki, Ryoji; Akita, Takeshi; Shima, Eiji

A thermal mathematical model plays an important role in operations on orbit as well as spacecraft thermal designs. The thermal mathematical model has some uncertain thermal characteristic parameters, such as thermal contact resistances between components, effective emittances of multilayer insulation (MLI) blankets, discouraging make up efficiency and accuracy of the model. A particle filter which is one of successive data assimilation methods has been applied to construct spacecraft thermal mathematical models. This method conducts a lot of ensemble computations, which require large computational power. Recently, General Purpose computing in Graphics Processing Unit (GPGPU) has been attracted attention in high performance computing. Therefore GPGPU is applied to increase the computational speed of thermal analysis used in the particle filter. This paper shows the speed-up results by using GPGPU as well as the application method of GPGPU.

Longitudinal train dynamics: an overview

NASA Astrophysics Data System (ADS)

Wu, Qing; Spiryagin, Maksym; Cole, Colin

2016-12-01

This paper discusses the evolution of longitudinal train dynamics (LTD) simulations, which covers numerical solvers, vehicle connection systems, air brake systems, wagon dumper systems and locomotives, resistance forces and gravitational components, vehicle in-train instabilities, and computing schemes. A number of potential research topics are suggested, such as modelling of friction, polymer, and transition characteristics for vehicle connection simulations, studies of wagon dumping operations, proper modelling of vehicle in-train instabilities, and computing schemes for LTD simulations. Evidence shows that LTD simulations have evolved with computing capabilities. Currently, advanced component models that directly describe the working principles of the operation of air brake systems, vehicle connection systems, and traction systems are available. Parallel computing is a good solution to combine and simulate all these advanced models. Parallel computing can also be used to conduct three-dimensional long train dynamics simulations.

Iterative updating of model error for Bayesian inversion

NASA Astrophysics Data System (ADS)

Calvetti, Daniela; Dunlop, Matthew; Somersalo, Erkki; Stuart, Andrew

2018-02-01

In computational inverse problems, it is common that a detailed and accurate forward model is approximated by a computationally less challenging substitute. The model reduction may be necessary to meet constraints in computing time when optimization algorithms are used to find a single estimate, or to speed up Markov chain Monte Carlo (MCMC) calculations in the Bayesian framework. The use of an approximate model introduces a discrepancy, or modeling error, that may have a detrimental effect on the solution of the ill-posed inverse problem, or it may severely distort the estimate of the posterior distribution. In the Bayesian paradigm, the modeling error can be considered as a random variable, and by using an estimate of the probability distribution of the unknown, one may estimate the probability distribution of the modeling error and incorporate it into the inversion. We introduce an algorithm which iterates this idea to update the distribution of the model error, leading to a sequence of posterior distributions that are demonstrated empirically to capture the underlying truth with increasing accuracy. Since the algorithm is not based on rejections, it requires only limited full model evaluations. We show analytically that, in the linear Gaussian case, the algorithm converges geometrically fast with respect to the number of iterations when the data is finite dimensional. For more general models, we introduce particle approximations of the iteratively generated sequence of distributions; we also prove that each element of the sequence converges in the large particle limit under a simplifying assumption. We show numerically that, as in the linear case, rapid convergence occurs with respect to the number of iterations. Additionally, we show through computed examples that point estimates obtained from this iterative algorithm are superior to those obtained by neglecting the model error.

A SCILAB Program for Computing General-Relativistic Models of Rotating Neutron Stars by Implementing Hartle's Perturbation Method

NASA Astrophysics Data System (ADS)

Papasotiriou, P. J.; Geroyannis, V. S.

We implement Hartle's perturbation method to the computation of relativistic rigidly rotating neutron star models. The program has been written in SCILAB (© INRIA ENPC), a matrix-oriented high-level programming language. The numerical method is described in very detail and is applied to many models in slow or fast rotation. We show that, although the method is perturbative, it gives accurate results for all practical purposes and it should prove an efficient tool for computing rapidly rotating pulsars.

FFT-local gravimetric geoid computation

NASA Technical Reports Server (NTRS)

Nagy, Dezso; Fury, Rudolf J.

1989-01-01

Model computations show that changes of sampling interval introduce only 0.3 cm changes, whereas zero padding provides an improvement of more than 5 cm in the fast Fourier transformation (FFT) generated geoid. For the Global Positioning System (GPS) survey of Franklin County, Ohio, the parameters selected as a result of model computations, allow large reduction in local data requirements while still retaining the cm accuracy when tapering and padding is applied. The results are shown in tables.

Petascale computation performance of lightweight multiscale cardiac models using hybrid programming models.

PubMed

Pope, Bernard J; Fitch, Blake G; Pitman, Michael C; Rice, John J; Reumann, Matthias

2011-01-01

Future multiscale and multiphysics models must use the power of high performance computing (HPC) systems to enable research into human disease, translational medical science, and treatment. Previously we showed that computationally efficient multiscale models will require the use of sophisticated hybrid programming models, mixing distributed message passing processes (e.g. the message passing interface (MPI)) with multithreading (e.g. OpenMP, POSIX pthreads). The objective of this work is to compare the performance of such hybrid programming models when applied to the simulation of a lightweight multiscale cardiac model. Our results show that the hybrid models do not perform favourably when compared to an implementation using only MPI which is in contrast to our results using complex physiological models. Thus, with regards to lightweight multiscale cardiac models, the user may not need to increase programming complexity by using a hybrid programming approach. However, considering that model complexity will increase as well as the HPC system size in both node count and number of cores per node, it is still foreseeable that we will achieve faster than real time multiscale cardiac simulations on these systems using hybrid programming models.

Uncertainty propagation of p-boxes using sparse polynomial chaos expansions

NASA Astrophysics Data System (ADS)

Schöbi, Roland; Sudret, Bruno

2017-06-01

In modern engineering, physical processes are modelled and analysed using advanced computer simulations, such as finite element models. Furthermore, concepts of reliability analysis and robust design are becoming popular, hence, making efficient quantification and propagation of uncertainties an important aspect. In this context, a typical workflow includes the characterization of the uncertainty in the input variables. In this paper, input variables are modelled by probability-boxes (p-boxes), accounting for both aleatory and epistemic uncertainty. The propagation of p-boxes leads to p-boxes of the output of the computational model. A two-level meta-modelling approach is proposed using non-intrusive sparse polynomial chaos expansions to surrogate the exact computational model and, hence, to facilitate the uncertainty quantification analysis. The capabilities of the proposed approach are illustrated through applications using a benchmark analytical function and two realistic engineering problem settings. They show that the proposed two-level approach allows for an accurate estimation of the statistics of the response quantity of interest using a small number of evaluations of the exact computational model. This is crucial in cases where the computational costs are dominated by the runs of high-fidelity computational models.

Uncertainty propagation of p-boxes using sparse polynomial chaos expansions

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

Schöbi, Roland, E-mail: schoebi@ibk.baug.ethz.ch; Sudret, Bruno, E-mail: sudret@ibk.baug.ethz.ch

2017-06-15

In modern engineering, physical processes are modelled and analysed using advanced computer simulations, such as finite element models. Furthermore, concepts of reliability analysis and robust design are becoming popular, hence, making efficient quantification and propagation of uncertainties an important aspect. In this context, a typical workflow includes the characterization of the uncertainty in the input variables. In this paper, input variables are modelled by probability-boxes (p-boxes), accounting for both aleatory and epistemic uncertainty. The propagation of p-boxes leads to p-boxes of the output of the computational model. A two-level meta-modelling approach is proposed using non-intrusive sparse polynomial chaos expansions tomore » surrogate the exact computational model and, hence, to facilitate the uncertainty quantification analysis. The capabilities of the proposed approach are illustrated through applications using a benchmark analytical function and two realistic engineering problem settings. They show that the proposed two-level approach allows for an accurate estimation of the statistics of the response quantity of interest using a small number of evaluations of the exact computational model. This is crucial in cases where the computational costs are dominated by the runs of high-fidelity computational models.« less

An assessment on the use of bivariate, multivariate and soft computing techniques for collapse susceptibility in GIS environ

NASA Astrophysics Data System (ADS)

Yilmaz, Işik; Marschalko, Marian; Bednarik, Martin

2013-04-01

The paper presented herein compares and discusses the use of bivariate, multivariate and soft computing techniques for collapse susceptibility modelling. Conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) models representing the bivariate, multivariate and soft computing techniques were used in GIS based collapse susceptibility mapping in an area from Sivas basin (Turkey). Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index (TWI), stream power index (SPI), Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from the models, and they were then compared by means of their validations. However, Area Under Curve (AUC) values obtained from all three models showed that the map obtained from soft computing (ANN) model looks like more accurate than the other models, accuracies of all three models can be evaluated relatively similar. The results also showed that the conditional probability is an essential method in preparation of collapse susceptibility map and highly compatible with GIS operating features.

Reverse time migration by Krylov subspace reduced order modeling

NASA Astrophysics Data System (ADS)

Basir, Hadi Mahdavi; Javaherian, Abdolrahim; Shomali, Zaher Hossein; Firouz-Abadi, Roohollah Dehghani; Gholamy, Shaban Ali

2018-04-01

Imaging is a key step in seismic data processing. To date, a myriad of advanced pre-stack depth migration approaches have been developed; however, reverse time migration (RTM) is still considered as the high-end imaging algorithm. The main limitations associated with the performance cost of reverse time migration are the intensive computation of the forward and backward simulations, time consumption, and memory allocation related to imaging condition. Based on the reduced order modeling, we proposed an algorithm, which can be adapted to all the aforementioned factors. Our proposed method benefit from Krylov subspaces method to compute certain mode shapes of the velocity model computed by as an orthogonal base of reduced order modeling. Reverse time migration by reduced order modeling is helpful concerning the highly parallel computation and strongly reduces the memory requirement of reverse time migration. The synthetic model results showed that suggested method can decrease the computational costs of reverse time migration by several orders of magnitudes, compared with reverse time migration by finite element method.

A novel grid-based mesoscopic model for evacuation dynamics

NASA Astrophysics Data System (ADS)

Shi, Meng; Lee, Eric Wai Ming; Ma, Yi

2018-05-01

This study presents a novel grid-based mesoscopic model for evacuation dynamics. In this model, the evacuation space is discretised into larger cells than those used in microscopic models. This approach directly computes the dynamic changes crowd densities in cells over the course of an evacuation. The density flow is driven by the density-speed correlation. The computation is faster than in traditional cellular automata evacuation models which determine density by computing the movements of each pedestrian. To demonstrate the feasibility of this model, we apply it to a series of practical scenarios and conduct a parameter sensitivity study of the effect of changes in time step δ. The simulation results show that within the valid range of δ, changing δ has only a minor impact on the simulation. The model also makes it possible to directly acquire key information such as bottleneck areas from a time-varied dynamic density map, even when a relatively large time step is adopted. We use the commercial software AnyLogic to evaluate the model. The result shows that the mesoscopic model is more efficient than the microscopic model and provides more in-situ details (e.g., pedestrian movement pattern) than the macroscopic models.

Measurement and computer simulation of antennas on ships and aircraft for results of operational reliability

NASA Astrophysics Data System (ADS)

Kubina, Stanley J.

1989-09-01

The review of the status of computational electromagnetics by Miller and the exposition by Burke of the developments in one of the more important computer codes in the application of the electric field integral equation method, the Numerical Electromagnetic Code (NEC), coupled with Molinet's summary of progress in techniques based on the Geometrical Theory of Diffraction (GTD), provide a clear perspective on the maturity of the modern discipline of computational electromagnetics and its potential. Audone's exposition of the application to the computation of Radar Scattering Cross-section (RCS) is an indication of the breadth of practical applications and his exploitation of modern near-field measurement techniques reminds one of progress in the measurement discipline which is essential to the validation or calibration of computational modeling methodology when applied to complex structures such as aircraft and ships. The latter monograph also presents some comparison results with computational models. Some of the results presented for scale model and flight measurements show some serious disagreements in the lobe structure which would require some detailed examination. This also applies to the radiation patterns obtained by flight measurement compared with those obtained using wire-grid models and integral equation modeling methods. In the examples which follow, an attempt is made to match measurements results completely over the entire 2 to 30 MHz HF range for antennas on a large patrol aircraft. The problem of validating computer models of HF antennas on a helicopter and using computer models to generate radiation pattern information which cannot be obtained by measurements are discussed. The use of NEC computer models to analyze top-side ship configurations where measurement results are not available and only self-validation measures are available or at best comparisons with an alternate GTD computer modeling technique is also discussed.

When Does Model-Based Control Pay Off?

PubMed Central

2016-01-01

Many accounts of decision making and reinforcement learning posit the existence of two distinct systems that control choice: a fast, automatic system and a slow, deliberative system. Recent research formalizes this distinction by mapping these systems to “model-free” and “model-based” strategies in reinforcement learning. Model-free strategies are computationally cheap, but sometimes inaccurate, because action values can be accessed by inspecting a look-up table constructed through trial-and-error. In contrast, model-based strategies compute action values through planning in a causal model of the environment, which is more accurate but also more cognitively demanding. It is assumed that this trade-off between accuracy and computational demand plays an important role in the arbitration between the two strategies, but we show that the hallmark task for dissociating model-free and model-based strategies, as well as several related variants, do not embody such a trade-off. We describe five factors that reduce the effectiveness of the model-based strategy on these tasks by reducing its accuracy in estimating reward outcomes and decreasing the importance of its choices. Based on these observations, we describe a version of the task that formally and empirically obtains an accuracy-demand trade-off between model-free and model-based strategies. Moreover, we show that human participants spontaneously increase their reliance on model-based control on this task, compared to the original paradigm. Our novel task and our computational analyses may prove important in subsequent empirical investigations of how humans balance accuracy and demand. PMID:27564094

When Does Model-Based Control Pay Off?

PubMed

Kool, Wouter; Cushman, Fiery A; Gershman, Samuel J

2016-08-01

Many accounts of decision making and reinforcement learning posit the existence of two distinct systems that control choice: a fast, automatic system and a slow, deliberative system. Recent research formalizes this distinction by mapping these systems to "model-free" and "model-based" strategies in reinforcement learning. Model-free strategies are computationally cheap, but sometimes inaccurate, because action values can be accessed by inspecting a look-up table constructed through trial-and-error. In contrast, model-based strategies compute action values through planning in a causal model of the environment, which is more accurate but also more cognitively demanding. It is assumed that this trade-off between accuracy and computational demand plays an important role in the arbitration between the two strategies, but we show that the hallmark task for dissociating model-free and model-based strategies, as well as several related variants, do not embody such a trade-off. We describe five factors that reduce the effectiveness of the model-based strategy on these tasks by reducing its accuracy in estimating reward outcomes and decreasing the importance of its choices. Based on these observations, we describe a version of the task that formally and empirically obtains an accuracy-demand trade-off between model-free and model-based strategies. Moreover, we show that human participants spontaneously increase their reliance on model-based control on this task, compared to the original paradigm. Our novel task and our computational analyses may prove important in subsequent empirical investigations of how humans balance accuracy and demand.

MS2PIP prediction server: compute and visualize MS2 peak intensity predictions for CID and HCD fragmentation.

PubMed

Degroeve, Sven; Maddelein, Davy; Martens, Lennart

2015-07-01

We present an MS(2) peak intensity prediction server that computes MS(2) charge 2+ and 3+ spectra from peptide sequences for the most common fragment ions. The server integrates the Unimod public domain post-translational modification database for modified peptides. The prediction model is an improvement of the previously published MS(2)PIP model for Orbitrap-LTQ CID spectra. Predicted MS(2) spectra can be downloaded as a spectrum file and can be visualized in the browser for comparisons with observations. In addition, we added prediction models for HCD fragmentation (Q-Exactive Orbitrap) and show that these models compute accurate intensity predictions on par with CID performance. We also show that training prediction models for CID and HCD separately improves the accuracy for each fragmentation method. The MS(2)PIP prediction server is accessible from http://iomics.ugent.be/ms2pip. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Free energy change of a dislocation due to a Cottrell atmosphere

DOE PAGES

Sills, R. B.; Cai, W.

2018-03-07

The free energy reduction of a dislocation due to a Cottrell atmosphere of solutes is computed using a continuum model. In this work, we show that the free energy change is composed of near-core and far-field components. The far-field component can be computed analytically using the linearized theory of solid solutions. Near the core the linearized theory is inaccurate, and the near-core component must be computed numerically. The influence of interactions between solutes in neighbouring lattice sites is also examined using the continuum model. We show that this model is able to reproduce atomistic calculations of the nickel–hydrogen system, predictingmore » hydride formation on dislocations. The formation of these hydrides leads to dramatic reductions in the free energy. Lastly, the influence of the free energy change on a dislocation’s line tension is examined by computing the equilibrium shape of a dislocation shear loop and the activation stress for a Frank–Read source using discrete dislocation dynamics.« less

Free energy change of a dislocation due to a Cottrell atmosphere

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

Sills, R. B.; Cai, W.

The free energy reduction of a dislocation due to a Cottrell atmosphere of solutes is computed using a continuum model. In this work, we show that the free energy change is composed of near-core and far-field components. The far-field component can be computed analytically using the linearized theory of solid solutions. Near the core the linearized theory is inaccurate, and the near-core component must be computed numerically. The influence of interactions between solutes in neighbouring lattice sites is also examined using the continuum model. We show that this model is able to reproduce atomistic calculations of the nickel–hydrogen system, predictingmore » hydride formation on dislocations. The formation of these hydrides leads to dramatic reductions in the free energy. Lastly, the influence of the free energy change on a dislocation’s line tension is examined by computing the equilibrium shape of a dislocation shear loop and the activation stress for a Frank–Read source using discrete dislocation dynamics.« less

Free energy change of a dislocation due to a Cottrell atmosphere

NASA Astrophysics Data System (ADS)

Sills, R. B.; Cai, W.

2018-06-01

The free energy reduction of a dislocation due to a Cottrell atmosphere of solutes is computed using a continuum model. We show that the free energy change is composed of near-core and far-field components. The far-field component can be computed analytically using the linearized theory of solid solutions. Near the core the linearized theory is inaccurate, and the near-core component must be computed numerically. The influence of interactions between solutes in neighbouring lattice sites is also examined using the continuum model. We show that this model is able to reproduce atomistic calculations of the nickel-hydrogen system, predicting hydride formation on dislocations. The formation of these hydrides leads to dramatic reductions in the free energy. Finally, the influence of the free energy change on a dislocation's line tension is examined by computing the equilibrium shape of a dislocation shear loop and the activation stress for a Frank-Read source using discrete dislocation dynamics.

SU-D-BRC-05: Effects of Motion and Variable RBE in a Lung Patient Treated with Passively Scattered Protons

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

Mirkovic, D; Titt, U; Mohan, R

2016-06-15

Purpose: To evaluate effects of motion and variable relative biological effectiveness (RBE) in a lung cancer patient treated with passively scattered proton therapy using dose volume histograms associated with patient dose computed using three different methods. Methods: A proton treatment plan of a lung cancer patient optimized using clinical treatment planning system (TPS) was used to construct a detailed Monte Carlo (MC) model of the beam delivery system and the patient specific aperture and compensator. A phase space file containing all particles transported through the beam line was collected at the distal surface of the range compensator and subsequently transportedmore » through two different patient models. The first model was based on the average CT used by the TPS and the second model included all 10 phases of the corresponding 4DCT. The physical dose and proton linear energy transfer (LET) were computed in each voxel of two models and used to compute constant and variable RBE MC dose on average CT and 4D CT. The MC computed doses were compared to the TPS dose using dose volume histograms for relevant structures. Results: The results show significant differences in doses to the target and critical structures suggesting the need for more accurate proton dose computation methods. In particular, the 4D dose shows reduced coverage of the target and higher dose to the spinal cord, while variable RBE dose shows higher lung dose. Conclusion: The methodology developed in this pilot study is currently used for the analysis of a cohort of ∼90 lung patients from a clinical trial comparing proton and photon therapy for lung cancer. The results from this study will help us in determining the clinical significance of more accurate dose computation models in proton therapy.« less

A Computer Simulation Approach to Assessing Therapeutic Intervention Points for the Prevention of Cytokine-Induced Cartilage Breakdown

PubMed Central

Proctor, CJ; Macdonald, C; Milner, JM; Rowan, AD; Cawston, TE

2014-01-01

Objective To use a novel computational approach to examine the molecular pathways involved in cartilage breakdown and to use computer simulation to test possible interventions for reducing collagen release. Methods We constructed a computational model of the relevant molecular pathways using the Systems Biology Markup Language, a computer-readable format of a biochemical network. The model was constructed using our experimental data showing that interleukin-1 (IL-1) and oncostatin M (OSM) act synergistically to up-regulate collagenase protein levels and activity and initiate cartilage collagen breakdown. Simulations were performed using the COPASI software package. Results The model predicted that simulated inhibition of JNK or p38 MAPK, and overexpression of tissue inhibitor of metalloproteinases 3 (TIMP-3) led to a reduction in collagen release. Overexpression of TIMP-1 was much less effective than that of TIMP-3 and led to a delay, rather than a reduction, in collagen release. Simulated interventions of receptor antagonists and inhibition of JAK-1, the first kinase in the OSM pathway, were ineffective. So, importantly, the model predicts that it is more effective to intervene at targets that are downstream, such as the JNK pathway, rather than those that are close to the cytokine signal. In vitro experiments confirmed the effectiveness of JNK inhibition. Conclusion Our study shows the value of computer modeling as a tool for examining possible interventions by which to reduce cartilage collagen breakdown. The model predicts that interventions that either prevent transcription or inhibit the activity of collagenases are promising strategies and should be investigated further in an experimental setting. PMID:24757149

Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons

PubMed Central

Buesing, Lars; Bill, Johannes; Nessler, Bernhard; Maass, Wolfgang

2011-01-01

The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons. PMID:22096452

The Impact of IBM Cell Technology on the Programming Paradigm in the Context of Computer Systems for Climate and Weather Models

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

Zhou, Shujia; Duffy, Daniel; Clune, Thomas

The call for ever-increasing model resolutions and physical processes in climate and weather models demands a continual increase in computing power. The IBM Cell processor's order-of-magnitude peak performance increase over conventional processors makes it very attractive to fulfill this requirement. However, the Cell's characteristics, 256KB local memory per SPE and the new low-level communication mechanism, make it very challenging to port an application. As a trial, we selected the solar radiation component of the NASA GEOS-5 climate model, which: (1) is representative of column physics components (half the total computational time), (2) has an extremely high computational intensity: the ratiomore » of computational load to main memory transfers, and (3) exhibits embarrassingly parallel column computations. In this paper, we converted the baseline code (single-precision Fortran) to C and ported it to an IBM BladeCenter QS20. For performance, we manually SIMDize four independent columns and include several unrolling optimizations. Our results show that when compared with the baseline implementation running on one core of Intel's Xeon Woodcrest, Dempsey, and Itanium2, the Cell is approximately 8.8x, 11.6x, and 12.8x faster, respectively. Our preliminary analysis shows that the Cell can also accelerate the dynamics component (~;;25percent total computational time). We believe these dramatic performance improvements make the Cell processor very competitive as an accelerator.« less

Hybrid architecture for encoded measurement-based quantum computation

PubMed Central

Zwerger, M.; Briegel, H. J.; Dür, W.

2014-01-01

We present a hybrid scheme for quantum computation that combines the modular structure of elementary building blocks used in the circuit model with the advantages of a measurement-based approach to quantum computation. We show how to construct optimal resource states of minimal size to implement elementary building blocks for encoded quantum computation in a measurement-based way, including states for error correction and encoded gates. The performance of the scheme is determined by the quality of the resource states, where within the considered error model a threshold of the order of 10% local noise per particle for fault-tolerant quantum computation and quantum communication. PMID:24946906

Users matter : multi-agent systems model of high performance computing cluster users.

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

North, M. J.; Hood, C. S.; Decision and Information Sciences

2005-01-01

High performance computing clusters have been a critical resource for computational science for over a decade and have more recently become integral to large-scale industrial analysis. Despite their well-specified components, the aggregate behavior of clusters is poorly understood. The difficulties arise from complicated interactions between cluster components during operation. These interactions have been studied by many researchers, some of whom have identified the need for holistic multi-scale modeling that simultaneously includes network level, operating system level, process level, and user level behaviors. Each of these levels presents its own modeling challenges, but the user level is the most complex duemore » to the adaptability of human beings. In this vein, there are several major user modeling goals, namely descriptive modeling, predictive modeling and automated weakness discovery. This study shows how multi-agent techniques were used to simulate a large-scale computing cluster at each of these levels.« less

The Modeling of Vibration Damping in SMA Wires

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

Reynolds, D R; Kloucek, P; Seidman, T I

Through a mathematical and computational model of the physical behavior of shape memory alloy wires, this study shows that localized heating and cooling of such materials provides an effective means of damping vibrational energy. The thermally induced pseudo-elastic behavior of a shape memory wire is modeled using a continuum thermodynamic model and solved computationally as described by the authors in [23]. Computational experiments confirm that up to 80% of an initial shock of vibrational energy can be eliminated at the onset of a thermally-induced phase transformation through the use of spatially-distributed transformation regions along the length of a shape memorymore » alloy wire.« less

Salient regions detection using convolutional neural networks and color volume

NASA Astrophysics Data System (ADS)

Liu, Guang-Hai; Hou, Yingkun

2018-03-01

Convolutional neural network is an important technique in machine learning, pattern recognition and image processing. In order to reduce the computational burden and extend the classical LeNet-5 model to the field of saliency detection, we propose a simple and novel computing model based on LeNet-5 network. In the proposed model, hue, saturation and intensity are utilized to extract depth cues, and then we integrate depth cues and color volume to saliency detection following the basic structure of the feature integration theory. Experimental results show that the proposed computing model outperforms some existing state-of-the-art methods on MSRA1000 and ECSSD datasets.

Computing a Comprehensible Model for Spam Filtering

NASA Astrophysics Data System (ADS)

Ruiz-Sepúlveda, Amparo; Triviño-Rodriguez, José L.; Morales-Bueno, Rafael

In this paper, we describe the application of the Desicion Tree Boosting (DTB) learning model to spam email filtering.This classification task implies the learning in a high dimensional feature space. So, it is an example of how the DTB algorithm performs in such feature space problems. In [1], it has been shown that hypotheses computed by the DTB model are more comprehensible that the ones computed by another ensemble methods. Hence, this paper tries to show that the DTB algorithm maintains the same comprehensibility of hypothesis in high dimensional feature space problems while achieving the performance of other ensemble methods. Four traditional evaluation measures (precision, recall, F1 and accuracy) have been considered for performance comparison between DTB and others models usually applied to spam email filtering. The size of the hypothesis computed by a DTB is smaller and more comprehensible than the hypothesis computed by Adaboost and Naïve Bayes.

Using soft computing techniques to predict corrected air permeability using Thomeer parameters, air porosity and grain density

NASA Astrophysics Data System (ADS)

Nooruddin, Hasan A.; Anifowose, Fatai; Abdulraheem, Abdulazeez

2014-03-01

Soft computing techniques are recently becoming very popular in the oil industry. A number of computational intelligence-based predictive methods have been widely applied in the industry with high prediction capabilities. Some of the popular methods include feed-forward neural networks, radial basis function network, generalized regression neural network, functional networks, support vector regression and adaptive network fuzzy inference system. A comparative study among most popular soft computing techniques is presented using a large dataset published in literature describing multimodal pore systems in the Arab D formation. The inputs to the models are air porosity, grain density, and Thomeer parameters obtained using mercury injection capillary pressure profiles. Corrected air permeability is the target variable. Applying developed permeability models in recent reservoir characterization workflow ensures consistency between micro and macro scale information represented mainly by Thomeer parameters and absolute permeability. The dataset was divided into two parts with 80% of data used for training and 20% for testing. The target permeability variable was transformed to the logarithmic scale as a pre-processing step and to show better correlations with the input variables. Statistical and graphical analysis of the results including permeability cross-plots and detailed error measures were created. In general, the comparative study showed very close results among the developed models. The feed-forward neural network permeability model showed the lowest average relative error, average absolute relative error, standard deviations of error and root means squares making it the best model for such problems. Adaptive network fuzzy inference system also showed very good results.

Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks

PubMed Central

Kaltenbacher, Barbara; Hasenauer, Jan

2017-01-01

Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics. PMID:28114351

The Brain Is both Neurocomputer and Quantum Computer

ERIC Educational Resources Information Center

Hameroff, Stuart R.

2007-01-01

In their article, "Is the Brain a Quantum Computer,?" Litt, Eliasmith, Kroon, Weinstein, and Thagard (2006) criticize the Penrose-Hameroff "Orch OR" quantum computational model of consciousness, arguing instead for neurocomputation as an explanation for mental phenomena. Here I clarify and defend Orch OR, show how Orch OR and neurocomputation are…

Technology Acceptance Predictors among Student Teachers and Experienced Classroom Teachers

ERIC Educational Resources Information Center

Smarkola, Claudia

2007-01-01

This study investigated 160 student teachers' and 158 experienced teachers' self-reported computer usage and their future intentions to use computer applications for school assignments. The Technology Acceptance Model (TAM) was used as the framework to determine computer usage and intentions. Statistically significant results showed that after…

Universal quantum computation with little entanglement.

PubMed

Van den Nest, Maarten

2013-02-08

We show that universal quantum computation can be achieved in the standard pure-state circuit model while the entanglement entropy of every bipartition is small in each step of the computation. The entanglement entropy required for large-scale quantum computation even tends to zero. Moreover we show that the same conclusion applies to many entanglement measures commonly used in the literature. This includes e.g., the geometric measure, localizable entanglement, multipartite concurrence, squashed entanglement, witness-based measures, and more generally any entanglement measure which is continuous in a certain natural sense. These results demonstrate that many entanglement measures are unsuitable tools to assess the power of quantum computers.

A computational workflow for designing silicon donor qubits

DOE PAGES

Humble, Travis S.; Ericson, M. Nance; Jakowski, Jacek; ...

2016-09-19

Developing devices that can reliably and accurately demonstrate the principles of superposition and entanglement is an on-going challenge for the quantum computing community. Modeling and simulation offer attractive means of testing early device designs and establishing expectations for operational performance. However, the complex integrated material systems required by quantum device designs are not captured by any single existing computational modeling method. We examine the development and analysis of a multi-staged computational workflow that can be used to design and characterize silicon donor qubit systems with modeling and simulation. Our approach integrates quantum chemistry calculations with electrostatic field solvers to performmore » detailed simulations of a phosphorus dopant in silicon. We show how atomistic details can be synthesized into an operational model for the logical gates that define quantum computation in this particular technology. In conclusion, the resulting computational workflow realizes a design tool for silicon donor qubits that can help verify and validate current and near-term experimental devices.« less

Development of an Aeroelastic Modeling Capability for Transient Nozzle Side Load Analysis

NASA Technical Reports Server (NTRS)

Wang, Ten-See; Zhao, Xiang; Zhang, Sijun; Chen, Yen-Sen

2013-01-01

Lateral nozzle forces are known to cause severe structural damage to any new rocket engine in development during test. While three-dimensional, transient, turbulent, chemically reacting computational fluid dynamics methodology has been demonstrated to capture major side load physics with rigid nozzles, hot-fire tests often show nozzle structure deformation during major side load events, leading to structural damages if structural strengthening measures were not taken. The modeling picture is incomplete without the capability to address the two-way responses between the structure and fluid. The objective of this study is to develop a coupled aeroelastic modeling capability by implementing the necessary structural dynamics component into an anchored computational fluid dynamics methodology. The computational fluid dynamics component is based on an unstructured-grid, pressure-based computational fluid dynamics formulation, while the computational structural dynamics component is developed in the framework of modal analysis. Transient aeroelastic nozzle startup analyses of the Block I Space Shuttle Main Engine at sea level were performed. The computed results from the aeroelastic nozzle modeling are presented.

Multi-scale modeling in cell biology

PubMed Central

Meier-Schellersheim, Martin; Fraser, Iain D. C.; Klauschen, Frederick

2009-01-01

Biomedical research frequently involves performing experiments and developing hypotheses that link different scales of biological systems such as, for instance, the scales of intracellular molecular interactions to the scale of cellular behavior and beyond to the behavior of cell populations. Computational modeling efforts that aim at exploring such multi-scale systems quantitatively with the help of simulations have to incorporate several different simulation techniques due to the different time and space scales involved. Here, we provide a non-technical overview of how different scales of experimental research can be combined with the appropriate computational modeling techniques. We also show that current modeling software permits building and simulating multi-scale models without having to become involved with the underlying technical details of computational modeling. PMID:20448808

Evaluation of a Computational Model of Situational Awareness

NASA Technical Reports Server (NTRS)

Burdick, Mark D.; Shively, R. Jay; Rutkewski, Michael (Technical Monitor)

2000-01-01

Although the use of the psychological construct of situational awareness (SA) assists researchers in creating a flight environment that is safer and more predictable, its true potential remains untapped until a valid means of predicting SA a priori becomes available. Previous work proposed a computational model of SA (CSA) that sought to Fill that void. The current line of research is aimed at validating that model. The results show that the model accurately predicted SA in a piloted simulation.

Comparison of Accelerated Testing with Modeling to Predict Lifetime of CPV Solder Layers (Presentation)

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

Silverman, T. J.; Bosco, N.; Kurtz, S.

2012-03-01

Concentrating photovoltaic (CPV) cell assemblies can fail due to thermomechanical fatigue in the die-attach layer. In this presentation, we show the latest results from our computational model of thermomechanical fatigue. The model is used to estimate the relative lifetime of cell assemblies exposed to various temperature histories consistent with service and with accelerated testing. We also present early results from thermal cycling experiments designed to help validate the computational model.

A computational model of selection by consequences.

PubMed

McDowell, J J

2004-05-01

Darwinian selection by consequences was instantiated in a computational model that consisted of a repertoire of behaviors undergoing selection, reproduction, and mutation over many generations. The model in effect created a digital organism that emitted behavior continuously. The behavior of this digital organism was studied in three series of computational experiments that arranged reinforcement according to random-interval (RI) schedules. The quantitative features of the model were varied over wide ranges in these experiments, and many of the qualitative features of the model also were varied. The digital organism consistently showed a hyperbolic relation between response and reinforcement rates, and this hyperbolic description of the data was consistently better than the description provided by other, similar, function forms. In addition, the parameters of the hyperbola varied systematically with the quantitative, and some of the qualitative, properties of the model in ways that were consistent with findings from biological organisms. These results suggest that the material events responsible for an organism's responding on RI schedules are computationally equivalent to Darwinian selection by consequences. They also suggest that the computational model developed here is worth pursuing further as a possible dynamic account of behavior.

Selecting Summary Statistics in Approximate Bayesian Computation for Calibrating Stochastic Models

PubMed Central

Burr, Tom

2013-01-01

Approximate Bayesian computation (ABC) is an approach for using measurement data to calibrate stochastic computer models, which are common in biology applications. ABC is becoming the “go-to” option when the data and/or parameter dimension is large because it relies on user-chosen summary statistics rather than the full data and is therefore computationally feasible. One technical challenge with ABC is that the quality of the approximation to the posterior distribution of model parameters depends on the user-chosen summary statistics. In this paper, the user requirement to choose effective summary statistics in order to accurately estimate the posterior distribution of model parameters is investigated and illustrated by example, using a model and corresponding real data of mitochondrial DNA population dynamics. We show that for some choices of summary statistics, the posterior distribution of model parameters is closely approximated and for other choices of summary statistics, the posterior distribution is not closely approximated. A strategy to choose effective summary statistics is suggested in cases where the stochastic computer model can be run at many trial parameter settings, as in the example. PMID:24288668

Selecting summary statistics in approximate Bayesian computation for calibrating stochastic models.

PubMed

Burr, Tom; Skurikhin, Alexei

2013-01-01

Approximate Bayesian computation (ABC) is an approach for using measurement data to calibrate stochastic computer models, which are common in biology applications. ABC is becoming the "go-to" option when the data and/or parameter dimension is large because it relies on user-chosen summary statistics rather than the full data and is therefore computationally feasible. One technical challenge with ABC is that the quality of the approximation to the posterior distribution of model parameters depends on the user-chosen summary statistics. In this paper, the user requirement to choose effective summary statistics in order to accurately estimate the posterior distribution of model parameters is investigated and illustrated by example, using a model and corresponding real data of mitochondrial DNA population dynamics. We show that for some choices of summary statistics, the posterior distribution of model parameters is closely approximated and for other choices of summary statistics, the posterior distribution is not closely approximated. A strategy to choose effective summary statistics is suggested in cases where the stochastic computer model can be run at many trial parameter settings, as in the example.

Bayesian Latent Class Analysis Tutorial.

PubMed

Li, Yuelin; Lord-Bessen, Jennifer; Shiyko, Mariya; Loeb, Rebecca

2018-01-01

This article is a how-to guide on Bayesian computation using Gibbs sampling, demonstrated in the context of Latent Class Analysis (LCA). It is written for students in quantitative psychology or related fields who have a working knowledge of Bayes Theorem and conditional probability and have experience in writing computer programs in the statistical language R . The overall goals are to provide an accessible and self-contained tutorial, along with a practical computation tool. We begin with how Bayesian computation is typically described in academic articles. Technical difficulties are addressed by a hypothetical, worked-out example. We show how Bayesian computation can be broken down into a series of simpler calculations, which can then be assembled together to complete a computationally more complex model. The details are described much more explicitly than what is typically available in elementary introductions to Bayesian modeling so that readers are not overwhelmed by the mathematics. Moreover, the provided computer program shows how Bayesian LCA can be implemented with relative ease. The computer program is then applied in a large, real-world data set and explained line-by-line. We outline the general steps in how to extend these considerations to other methodological applications. We conclude with suggestions for further readings.

Resolution of Infinite-Loop in Hyperincursive and Nonlocal Cellular Automata: Introduction to Slime Mold Computing

NASA Astrophysics Data System (ADS)

Aono, Masashi; Gunji, Yukio-Pegio

2004-08-01

How can non-algorithmic/non-deterministic computational syntax be computed? "The hyperincursive system" introduced by Dubois is an anticipatory system embracing the contradiction/uncertainty. Although it may provide a novel viewpoint for the understanding of complex systems, conventional digital computers cannot run faithfully as the hyperincursive computational syntax specifies, in a strict sense. Then is it an imaginary story? In this paper we try to argue that it is not. We show that a model of complex systems "Elementary Conflictable Cellular Automata (ECCA)" proposed by Aono and Gunji is embracing the hyperincursivity and the nonlocality. ECCA is based on locality-only type settings basically as well as other CA models, and/but at the same time, each cell is required to refer to globality-dominant regularity. Due to this contradictory locality-globality loop, the time evolution equation specifies that the system reaches the deadlock/infinite-loop. However, we show that there is a possibility of the resolution of these problems if the computing system has parallel and/but non-distributed property like an amoeboid organism. This paper is an introduction to "the slime mold computing" that is an attempt to cultivate an unconventional notion of computation.

Computer aided segmentation of kidneys using locally shape constrained deformable models on CT images

NASA Astrophysics Data System (ADS)

Erdt, Marius; Sakas, Georgios

2010-03-01

This work presents a novel approach for model based segmentation of the kidney in images acquired by Computed Tomography (CT). The developed computer aided segmentation system is expected to support computer aided diagnosis and operation planning. We have developed a deformable model based approach based on local shape constraints that prevents the model from deforming into neighboring structures while allowing the global shape to adapt freely to the data. Those local constraints are derived from the anatomical structure of the kidney and the presence and appearance of neighboring organs. The adaptation process is guided by a rule-based deformation logic in order to improve the robustness of the segmentation in areas of diffuse organ boundaries. Our work flow consists of two steps: 1.) a user guided positioning and 2.) an automatic model adaptation using affine and free form deformation in order to robustly extract the kidney. In cases which show pronounced pathologies, the system also offers real time mesh editing tools for a quick refinement of the segmentation result. Evaluation results based on 30 clinical cases using CT data sets show an average dice correlation coefficient of 93% compared to the ground truth. The results are therefore in most cases comparable to manual delineation. Computation times of the automatic adaptation step are lower than 6 seconds which makes the proposed system suitable for an application in clinical practice.

A mathematical model of an active control landing gear for load control during impact and roll-out

NASA Technical Reports Server (NTRS)

Mcgehee, J. R.; Carden, H. D.

1976-01-01

A mathematical model of an active control landing gear (ACOLAG) was developed and programmed for operation on a digital computer. The mathematical model includes theoretical subsonic aerodynamics; first-mode wing bending and torsional characteristics; oleo-pneumatic shock strut with fit and binding friction; closed-loop, series-hydraulic control; empirical tire force-deflection characteristics; antiskid braking; and sinusoidal or random runway roughness. The mathematical model was used to compute the loads and motions for a simulated vertical drop test and a simulated landing impact of a conventional (passive) main landing gear designed for a 2268-kg (5000-lbm) class airplane. Computations were also made for a simply modified version of the passive gear including a series-hydraulic active control system. Comparison of computed results for the passive gear with experimental data shows that the active control landing gear analysis is valid for predicting the loads and motions of an airplane during a symmetrical landing. Computed results for the series-hydraulic active control in conjunction with the simply modified passive gear show that 20- to 30-percent reductions in wing force, relative to those occurring with the modified passive gear, can be obtained during the impact phase of the landing. These reductions in wing force could result in substantial increases in fatigue life of the structure.

Make the most of your samples: Bayes factor estimators for high-dimensional models of sequence evolution.

PubMed

Baele, Guy; Lemey, Philippe; Vansteelandt, Stijn

2013-03-06

Accurate model comparison requires extensive computation times, especially for parameter-rich models of sequence evolution. In the Bayesian framework, model selection is typically performed through the evaluation of a Bayes factor, the ratio of two marginal likelihoods (one for each model). Recently introduced techniques to estimate (log) marginal likelihoods, such as path sampling and stepping-stone sampling, offer increased accuracy over the traditional harmonic mean estimator at an increased computational cost. Most often, each model's marginal likelihood will be estimated individually, which leads the resulting Bayes factor to suffer from errors associated with each of these independent estimation processes. We here assess the original 'model-switch' path sampling approach for direct Bayes factor estimation in phylogenetics, as well as an extension that uses more samples, to construct a direct path between two competing models, thereby eliminating the need to calculate each model's marginal likelihood independently. Further, we provide a competing Bayes factor estimator using an adaptation of the recently introduced stepping-stone sampling algorithm and set out to determine appropriate settings for accurately calculating such Bayes factors, with context-dependent evolutionary models as an example. While we show that modest efforts are required to roughly identify the increase in model fit, only drastically increased computation times ensure the accuracy needed to detect more subtle details of the evolutionary process. We show that our adaptation of stepping-stone sampling for direct Bayes factor calculation outperforms the original path sampling approach as well as an extension that exploits more samples. Our proposed approach for Bayes factor estimation also has preferable statistical properties over the use of individual marginal likelihood estimates for both models under comparison. Assuming a sigmoid function to determine the path between two competing models, we provide evidence that a single well-chosen sigmoid shape value requires less computational efforts in order to approximate the true value of the (log) Bayes factor compared to the original approach. We show that the (log) Bayes factors calculated using path sampling and stepping-stone sampling differ drastically from those estimated using either of the harmonic mean estimators, supporting earlier claims that the latter systematically overestimate the performance of high-dimensional models, which we show can lead to erroneous conclusions. Based on our results, we argue that highly accurate estimation of differences in model fit for high-dimensional models requires much more computational effort than suggested in recent studies on marginal likelihood estimation.

An Analysis of Computer-Mediated Communication between Middle School Students and Scientist Role Models: A Pilot Study.

ERIC Educational Resources Information Center

Murfin, Brian

1994-01-01

Reports on a study of the effectiveness of computer-mediated communication (CMC) in providing African American and female middle school students with scientist role models. Quantitative and qualitative data gathered by analyzing messages students and scientists posted on a shared electronic bulletin board showed that CMC could be an effective…

EUV/soft x-ray spectra for low B neutron stars

NASA Technical Reports Server (NTRS)

Romani, Roger W.; Rajagopal, Mohan; Rogers, Forrest J.; Iglesias, Carlos A.

1995-01-01

Recent ROSAT and EUVE detections of spin-powered neutron stars suggest that many emit 'thermal' radiation, peaking in the EUV/soft X-ray band. These data constrain the neutron stars' thermal history, but interpretation requires comparison with model atmosphere computations, since emergent spectra depend strongly on the surface composition and magnetic field. As recent opacity computations show substantial change to absorption cross sections at neutron star photospheric conditions, we report here on new model atmosphere computations employing such data. The results are compared with magnetic atmosphere models and applied to PSR J0437-4715, a low field neutron star.

Mind the Noise When Identifying Computational Models of Cognition from Brain Activity.

PubMed

Kolossa, Antonio; Kopp, Bruno

2016-01-01

The aim of this study was to analyze how measurement error affects the validity of modeling studies in computational neuroscience. A synthetic validity test was created using simulated P300 event-related potentials as an example. The model space comprised four computational models of single-trial P300 amplitude fluctuations which differed in terms of complexity and dependency. The single-trial fluctuation of simulated P300 amplitudes was computed on the basis of one of the models, at various levels of measurement error and at various numbers of data points. Bayesian model selection was performed based on exceedance probabilities. At very low numbers of data points, the least complex model generally outperformed the data-generating model. Invalid model identification also occurred at low levels of data quality and under low numbers of data points if the winning model's predictors were closely correlated with the predictors from the data-generating model. Given sufficient data quality and numbers of data points, the data-generating model could be correctly identified, even against models which were very similar to the data-generating model. Thus, a number of variables affects the validity of computational modeling studies, and data quality and numbers of data points are among the main factors relevant to the issue. Further, the nature of the model space (i.e., model complexity, model dependency) should not be neglected. This study provided quantitative results which show the importance of ensuring the validity of computational modeling via adequately prepared studies. The accomplishment of synthetic validity tests is recommended for future applications. Beyond that, we propose to render the demonstration of sufficient validity via adequate simulations mandatory to computational modeling studies.

Colour computer-generated holography for point clouds utilizing the Phong illumination model.

PubMed

Symeonidou, Athanasia; Blinder, David; Schelkens, Peter

2018-04-16

A technique integrating the bidirectional reflectance distribution function (BRDF) is proposed to generate realistic high-quality colour computer-generated holograms (CGHs). We build on prior work, namely a fast computer-generated holography method for point clouds that handles occlusions. We extend the method by integrating the Phong illumination model so that the properties of the objects' surfaces are taken into account to achieve natural light phenomena such as reflections and shadows. Our experiments show that rendering holograms with the proposed algorithm provides realistic looking objects without any noteworthy increase to the computational cost.

Crustal thickness of Antarctica estimated using data from gravimetric satellites

NASA Astrophysics Data System (ADS)

Llubes, Muriel; Seoane, Lucia; Bruinsma, Sean; Rémy, Frédérique

2018-04-01

Computing a better crustal thickness model is still a necessary improvement in Antarctica. In this remote continent where almost all the bedrock is covered by the ice sheet, seismic investigations do not reach a sufficient spatial resolution for geological and geophysical purposes. Here, we present a global map of Antarctic crustal thickness computed from space gravity observations. The DIR5 gravity field model, built from GOCE and GRACE gravimetric data, is inverted with the Parker-Oldenburg iterative algorithm. The BEDMAP products are used to estimate the gravity effect of the ice and the rocky surface. Our result is compared to crustal thickness calculated from seismological studies and the CRUST1.0 and AN1 models. Although the CRUST1.0 model shows a very good agreement with ours, its spatial resolution is larger than the one we obtain with gravimetric data. Finally, we compute a model in which the crust-mantle density contrast is adjusted to fit the Moho depth from the CRUST1.0 model. In East Antarctica, the resulting density contrast clearly shows higher values than in West Antarctica.

Exploration of a physiologically-inspired hearing-aid algorithm using a computer model mimicking impaired hearing.

PubMed

Jürgens, Tim; Clark, Nicholas R; Lecluyse, Wendy; Meddis, Ray

2016-01-01

To use a computer model of impaired hearing to explore the effects of a physiologically-inspired hearing-aid algorithm on a range of psychoacoustic measures. A computer model of a hypothetical impaired listener's hearing was constructed by adjusting parameters of a computer model of normal hearing. Absolute thresholds, estimates of compression, and frequency selectivity (summarized to a hearing profile) were assessed using this model with and without pre-processing the stimuli by a hearing-aid algorithm. The influence of different settings of the algorithm on the impaired profile was investigated. To validate the model predictions, the effect of the algorithm on hearing profiles of human impaired listeners was measured. A computer model simulating impaired hearing (total absence of basilar membrane compression) was used, and three hearing-impaired listeners participated. The hearing profiles of the model and the listeners showed substantial changes when the test stimuli were pre-processed by the hearing-aid algorithm. These changes consisted of lower absolute thresholds, steeper temporal masking curves, and sharper psychophysical tuning curves. The hearing-aid algorithm affected the impaired hearing profile of the model to approximate a normal hearing profile. Qualitatively similar results were found with the impaired listeners' hearing profiles.

Cloud computing task scheduling strategy based on improved differential evolution algorithm

NASA Astrophysics Data System (ADS)

Ge, Junwei; He, Qian; Fang, Yiqiu

2017-04-01

In order to optimize the cloud computing task scheduling scheme, an improved differential evolution algorithm for cloud computing task scheduling is proposed. Firstly, the cloud computing task scheduling model, according to the model of the fitness function, and then used improved optimization calculation of the fitness function of the evolutionary algorithm, according to the evolution of generation of dynamic selection strategy through dynamic mutation strategy to ensure the global and local search ability. The performance test experiment was carried out in the CloudSim simulation platform, the experimental results show that the improved differential evolution algorithm can reduce the cloud computing task execution time and user cost saving, good implementation of the optimal scheduling of cloud computing tasks.

Computing with motile bio-agents

NASA Astrophysics Data System (ADS)

Nicolau, Dan V., Jr.; Burrage, Kevin; Nicolau, Dan V.

2007-12-01

We describe a model of computation of the parallel type, which we call 'computing with bio-agents', based on the concept that motions of biological objects such as bacteria or protein molecular motors in confined spaces can be regarded as computations. We begin with the observation that the geometric nature of the physical structures in which model biological objects move modulates the motions of the latter. Consequently, by changing the geometry, one can control the characteristic trajectories of the objects; on the basis of this, we argue that such systems are computing devices. We investigate the computing power of mobile bio-agent systems and show that they are computationally universal in the sense that they are capable of computing any Boolean function in parallel. We argue also that using appropriate conditions, bio-agent systems can solve NP-complete problems in probabilistic polynomial time.

Computational study of Drucker-Prager plasticity of rock using microtomography

NASA Astrophysics Data System (ADS)

Liu, J.; Sarout, J.; Zhang, M.; Dautriat, J.; Veveakis, M.; Regenauer-Lieb, K.

2016-12-01

Understanding the physics of rocks is essential for the industry of mining and petroleum. Microtomography provides a new way to quantify the relationship between the microstructure and their mechanical and transport properties. Transport and elastic properties have been studied widely while plastic properties are still poorly understood. In this study, we analyse a synthetic sandstone sample for its up-scaled plastic properties from the micro-scale. The computations are based on the representative volume element (RVE). The mechanical RVE was determined by the upper and lower bound finite element computations of elasticity. By comparing with experimental curves, the parameters of the matrix (solid part), which consists of calcite-cemented quartz grains, were investigated and quite accurate values obtained. Analyses deduced the bulk properties of yield stress, cohesion and the angle of friction of the rock with pores. Computations of a series of models of volume-sizes from 240-cube to 400-cube showed almost overlapped stress-strain curves, suggesting that the mechanical RVE determined by elastic computations is valid for plastic yielding. Furthermore, a series of derivative models were created which have similar structure but different porosity values. The analyses of these models showed that yield stress, cohesion and the angle of friction linearly decrease with the porosity increasing in the range of porosity from 8% to 28%. The angle of friction decreases the fastest and cohesion shows the most stable along with porosity.

A conduction velocity adapted eikonal model for electrophysiology problems with re-excitability evaluation.

PubMed

Corrado, Cesare; Zemzemi, Nejib

2018-01-01

Computational models of heart electrophysiology achieved a considerable interest in the medical community as they represent a novel framework for the study of the mechanisms underpinning heart pathologies. The high demand of computational resources and the long computational time required to evaluate the model solution hamper the use of detailed computational models in clinical applications. In this paper, we present a multi-front eikonal algorithm that adapts the conduction velocity (CV) to the activation frequency of the tissue substrate. We then couple the eikonal new algorithm with the Mitchell-Schaeffer (MS) ionic model to determine the tissue electrical state. Compared to the standard eikonal model, this model introduces three novelties: first, it evaluates the local value of the transmembrane potential and of the ionic variable solving an ionic model; second, it computes the action potential duration (APD) and the diastolic interval (DI) from the solution of the MS model and uses them to determine if the tissue is locally re-excitable; third, it adapts the CV to the underpinning electrophysiological state through an analytical expression of the CV restitution and the computed local DI. We conduct series of simulations on a 3D tissue slab and on a realistic heart geometry and compare the solutions with those obtained solving the monodomain equation. Our results show that the new model is significantly more accurate than the standard eikonal model. The proposed model enables the numerical simulation of the heart electrophysiology on a clinical time scale and thus constitutes a viable model candidate for computer-guided radio-frequency ablation. Copyright © 2017 Elsevier B.V. All rights reserved.

Development of a Microsoft Excel tool for one-parameter Rasch model of continuous items: an application to a safety attitude survey.

PubMed

Chien, Tsair-Wei; Shao, Yang; Kuo, Shu-Chun

2017-01-10

Many continuous item responses (CIRs) are encountered in healthcare settings, but no one uses item response theory's (IRT) probabilistic modeling to present graphical presentations for interpreting CIR results. A computer module that is programmed to deal with CIRs is required. To present a computer module, validate it, and verify its usefulness in dealing with CIR data, and then to apply the model to real healthcare data in order to show how the CIR that can be applied to healthcare settings with an example regarding a safety attitude survey. Using Microsoft Excel VBA (Visual Basic for Applications), we designed a computer module that minimizes the residuals and calculates model's expected scores according to person responses across items. Rasch models based on a Wright map and on KIDMAP were demonstrated to interpret results of the safety attitude survey. The author-made CIR module yielded OUTFIT mean square (MNSQ) and person measures equivalent to those yielded by professional Rasch Winsteps software. The probabilistic modeling of the CIR module provides messages that are much more valuable to users and show the CIR advantage over classic test theory. Because of advances in computer technology, healthcare users who are familiar to MS Excel can easily apply the study CIR module to deal with continuous variables to benefit comparisons of data with a logistic distribution and model fit statistics.

Digital model analysis of the principal artesian aquifer, Savannah, Georgia area

USGS Publications Warehouse

Counts, H.B.; Krause, R.E.

1977-01-01

A digital model of the principal artesian aquifer has been developed for the Savannah, Georgia, area. The model simulates the response of the aquifer system to various hydrologic stresses. Model results of the water levels and water-level changes are shown on maps. Computations may be extended in time, indicating changes in pumpage were applied to the system and probable results calculated. Drawdown or water-level differences were computed, showing comparisons of different water management alternatives. (Woodard-USGS)

Structure, function, and behaviour of computational models in systems biology

PubMed Central

2013-01-01

Background Systems Biology develops computational models in order to understand biological phenomena. The increasing number and complexity of such “bio-models” necessitate computer support for the overall modelling task. Computer-aided modelling has to be based on a formal semantic description of bio-models. But, even if computational bio-models themselves are represented precisely in terms of mathematical expressions their full meaning is not yet formally specified and only described in natural language. Results We present a conceptual framework – the meaning facets – which can be used to rigorously specify the semantics of bio-models. A bio-model has a dual interpretation: On the one hand it is a mathematical expression which can be used in computational simulations (intrinsic meaning). On the other hand the model is related to the biological reality (extrinsic meaning). We show that in both cases this interpretation should be performed from three perspectives: the meaning of the model’s components (structure), the meaning of the model’s intended use (function), and the meaning of the model’s dynamics (behaviour). In order to demonstrate the strengths of the meaning facets framework we apply it to two semantically related models of the cell cycle. Thereby, we make use of existing approaches for computer representation of bio-models as much as possible and sketch the missing pieces. Conclusions The meaning facets framework provides a systematic in-depth approach to the semantics of bio-models. It can serve two important purposes: First, it specifies and structures the information which biologists have to take into account if they build, use and exchange models. Secondly, because it can be formalised, the framework is a solid foundation for any sort of computer support in bio-modelling. The proposed conceptual framework establishes a new methodology for modelling in Systems Biology and constitutes a basis for computer-aided collaborative research. PMID:23721297

Spin-neurons: A possible path to energy-efficient neuromorphic computers

NASA Astrophysics Data System (ADS)

Sharad, Mrigank; Fan, Deliang; Roy, Kaushik

2013-12-01

Recent years have witnessed growing interest in the field of brain-inspired computing based on neural-network architectures. In order to translate the related algorithmic models into powerful, yet energy-efficient cognitive-computing hardware, computing-devices beyond CMOS may need to be explored. The suitability of such devices to this field of computing would strongly depend upon how closely their physical characteristics match with the essential computing primitives employed in such models. In this work, we discuss the rationale of applying emerging spin-torque devices for bio-inspired computing. Recent spin-torque experiments have shown the path to low-current, low-voltage, and high-speed magnetization switching in nano-scale magnetic devices. Such magneto-metallic, current-mode spin-torque switches can mimic the analog summing and "thresholding" operation of an artificial neuron with high energy-efficiency. Comparison with CMOS-based analog circuit-model of a neuron shows that "spin-neurons" (spin based circuit model of neurons) can achieve more than two orders of magnitude lower energy and beyond three orders of magnitude reduction in energy-delay product. The application of spin-neurons can therefore be an attractive option for neuromorphic computers of future.

Spin-neurons: A possible path to energy-efficient neuromorphic computers

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

Sharad, Mrigank; Fan, Deliang; Roy, Kaushik

Recent years have witnessed growing interest in the field of brain-inspired computing based on neural-network architectures. In order to translate the related algorithmic models into powerful, yet energy-efficient cognitive-computing hardware, computing-devices beyond CMOS may need to be explored. The suitability of such devices to this field of computing would strongly depend upon how closely their physical characteristics match with the essential computing primitives employed in such models. In this work, we discuss the rationale of applying emerging spin-torque devices for bio-inspired computing. Recent spin-torque experiments have shown the path to low-current, low-voltage, and high-speed magnetization switching in nano-scale magnetic devices.more » Such magneto-metallic, current-mode spin-torque switches can mimic the analog summing and “thresholding” operation of an artificial neuron with high energy-efficiency. Comparison with CMOS-based analog circuit-model of a neuron shows that “spin-neurons” (spin based circuit model of neurons) can achieve more than two orders of magnitude lower energy and beyond three orders of magnitude reduction in energy-delay product. The application of spin-neurons can therefore be an attractive option for neuromorphic computers of future.« less

The application of virtual reality systems as a support of digital manufacturing and logistics

NASA Astrophysics Data System (ADS)

Golda, G.; Kampa, A.; Paprocka, I.

2016-08-01

Modern trends in development of computer aided techniques are heading toward the integration of design competitive products and so-called "digital manufacturing and logistics", supported by computer simulation software. All phases of product lifecycle: starting from design of a new product, through planning and control of manufacturing, assembly, internal logistics and repairs, quality control, distribution to customers and after-sale service, up to its recycling or utilization should be aided and managed by advanced packages of product lifecycle management software. Important problems for providing the efficient flow of materials in supply chain management of whole product lifecycle, using computer simulation will be described on that paper. Authors will pay attention to the processes of acquiring relevant information and correct data, necessary for virtual modeling and computer simulation of integrated manufacturing and logistics systems. The article describes possibilities of use an applications of virtual reality software for modeling and simulation the production and logistics processes in enterprise in different aspects of product lifecycle management. The authors demonstrate effective method of creating computer simulations for digital manufacturing and logistics and show modeled and programmed examples and solutions. They pay attention to development trends and show options of the applications that go beyond enterprise.

Make the most of your samples: Bayes factor estimators for high-dimensional models of sequence evolution

PubMed Central

2013-01-01

Background Accurate model comparison requires extensive computation times, especially for parameter-rich models of sequence evolution. In the Bayesian framework, model selection is typically performed through the evaluation of a Bayes factor, the ratio of two marginal likelihoods (one for each model). Recently introduced techniques to estimate (log) marginal likelihoods, such as path sampling and stepping-stone sampling, offer increased accuracy over the traditional harmonic mean estimator at an increased computational cost. Most often, each model’s marginal likelihood will be estimated individually, which leads the resulting Bayes factor to suffer from errors associated with each of these independent estimation processes. Results We here assess the original ‘model-switch’ path sampling approach for direct Bayes factor estimation in phylogenetics, as well as an extension that uses more samples, to construct a direct path between two competing models, thereby eliminating the need to calculate each model’s marginal likelihood independently. Further, we provide a competing Bayes factor estimator using an adaptation of the recently introduced stepping-stone sampling algorithm and set out to determine appropriate settings for accurately calculating such Bayes factors, with context-dependent evolutionary models as an example. While we show that modest efforts are required to roughly identify the increase in model fit, only drastically increased computation times ensure the accuracy needed to detect more subtle details of the evolutionary process. Conclusions We show that our adaptation of stepping-stone sampling for direct Bayes factor calculation outperforms the original path sampling approach as well as an extension that exploits more samples. Our proposed approach for Bayes factor estimation also has preferable statistical properties over the use of individual marginal likelihood estimates for both models under comparison. Assuming a sigmoid function to determine the path between two competing models, we provide evidence that a single well-chosen sigmoid shape value requires less computational efforts in order to approximate the true value of the (log) Bayes factor compared to the original approach. We show that the (log) Bayes factors calculated using path sampling and stepping-stone sampling differ drastically from those estimated using either of the harmonic mean estimators, supporting earlier claims that the latter systematically overestimate the performance of high-dimensional models, which we show can lead to erroneous conclusions. Based on our results, we argue that highly accurate estimation of differences in model fit for high-dimensional models requires much more computational effort than suggested in recent studies on marginal likelihood estimation. PMID:23497171

Realizing the Promise of Visualization in the Theory of Computing

ERIC Educational Resources Information Center

Cogliati, Joshua J.; Goosey, Frances W.; Grinder, Michael T.; Pascoe, Bradley A.; Ross, Rockford J.; Williams, Cheston J.

2005-01-01

Progress on a hypertextbook on the theory of computing is presented. The hypertextbook is a novel teaching and learning resource built around web technologies that incorporates text, sound, pictures, illustrations, slide shows, video clips, and--most importantly--active learning models of the key concepts of the theory of computing into an…

The Computer, the Discipline and the Classroom: Two Perspectives.

ERIC Educational Resources Information Center

Thurber, Bart; Pope, Jack

The authors present two case studies in the use of computers in the classroom, one involving an introductory computer science class, the other an upper division literature class. After describing each case, the differences are discussed, showing that pedagogical models developed for one discipline may not transfer to another, and that the…

Navier-Stokes Computations With One-Equation Turbulence Model for Flows Along Concave Wall Surfaces

NASA Technical Reports Server (NTRS)

Wang, Chi R.

2005-01-01

This report presents the use of a time-marching three-dimensional compressible Navier-Stokes equation numerical solver with a one-equation turbulence model to simulate the flow fields developed along concave wall surfaces without and with a downstream extension flat wall surface. The 3-D Navier- Stokes numerical solver came from the NASA Glenn-HT code. The one-equation turbulence model was derived from the Spalart and Allmaras model. The computational approach was first calibrated with the computations of the velocity and Reynolds shear stress profiles of a steady flat plate boundary layer flow. The computational approach was then used to simulate developing boundary layer flows along concave wall surfaces without and with a downstream extension wall. The author investigated the computational results of surface friction factors, near surface velocity components, near wall temperatures, and a turbulent shear stress component in terms of turbulence modeling, computational mesh configurations, inlet turbulence level, and time iteration step. The computational results were compared with existing measurements of skin friction factors, velocity components, and shear stresses of the developing boundary layer flows. With a fine computational mesh and a one-equation model, the computational approach could predict accurately the skin friction factors, near surface velocity and temperature, and shear stress within the flows. The computed velocity components and shear stresses also showed the vortices effect on the velocity variations over a concave wall. The computed eddy viscosities at the near wall locations were also compared with the results from a two equation turbulence modeling technique. The inlet turbulence length scale was found to have little effect on the eddy viscosities at locations near the concave wall surface. The eddy viscosities, from the one-equation and two-equation modeling, were comparable at most stream-wise stations. The present one-equation turbulence model is an effective approach for turbulence modeling in the near solid wall surface region of flow over a concave wall.

Agricultural soil moisture experiment, Colby, Kansas 1978: Measured and predicted hydrological properties of the soil

NASA Technical Reports Server (NTRS)

Arya, L. M. (Principal Investigator)

1980-01-01

Predictive procedures for developing soil hydrologic properties (i.e., relationships of soil water pressure and hydraulic conductivity to soil water content) are presented. Three models of the soil water pressure-water content relationship and one model of the hydraulic conductivity-water content relationship are discussed. Input requirements for the models are indicated, and computational procedures are outlined. Computed hydrologic properties for Keith silt loam, a soil typer near Colby, Kansas, on which the 1978 Agricultural Soil Moisture Experiment was conducted, are presented. A comparison of computed results with experimental data in the dry range shows that analytical models utilizing a few basic hydrophysical parameters can produce satisfactory data for large-scale applications.

Developing a particle tracking surrogate model to improve inversion of ground water - Surface water models

NASA Astrophysics Data System (ADS)

Cousquer, Yohann; Pryet, Alexandre; Atteia, Olivier; Ferré, Ty P. A.; Delbart, Célestine; Valois, Rémi; Dupuy, Alain

2018-03-01

The inverse problem of groundwater models is often ill-posed and model parameters are likely to be poorly constrained. Identifiability is improved if diverse data types are used for parameter estimation. However, some models, including detailed solute transport models, are further limited by prohibitive computation times. This often precludes the use of concentration data for parameter estimation, even if those data are available. In the case of surface water-groundwater (SW-GW) models, concentration data can provide SW-GW mixing ratios, which efficiently constrain the estimate of exchange flow, but are rarely used. We propose to reduce computational limits by simulating SW-GW exchange at a sink (well or drain) based on particle tracking under steady state flow conditions. Particle tracking is used to simulate advective transport. A comparison between the particle tracking surrogate model and an advective-dispersive model shows that dispersion can often be neglected when the mixing ratio is computed for a sink, allowing for use of the particle tracking surrogate model. The surrogate model was implemented to solve the inverse problem for a real SW-GW transport problem with heads and concentrations combined in a weighted hybrid objective function. The resulting inversion showed markedly reduced uncertainty in the transmissivity field compared to calibration on head data alone.

A 3D staggered-grid finite difference scheme for poroelastic wave equation

NASA Astrophysics Data System (ADS)

Zhang, Yijie; Gao, Jinghuai

2014-10-01

Three dimensional numerical modeling has been a viable tool for understanding wave propagation in real media. The poroelastic media can better describe the phenomena of hydrocarbon reservoirs than acoustic and elastic media. However, the numerical modeling in 3D poroelastic media demands significantly more computational capacity, including both computational time and memory. In this paper, we present a 3D poroelastic staggered-grid finite difference (SFD) scheme. During the procedure, parallel computing is implemented to reduce the computational time. Parallelization is based on domain decomposition, and communication between processors is performed using message passing interface (MPI). Parallel analysis shows that the parallelized SFD scheme significantly improves the simulation efficiency and 3D decomposition in domain is the most efficient. We also analyze the numerical dispersion and stability condition of the 3D poroelastic SFD method. Numerical results show that the 3D numerical simulation can provide a real description of wave propagation.

A neuronal network model with simplified tonotopicity for tinnitus generation and its relief by sound therapy.

PubMed

Nagashino, Hirofumi; Kinouchi, Yohsuke; Danesh, Ali A; Pandya, Abhijit S

2013-01-01

Tinnitus is the perception of sound in the ears or in the head where no external source is present. Sound therapy is one of the most effective techniques for tinnitus treatment that have been proposed. In order to investigate mechanisms of tinnitus generation and the clinical effects of sound therapy, we have proposed conceptual and computational models with plasticity using a neural oscillator or a neuronal network model. In the present paper, we propose a neuronal network model with simplified tonotopicity of the auditory system as more detailed structure. In this model an integrate-and-fire neuron model is employed and homeostatic plasticity is incorporated. The computer simulation results show that the present model can show the generation of oscillation and its cessation by external input. It suggests that the present framework is promising as a modeling for the tinnitus generation and the effects of sound therapy.

Mixed Models and Reduction Techniques for Large-Rotation, Nonlinear Analysis of Shells of Revolution with Application to Tires

NASA Technical Reports Server (NTRS)

Noor, A. K.; Andersen, C. M.; Tanner, J. A.

1984-01-01

An effective computational strategy is presented for the large-rotation, nonlinear axisymmetric analysis of shells of revolution. The three key elements of the computational strategy are: (1) use of mixed finite-element models with discontinuous stress resultants at the element interfaces; (2) substantial reduction in the total number of degrees of freedom through the use of a multiple-parameter reduction technique; and (3) reduction in the size of the analysis model through the decomposition of asymmetric loads into symmetric and antisymmetric components coupled with the use of the multiple-parameter reduction technique. The potential of the proposed computational strategy is discussed. Numerical results are presented to demonstrate the high accuracy of the mixed models developed and to show the potential of using the proposed computational strategy for the analysis of tires.

A local-circulation model for Darrieus vertical-axis wind turbines

NASA Astrophysics Data System (ADS)

Masse, B.

1986-04-01

A new computational model for the aerodynamics of the vertical-axis wind turbine is presented. Based on the local-circulation method generalized for curved blades, combined with a wake model for the vertical-axis wind turbine, it differs markedly from current models based on variations in the streamtube momentum and vortex models using the lifting-line theory. A computer code has been developed to calculate the loads and performance of the Darrieus vertical-axis wind turbine. The results show good agreement with experimental data and compare well with other methods.

A Study of Grid Resolution, Transition and Turbulence Model Using the Transonic Simple Straked Delta Wing

NASA Technical Reports Server (NTRS)

Bartels, Robert E.

2001-01-01

Three-dimensional transonic flow over a delta wing is investigated using several turbulence models. The performance of linear eddy viscosity models and an explicit algebraic stress model is assessed at the start of vortex flow, and the results compared with experimental data. To assess the effect of transition location, computations that either fix transition aft of the leading edge or are fully turbulent are performed. These computations show that grid resolution, transition location and turbulence model significantly affect the 3D flowfield.

Time-domain seismic modeling in viscoelastic media for full waveform inversion on heterogeneous computing platforms with OpenCL

NASA Astrophysics Data System (ADS)

Fabien-Ouellet, Gabriel; Gloaguen, Erwan; Giroux, Bernard

2017-03-01

Full Waveform Inversion (FWI) aims at recovering the elastic parameters of the Earth by matching recordings of the ground motion with the direct solution of the wave equation. Modeling the wave propagation for realistic scenarios is computationally intensive, which limits the applicability of FWI. The current hardware evolution brings increasing parallel computing power that can speed up the computations in FWI. However, to take advantage of the diversity of parallel architectures presently available, new programming approaches are required. In this work, we explore the use of OpenCL to develop a portable code that can take advantage of the many parallel processor architectures now available. We present a program called SeisCL for 2D and 3D viscoelastic FWI in the time domain. The code computes the forward and adjoint wavefields using finite-difference and outputs the gradient of the misfit function given by the adjoint state method. To demonstrate the code portability on different architectures, the performance of SeisCL is tested on three different devices: Intel CPUs, NVidia GPUs and Intel Xeon PHI. Results show that the use of GPUs with OpenCL can speed up the computations by nearly two orders of magnitudes over a single threaded application on the CPU. Although OpenCL allows code portability, we show that some device-specific optimization is still required to get the best performance out of a specific architecture. Using OpenCL in conjunction with MPI allows the domain decomposition of large models on several devices located on different nodes of a cluster. For large enough models, the speedup of the domain decomposition varies quasi-linearly with the number of devices. Finally, we investigate two different approaches to compute the gradient by the adjoint state method and show the significant advantages of using OpenCL for FWI.

A baseline-free procedure for transformation models under interval censorship.

PubMed

Gu, Ming Gao; Sun, Liuquan; Zuo, Guoxin

2005-12-01

An important property of Cox regression model is that the estimation of regression parameters using the partial likelihood procedure does not depend on its baseline survival function. We call such a procedure baseline-free. Using marginal likelihood, we show that an baseline-free procedure can be derived for a class of general transformation models under interval censoring framework. The baseline-free procedure results a simplified and stable computation algorithm for some complicated and important semiparametric models, such as frailty models and heteroscedastic hazard/rank regression models, where the estimation procedures so far available involve estimation of the infinite dimensional baseline function. A detailed computational algorithm using Markov Chain Monte Carlo stochastic approximation is presented. The proposed procedure is demonstrated through extensive simulation studies, showing the validity of asymptotic consistency and normality. We also illustrate the procedure with a real data set from a study of breast cancer. A heuristic argument showing that the score function is a mean zero martingale is provided.

A Web-Based Computer-Aided Learning Module for an Anatomy Course Using Open Source Image Mapping Software

ERIC Educational Resources Information Center

Carleton, Renee E.

2012-01-01

Computer-aided learning (CAL) is used increasingly to teach anatomy in post-secondary programs. Studies show that augmentation of traditional cadaver dissection and model examination by CAL can be associated with positive student learning outcomes. In order to reduce costs associated with the purchase of skeletons and models and to encourage study…

A Path Analysis of Pre-Service Teachers' Attitudes to Computer Use: Applying and Extending the Technology Acceptance Model in an Educational Context

ERIC Educational Resources Information Center

Teo, Timothy

2010-01-01

The purpose of this study is to examine pre-service teachers' attitudes to computers. This study extends the technology acceptance model (TAM) framework by adding subjective norm, facilitating conditions, and technological complexity as external variables. Results show that the TAM and subjective norm, facilitating conditions, and technological…

Computational neurorehabilitation: modeling plasticity and learning to predict recovery.

PubMed

Reinkensmeyer, David J; Burdet, Etienne; Casadio, Maura; Krakauer, John W; Kwakkel, Gert; Lang, Catherine E; Swinnen, Stephan P; Ward, Nick S; Schweighofer, Nicolas

2016-04-30

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling - regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity.

Does attitude matter in computer use in Australian general practice? A zero-inflated Poisson regression analysis.

PubMed

Khan, Asaduzzaman; Western, Mark

The purpose of this study was to explore factors that facilitate or hinder effective use of computers in Australian general medical practice. This study is based on data extracted from a national telephone survey of 480 general practitioners (GPs) across Australia. Clinical functions performed by GPs using computers were examined using a zero-inflated Poisson (ZIP) regression modelling. About 17% of GPs were not using computer for any clinical function, while 18% reported using computers for all clinical functions. The ZIP model showed that computer anxiety was negatively associated with effective computer use, while practitioners' belief about usefulness of computers was positively associated with effective computer use. Being a female GP or working in partnership or group practice increased the odds of effectively using computers for clinical functions. To fully capitalise on the benefits of computer technology, GPs need to be convinced that this technology is useful and can make a difference.

Rotorcraft Transmission Noise Path Model, Including Distributed Fluid Film Bearing Impedance Modeling

NASA Technical Reports Server (NTRS)

Hambric, Stephen A.; Hanford, Amanda D.; Shepherd, Micah R.; Campbell, Robert L.; Smith, Edward C.

2010-01-01

A computational approach for simulating the effects of rolling element and journal bearings on the vibration and sound transmission through gearboxes has been demonstrated. The approach, using ARL/Penn State s CHAMP methodology, uses Component Mode Synthesis of housing and shafting modes computed using Finite Element (FE) models to allow for rapid adjustment of bearing impedances in gearbox models. The approach has been demonstrated on NASA GRC s test gearbox with three different bearing configurations: in the first condition, traditional rolling element (ball and roller) bearings were installed, and in the second and third conditions, the traditional bearings were replaced with journal and wave bearings (wave bearings are journal bearings with a multi-lobed wave pattern on the bearing surface). A methodology for computing the stiffnesses and damping in journal and wave bearings has been presented, and demonstrated for the journal and wave bearings used in the NASA GRC test gearbox. The FE model of the gearbox, along with the rolling element bearing coupling impedances, was analyzed to compute dynamic transfer functions between forces applied to the meshing gears and accelerations on the gearbox housing, including several locations near the bearings. A Boundary Element (BE) acoustic model was used to compute the sound radiated by the gearbox. Measurements of the Gear Mesh Frequency (GMF) tones were made by NASA GRC at several operational speeds for the rolling element and journal bearing gearbox configurations. Both the measurements and the CHAMP numerical model indicate that the journal bearings reduce vibration and noise for the second harmonic of the gear meshing tones, but show no clear benefit to using journal bearings to reduce the amplitudes of the fundamental gear meshing tones. Also, the numerical model shows that the gearbox vibrations and radiated sound are similar for journal and wave bearing configurations.

CPMIP: measurements of real computational performance of Earth system models in CMIP6

NASA Astrophysics Data System (ADS)

Balaji, Venkatramani; Maisonnave, Eric; Zadeh, Niki; Lawrence, Bryan N.; Biercamp, Joachim; Fladrich, Uwe; Aloisio, Giovanni; Benson, Rusty; Caubel, Arnaud; Durachta, Jeffrey; Foujols, Marie-Alice; Lister, Grenville; Mocavero, Silvia; Underwood, Seth; Wright, Garrett

2017-01-01

A climate model represents a multitude of processes on a variety of timescales and space scales: a canonical example of multi-physics multi-scale modeling. The underlying climate system is physically characterized by sensitive dependence on initial conditions, and natural stochastic variability, so very long integrations are needed to extract signals of climate change. Algorithms generally possess weak scaling and can be I/O and/or memory-bound. Such weak-scaling, I/O, and memory-bound multi-physics codes present particular challenges to computational performance. Traditional metrics of computational efficiency such as performance counters and scaling curves do not tell us enough about real sustained performance from climate models on different machines. They also do not provide a satisfactory basis for comparative information across models. codes present particular challenges to computational performance. We introduce a set of metrics that can be used for the study of computational performance of climate (and Earth system) models. These measures do not require specialized software or specific hardware counters, and should be accessible to anyone. They are independent of platform and underlying parallel programming models. We show how these metrics can be used to measure actually attained performance of Earth system models on different machines, and identify the most fruitful areas of research and development for performance engineering. codes present particular challenges to computational performance. We present results for these measures for a diverse suite of models from several modeling centers, and propose to use these measures as a basis for a CPMIP, a computational performance model intercomparison project (MIP).

Quantum lattice model solver HΦ

NASA Astrophysics Data System (ADS)

Kawamura, Mitsuaki; Yoshimi, Kazuyoshi; Misawa, Takahiro; Yamaji, Youhei; Todo, Synge; Kawashima, Naoki

2017-08-01

HΦ [aitch-phi ] is a program package based on the Lanczos-type eigenvalue solution applicable to a broad range of quantum lattice models, i.e., arbitrary quantum lattice models with two-body interactions, including the Heisenberg model, the Kitaev model, the Hubbard model and the Kondo-lattice model. While it works well on PCs and PC-clusters, HΦ also runs efficiently on massively parallel computers, which considerably extends the tractable range of the system size. In addition, unlike most existing packages, HΦ supports finite-temperature calculations through the method of thermal pure quantum (TPQ) states. In this paper, we explain theoretical background and user-interface of HΦ. We also show the benchmark results of HΦ on supercomputers such as the K computer at RIKEN Advanced Institute for Computational Science (AICS) and SGI ICE XA (Sekirei) at the Institute for the Solid State Physics (ISSP).

Turbulence modeling of free shear layers for high performance aircraft

NASA Technical Reports Server (NTRS)

Sondak, Douglas

1993-01-01

In many flowfield computations, accuracy of the turbulence model employed is frequently a limiting factor in the overall accuracy of the computation. This is particularly true for complex flowfields such as those around full aircraft configurations. Free shear layers such as wakes, impinging jets (in V/STOL applications), and mixing layers over cavities are often part of these flowfields. Although flowfields have been computed for full aircraft, the memory and CPU requirements for these computations are often excessive. Additional computer power is required for multidisciplinary computations such as coupled fluid dynamics and conduction heat transfer analysis. Massively parallel computers show promise in alleviating this situation, and the purpose of this effort was to adapt and optimize CFD codes to these new machines. The objective of this research effort was to compute the flowfield and heat transfer for a two-dimensional jet impinging normally on a cool plate. The results of this research effort were summarized in an AIAA paper titled 'Parallel Implementation of the k-epsilon Turbulence Model'. Appendix A contains the full paper.

SPARSE—A subgrid particle averaged Reynolds stress equivalent model: testing with a priori closure

PubMed Central

Davis, Sean L.; Sen, Oishik; Udaykumar, H. S.

2017-01-01

A Lagrangian particle cloud model is proposed that accounts for the effects of Reynolds-averaged particle and turbulent stresses and the averaged carrier-phase velocity of the subparticle cloud scale on the averaged motion and velocity of the cloud. The SPARSE (subgrid particle averaged Reynolds stress equivalent) model is based on a combination of a truncated Taylor expansion of a drag correction function and Reynolds averaging. It reduces the required number of computational parcels to trace a cloud of particles in Eulerian–Lagrangian methods for the simulation of particle-laden flow. Closure is performed in an a priori manner using a reference simulation where all particles in the cloud are traced individually with a point-particle model. Comparison of a first-order model and SPARSE with the reference simulation in one dimension shows that both the stress and the averaging of the carrier-phase velocity on the cloud subscale affect the averaged motion of the particle. A three-dimensional isotropic turbulence computation shows that only one computational parcel is sufficient to accurately trace a cloud of tens of thousands of particles. PMID:28413341

SPARSE-A subgrid particle averaged Reynolds stress equivalent model: testing with a priori closure.

PubMed

Davis, Sean L; Jacobs, Gustaaf B; Sen, Oishik; Udaykumar, H S

2017-03-01

A Lagrangian particle cloud model is proposed that accounts for the effects of Reynolds-averaged particle and turbulent stresses and the averaged carrier-phase velocity of the subparticle cloud scale on the averaged motion and velocity of the cloud. The SPARSE (subgrid particle averaged Reynolds stress equivalent) model is based on a combination of a truncated Taylor expansion of a drag correction function and Reynolds averaging. It reduces the required number of computational parcels to trace a cloud of particles in Eulerian-Lagrangian methods for the simulation of particle-laden flow. Closure is performed in an a priori manner using a reference simulation where all particles in the cloud are traced individually with a point-particle model. Comparison of a first-order model and SPARSE with the reference simulation in one dimension shows that both the stress and the averaging of the carrier-phase velocity on the cloud subscale affect the averaged motion of the particle. A three-dimensional isotropic turbulence computation shows that only one computational parcel is sufficient to accurately trace a cloud of tens of thousands of particles.

Multi-Scale Modeling to Improve Single-Molecule, Single-Cell Experiments

NASA Astrophysics Data System (ADS)

Munsky, Brian; Shepherd, Douglas

2014-03-01

Single-cell, single-molecule experiments are producing an unprecedented amount of data to capture the dynamics of biological systems. When integrated with computational models, observations of spatial, temporal and stochastic fluctuations can yield powerful quantitative insight. We concentrate on experiments that localize and count individual molecules of mRNA. These high precision experiments have large imaging and computational processing costs, and we explore how improved computational analyses can dramatically reduce overall data requirements. In particular, we show how analyses of spatial, temporal and stochastic fluctuations can significantly enhance parameter estimation results for small, noisy data sets. We also show how full probability distribution analyses can constrain parameters with far less data than bulk analyses or statistical moment closures. Finally, we discuss how a systematic modeling progression from simple to more complex analyses can reduce total computational costs by orders of magnitude. We illustrate our approach using single-molecule, spatial mRNA measurements of Interleukin 1-alpha mRNA induction in human THP1 cells following stimulation. Our approach could improve the effectiveness of single-molecule gene regulation analyses for many other process.

Prediction of resource volumes at untested locations using simple local prediction models

USGS Publications Warehouse

Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.

2006-01-01

This paper shows how local spatial nonparametric prediction models can be applied to estimate volumes of recoverable gas resources at individual undrilled sites, at multiple sites on a regional scale, and to compute confidence bounds for regional volumes based on the distribution of those estimates. An approach that combines cross-validation, the jackknife, and bootstrap procedures is used to accomplish this task. Simulation experiments show that cross-validation can be applied beneficially to select an appropriate prediction model. The cross-validation procedure worked well for a wide range of different states of nature and levels of information. Jackknife procedures are used to compute individual prediction estimation errors at undrilled locations. The jackknife replicates also are used with a bootstrap resampling procedure to compute confidence bounds for the total volume. The method was applied to data (partitioned into a training set and target set) from the Devonian Antrim Shale continuous-type gas play in the Michigan Basin in Otsego County, Michigan. The analysis showed that the model estimate of total recoverable volumes at prediction sites is within 4 percent of the total observed volume. The model predictions also provide frequency distributions of the cell volumes at the production unit scale. Such distributions are the basis for subsequent economic analyses. ?? Springer Science+Business Media, LLC 2007.

Reciprocity in computer-human interaction: source-based, norm-based, and affect-based explanations.

PubMed

Lee, Seungcheol Austin; Liang, Yuhua Jake

2015-04-01

Individuals often apply social rules when they interact with computers, and this is known as the Computers Are Social Actors (CASA) effect. Following previous work, one approach to understand the mechanism responsible for CASA is to utilize computer agents and have the agents attempt to gain human compliance (e.g., completing a pattern recognition task). The current study focuses on three key factors frequently cited to influence traditional notions of compliance: evaluations toward the source (competence and warmth), normative influence (reciprocity), and affective influence (mood). Structural equation modeling assessed the effects of these factors on human compliance with computer request. The final model shows that norm-based influence (reciprocity) increased the likelihood of compliance, while evaluations toward the computer agent did not significantly influence compliance.

Quantum computation with indefinite causal structures

NASA Astrophysics Data System (ADS)

Araújo, Mateus; Guérin, Philippe Allard; Baumeler, ńmin

2017-11-01

One way to study the physical plausibility of closed timelike curves (CTCs) is to examine their computational power. This has been done for Deutschian CTCs (D-CTCs) and postselection CTCs (P-CTCs), with the result that they allow for the efficient solution of problems in PSPACE and PP, respectively. Since these are extremely powerful complexity classes, which are not expected to be solvable in reality, this can be taken as evidence that these models for CTCs are pathological. This problem is closely related to the nonlinearity of this models, which also allows, for example, cloning quantum states, in the case of D-CTCs, or distinguishing nonorthogonal quantum states, in the case of P-CTCs. In contrast, the process matrix formalism allows one to model indefinite causal structures in a linear way, getting rid of these effects and raising the possibility that its computational power is rather tame. In this paper, we show that process matrices correspond to a linear particular case of P-CTCs, and therefore that its computational power is upperbounded by that of PP. We show, furthermore, a family of processes that can violate causal inequalities but nevertheless can be simulated by a causally ordered quantum circuit with only a constant overhead, showing that indefinite causality is not necessarily hard to simulate.

Clinical Pilot Study and Computational Modeling of Bitemporal Transcranial Direct Current Stimulation, and Safety of Repeated Courses of Treatment, in Major Depression.

PubMed

Ho, Kerrie-Anne; Bai, Siwei; Martin, Donel; Alonzo, Angelo; Dokos, Socrates; Loo, Colleen K

2015-12-01

This study aimed to examine a bitemporal (BT) transcranial direct current stimulation (tDCS) electrode montage for the treatment of depression through a clinical pilot study and computational modeling. The safety of repeated courses of stimulation was also examined. Four participants with depression who had previously received multiple courses of tDCS received a 4-week course of BT tDCS. Mood and neuropsychological function were assessed. The results were compared with previous courses of tDCS given to the same participants using different electrode montages. Computational modeling examined the electric field maps produced by the different montages. Three participants showed clinical improvement with BT tDCS (mean [SD] improvement, 49.6% [33.7%]). There were no adverse neuropsychological effects. Computational modeling showed that the BT montage activates the anterior cingulate cortices and brainstem, which are deep brain regions that are important for depression. However, a fronto-extracephalic montage stimulated these areas more effectively. No adverse effects were found in participants receiving up to 6 courses of tDCS. Bitemporal tDCS was safe and led to clinically meaningful efficacy in 3 of 4 participants. However, computational modeling suggests that the BT montage may not activate key brain regions in depression more effectively than another novel montage--fronto-extracephalic tDCS. There is also preliminary evidence to support the safety of up to 6 repeated courses of tDCS.

A Model of In vitro Plasticity at the Parallel Fiber—Molecular Layer Interneuron Synapses

PubMed Central

Lennon, William; Yamazaki, Tadashi; Hecht-Nielsen, Robert

2015-01-01

Theoretical and computational models of the cerebellum typically focus on the role of parallel fiber (PF)—Purkinje cell (PKJ) synapses for learned behavior, but few emphasize the role of the molecular layer interneurons (MLIs)—the stellate and basket cells. A number of recent experimental results suggest the role of MLIs is more important than previous models put forth. We investigate learning at PF—MLI synapses and propose a mathematical model to describe plasticity at this synapse. We perform computer simulations with this form of learning using a spiking neuron model of the MLI and show that it reproduces six in vitro experimental results in addition to simulating four novel protocols. Further, we show how this plasticity model can predict the results of other experimental protocols that are not simulated. Finally, we hypothesize what the biological mechanisms are for changes in synaptic efficacy that embody the phenomenological model proposed here. PMID:26733856

A genetic algorithm for solving supply chain network design model

NASA Astrophysics Data System (ADS)

Firoozi, Z.; Ismail, N.; Ariafar, S. H.; Tang, S. H.; Ariffin, M. K. M. A.

2013-09-01

Network design is by nature costly and optimization models play significant role in reducing the unnecessary cost components of a distribution network. This study proposes a genetic algorithm to solve a distribution network design model. The structure of the chromosome in the proposed algorithm is defined in a novel way that in addition to producing feasible solutions, it also reduces the computational complexity of the algorithm. Computational results are presented to show the algorithm performance.

Interactive computer graphics and its role in control system design of large space structures

NASA Technical Reports Server (NTRS)

Reddy, A. S. S. R.

1985-01-01

This paper attempts to show the relevance of interactive computer graphics in the design of control systems to maintain attitude and shape of large space structures to accomplish the required mission objectives. The typical phases of control system design, starting from the physical model such as modeling the dynamics, modal analysis, and control system design methodology are reviewed and the need of the interactive computer graphics is demonstrated. Typical constituent parts of large space structures such as free-free beams and free-free plates are used to demonstrate the complexity of the control system design and the effectiveness of the interactive computer graphics.

Π4U: A high performance computing framework for Bayesian uncertainty quantification of complex models

NASA Astrophysics Data System (ADS)

Hadjidoukas, P. E.; Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P.

2015-03-01

We present Π4U, an extensible framework, for non-intrusive Bayesian Uncertainty Quantification and Propagation (UQ+P) of complex and computationally demanding physical models, that can exploit massively parallel computer architectures. The framework incorporates Laplace asymptotic approximations as well as stochastic algorithms, along with distributed numerical differentiation and task-based parallelism for heterogeneous clusters. Sampling is based on the Transitional Markov Chain Monte Carlo (TMCMC) algorithm and its variants. The optimization tasks associated with the asymptotic approximations are treated via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). A modified subset simulation method is used for posterior reliability measurements of rare events. The framework accommodates scheduling of multiple physical model evaluations based on an adaptive load balancing library and shows excellent scalability. In addition to the software framework, we also provide guidelines as to the applicability and efficiency of Bayesian tools when applied to computationally demanding physical models. Theoretical and computational developments are demonstrated with applications drawn from molecular dynamics, structural dynamics and granular flow.

A computational model of selection by consequences.

PubMed Central

McDowell, J J

2004-01-01

Darwinian selection by consequences was instantiated in a computational model that consisted of a repertoire of behaviors undergoing selection, reproduction, and mutation over many generations. The model in effect created a digital organism that emitted behavior continuously. The behavior of this digital organism was studied in three series of computational experiments that arranged reinforcement according to random-interval (RI) schedules. The quantitative features of the model were varied over wide ranges in these experiments, and many of the qualitative features of the model also were varied. The digital organism consistently showed a hyperbolic relation between response and reinforcement rates, and this hyperbolic description of the data was consistently better than the description provided by other, similar, function forms. In addition, the parameters of the hyperbola varied systematically with the quantitative, and some of the qualitative, properties of the model in ways that were consistent with findings from biological organisms. These results suggest that the material events responsible for an organism's responding on RI schedules are computationally equivalent to Darwinian selection by consequences. They also suggest that the computational model developed here is worth pursuing further as a possible dynamic account of behavior. PMID:15357512

Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks

NASA Astrophysics Data System (ADS)

Pyle, Ryan; Rosenbaum, Robert

2017-01-01

Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

A Novel Method to Compute Breathing Volumes via Motion Capture Systems: Design and Experimental Trials.

PubMed

Massaroni, Carlo; Cassetta, Eugenio; Silvestri, Sergio

2017-10-01

Respiratory assessment can be carried out by using motion capture systems. A geometrical model is mandatory in order to compute the breathing volume as a function of time from the markers' trajectories. This study describes a novel model to compute volume changes and calculate respiratory parameters by using a motion capture system. The novel method, ie, prism-based method, computes the volume enclosed within the chest by defining 82 prisms from the 89 markers attached to the subject chest. Volumes computed with this method are compared to spirometry volumes and to volumes computed by a conventional method based on the tetrahedron's decomposition of the chest wall and integrated in a commercial motion capture system. Eight healthy volunteers were enrolled and 30 seconds of quiet breathing data collected from each of them. Results show a better agreement between volumes computed by the prism-based method and the spirometry (discrepancy of 2.23%, R 2  = .94) compared to the agreement between volumes computed by the conventional method and the spirometry (discrepancy of 3.56%, R 2  = .92). The proposed method also showed better performances in the calculation of respiratory parameters. Our findings open up prospects for the further use of the new method in the breathing assessment via motion capture systems.

Computation of rare transitions in the barotropic quasi-geostrophic equations

NASA Astrophysics Data System (ADS)

Laurie, Jason; Bouchet, Freddy

2015-01-01

We investigate the theoretical and numerical computation of rare transitions in simple geophysical turbulent models. We consider the barotropic quasi-geostrophic and two-dimensional Navier-Stokes equations in regimes where bistability between two coexisting large-scale attractors exist. By means of large deviations and instanton theory with the use of an Onsager-Machlup path integral formalism for the transition probability, we show how one can directly compute the most probable transition path between two coexisting attractors analytically in an equilibrium (Langevin) framework and numerically otherwise. We adapt a class of numerical optimization algorithms known as minimum action methods to simple geophysical turbulent models. We show that by numerically minimizing an appropriate action functional in a large deviation limit, one can predict the most likely transition path for a rare transition between two states. By considering examples where theoretical predictions can be made, we show that the minimum action method successfully predicts the most likely transition path. Finally, we discuss the application and extension of such numerical optimization schemes to the computation of rare transitions observed in direct numerical simulations and experiments and to other, more complex, turbulent systems.

Seismic activity prediction using computational intelligence techniques in northern Pakistan

NASA Astrophysics Data System (ADS)

Asim, Khawaja M.; Awais, Muhammad; Martínez-Álvarez, F.; Iqbal, Talat

2017-10-01

Earthquake prediction study is carried out for the region of northern Pakistan. The prediction methodology includes interdisciplinary interaction of seismology and computational intelligence. Eight seismic parameters are computed based upon the past earthquakes. Predictive ability of these eight seismic parameters is evaluated in terms of information gain, which leads to the selection of six parameters to be used in prediction. Multiple computationally intelligent models have been developed for earthquake prediction using selected seismic parameters. These models include feed-forward neural network, recurrent neural network, random forest, multi layer perceptron, radial basis neural network, and support vector machine. The performance of every prediction model is evaluated and McNemar's statistical test is applied to observe the statistical significance of computational methodologies. Feed-forward neural network shows statistically significant predictions along with accuracy of 75% and positive predictive value of 78% in context of northern Pakistan.

Sensitivity derivatives for advanced CFD algorithm and viscous modelling parameters via automatic differentiation

NASA Technical Reports Server (NTRS)

Green, Lawrence L.; Newman, Perry A.; Haigler, Kara J.

1993-01-01

The computational technique of automatic differentiation (AD) is applied to a three-dimensional thin-layer Navier-Stokes multigrid flow solver to assess the feasibility and computational impact of obtaining exact sensitivity derivatives typical of those needed for sensitivity analyses. Calculations are performed for an ONERA M6 wing in transonic flow with both the Baldwin-Lomax and Johnson-King turbulence models. The wing lift, drag, and pitching moment coefficients are differentiated with respect to two different groups of input parameters. The first group consists of the second- and fourth-order damping coefficients of the computational algorithm, whereas the second group consists of two parameters in the viscous turbulent flow physics modelling. Results obtained via AD are compared, for both accuracy and computational efficiency with the results obtained with divided differences (DD). The AD results are accurate, extremely simple to obtain, and show significant computational advantage over those obtained by DD for some cases.

Probabilistic Modeling and Visualization of the Flexibility in Morphable Models

NASA Astrophysics Data System (ADS)

Lüthi, M.; Albrecht, T.; Vetter, T.

Statistical shape models, and in particular morphable models, have gained widespread use in computer vision, computer graphics and medical imaging. Researchers have started to build models of almost any anatomical structure in the human body. While these models provide a useful prior for many image analysis task, relatively little information about the shape represented by the morphable model is exploited. We propose a method for computing and visualizing the remaining flexibility, when a part of the shape is fixed. Our method, which is based on Probabilistic PCA, not only leads to an approach for reconstructing the full shape from partial information, but also allows us to investigate and visualize the uncertainty of a reconstruction. To show the feasibility of our approach we performed experiments on a statistical model of the human face and the femur bone. The visualization of the remaining flexibility allows for greater insight into the statistical properties of the shape.

Computational Foundations of Natural Intelligence

PubMed Central

van Gerven, Marcel

2017-01-01

New developments in AI and neuroscience are revitalizing the quest to understanding natural intelligence, offering insight about how to equip machines with human-like capabilities. This paper reviews some of the computational principles relevant for understanding natural intelligence and, ultimately, achieving strong AI. After reviewing basic principles, a variety of computational modeling approaches is discussed. Subsequently, I concentrate on the use of artificial neural networks as a framework for modeling cognitive processes. This paper ends by outlining some of the challenges that remain to fulfill the promise of machines that show human-like intelligence. PMID:29375355

Assessing the Electromagnetic Fields Generated by a Radiofrequency MRI Body Coil at 64 MHz: Defeaturing vs. Accuracy

PubMed Central

Lucano, Elena; Liberti, Micaela; Mendoza, Gonzalo G.; Lloyd, Tom; Iacono, Maria Ida; Apollonio, Francesca; Wedan, Steve; Kainz, Wolfgang; Angelone, Leonardo M.

2016-01-01

Goal This study aims at a systematic assessment of five computational models of a birdcage coil for magnetic resonance imaging (MRI) with respect to accuracy and computational cost. Methods The models were implemented using the same geometrical model and numerical algorithm, but different driving methods (i.e., coil “defeaturing”). The defeatured models were labeled as: specific (S2), generic (G32, G16), and hybrid (H16, H16fr-forced). The accuracy of the models was evaluated using the “Symmetric Mean Absolute Percentage Error” (“SMAPE”), by comparison with measurements in terms of frequency response, as well as electric (||E⃗||) and magnetic (||B⃗||) field magnitude. Results All the models computed the ||B⃗|| within 35 % of the measurements, only the S2, G32, and H16 were able to accurately model the ||E⃗|| inside the phantom with a maximum SMAPE of 16 %. Outside the phantom, only the S2 showed a SMAPE lower than 11 %. Conclusions Results showed that assessing the accuracy of ||B⃗|| based only on comparison along the central longitudinal line of the coil can be misleading. Generic or hybrid coils – when properly modeling the currents along the rings/rungs – were sufficient to accurately reproduce the fields inside a phantom while a specific model was needed to accurately model ||E⃗|| in the space between coil and phantom. Significance Computational modeling of birdcage body coils is extensively used in the evaluation of RF-induced heating during MRI. Experimental validation of numerical models is needed to determine if a model is an accurate representation of a physical coil. PMID:26685220

A Parallel Sliding Region Algorithm to Make Agent-Based Modeling Possible for a Large-Scale Simulation: Modeling Hepatitis C Epidemics in Canada.

PubMed

Wong, William W L; Feng, Zeny Z; Thein, Hla-Hla

2016-11-01

Agent-based models (ABMs) are computer simulation models that define interactions among agents and simulate emergent behaviors that arise from the ensemble of local decisions. ABMs have been increasingly used to examine trends in infectious disease epidemiology. However, the main limitation of ABMs is the high computational cost for a large-scale simulation. To improve the computational efficiency for large-scale ABM simulations, we built a parallelizable sliding region algorithm (SRA) for ABM and compared it to a nonparallelizable ABM. We developed a complex agent network and performed two simulations to model hepatitis C epidemics based on the real demographic data from Saskatchewan, Canada. The first simulation used the SRA that processed on each postal code subregion subsequently. The second simulation processed the entire population simultaneously. It was concluded that the parallelizable SRA showed computational time saving with comparable results in a province-wide simulation. Using the same method, SRA can be generalized for performing a country-wide simulation. Thus, this parallel algorithm enables the possibility of using ABM for large-scale simulation with limited computational resources.

Computational Modeling For The Transitional Flow Over A Multi-Element Airfoil

NASA Technical Reports Server (NTRS)

Liou, William W.; Liu, Feng-Jun; Rumsey, Chris L. (Technical Monitor)

2000-01-01

The transitional flow over a multi-element airfoil in a landing configuration are computed using a two equation transition model. The transition model is predictive in the sense that the transition onset is a result of the calculation and no prior knowledge of the transition location is required. The computations were performed using the INS2D) Navier-Stokes code. Overset grids are used for the three-element airfoil. The airfoil operating conditions are varied for a range of angle of attack and for two different Reynolds numbers of 5 million and 9 million. The computed results are compared with experimental data for the surface pressure, skin friction, transition onset location, and velocity magnitude. In general, the comparison shows a good agreement with the experimental data.

Transitions amongst synchronous solutions in the stochastic Kuramoto model

NASA Astrophysics Data System (ADS)

DeVille, Lee

2012-05-01

We consider the Kuramoto model of coupled oscillators with nearest-neighbour coupling and additive white noise. We show that synchronous solutions which are stable without the addition of noise become metastable and that we have transitions amongst synchronous solutions on long timescales. We compute these timescales and, moreover, compute the most likely path in phase space that transitions will follow. We show that these transition timescales do not increase as the number of oscillators in the system increases, and are roughly constant in the system size. Finally, we show that the transitions correspond to a splitting of one synchronous solution into two communities which move independently for some time and which rejoin to form a different synchronous solution.

Combining Static Analysis and Model Checking for Software Analysis

NASA Technical Reports Server (NTRS)

Brat, Guillaume; Visser, Willem; Clancy, Daniel (Technical Monitor)

2003-01-01

We present an iterative technique in which model checking and static analysis are combined to verify large software systems. The role of the static analysis is to compute partial order information which the model checker uses to reduce the state space. During exploration, the model checker also computes aliasing information that it gives to the static analyzer which can then refine its analysis. The result of this refined analysis is then fed back to the model checker which updates its partial order reduction. At each step of this iterative process, the static analysis computes optimistic information which results in an unsafe reduction of the state space. However we show that the process converges to a fired point at which time the partial order information is safe and the whole state space is explored.

From Birdsong to Human Speech Recognition: Bayesian Inference on a Hierarchy of Nonlinear Dynamical Systems

PubMed Central

Yildiz, Izzet B.; von Kriegstein, Katharina; Kiebel, Stefan J.

2013-01-01

Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents—an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments. PMID:24068902

From birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.

PubMed

Yildiz, Izzet B; von Kriegstein, Katharina; Kiebel, Stefan J

2013-01-01

Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents-an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments.

A performance comparison of scalar, vector, and concurrent vector computers including supercomputers for modeling transport of reactive contaminants in groundwater

NASA Astrophysics Data System (ADS)

Tripathi, Vijay S.; Yeh, G. T.

1993-06-01

Sophisticated and highly computation-intensive models of transport of reactive contaminants in groundwater have been developed in recent years. Application of such models to real-world contaminant transport problems, e.g., simulation of groundwater transport of 10-15 chemically reactive elements (e.g., toxic metals) and relevant complexes and minerals in two and three dimensions over a distance of several hundred meters, requires high-performance computers including supercomputers. Although not widely recognized as such, the computational complexity and demand of these models compare with well-known computation-intensive applications including weather forecasting and quantum chemical calculations. A survey of the performance of a variety of available hardware, as measured by the run times for a reactive transport model HYDROGEOCHEM, showed that while supercomputers provide the fastest execution times for such problems, relatively low-cost reduced instruction set computer (RISC) based scalar computers provide the best performance-to-price ratio. Because supercomputers like the Cray X-MP are inherently multiuser resources, often the RISC computers also provide much better turnaround times. Furthermore, RISC-based workstations provide the best platforms for "visualization" of groundwater flow and contaminant plumes. The most notable result, however, is that current workstations costing less than $10,000 provide performance within a factor of 5 of a Cray X-MP.

Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

PubMed Central

Pecevski, Dejan; Buesing, Lars; Maass, Wolfgang

2011-01-01

An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows (“explaining away”) and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons. PMID:22219717

Aeroelastic Modeling of a Nozzle Startup Transient

NASA Technical Reports Server (NTRS)

Wang, Ten-See; Zhao, Xiang; Zhang, Sijun; Chen, Yen-Sen

2014-01-01

Lateral nozzle forces are known to cause severe structural damage to any new rocket engine in development during test. While three-dimensional, transient, turbulent, chemically reacting computational fluid dynamics methodology has been demonstrated to capture major side load physics with rigid nozzles, hot-fire tests often show nozzle structure deformation during major side load events, leading to structural damages if structural strengthening measures were not taken. The modeling picture is incomplete without the capability to address the two-way responses between the structure and fluid. The objective of this study is to develop a tightly coupled aeroelastic modeling algorithm by implementing the necessary structural dynamics component into an anchored computational fluid dynamics methodology. The computational fluid dynamics component is based on an unstructured-grid, pressure-based computational fluid dynamics formulation, while the computational structural dynamics component is developed under the framework of modal analysis. Transient aeroelastic nozzle startup analyses at sea level were performed, and the computed transient nozzle fluid-structure interaction physics presented,

GRAVTool, a Package to Compute Geoid Model by Remove-Compute-Restore Technique

NASA Astrophysics Data System (ADS)

Marotta, G. S.; Blitzkow, D.; Vidotti, R. M.

2015-12-01

Currently, there are several methods to determine geoid models. They can be based on terrestrial gravity data, geopotential coefficients, astro-geodetic data or a combination of them. Among the techniques to compute a precise geoid model, the Remove-Compute-Restore (RCR) has been widely applied. It considers short, medium and long wavelengths derived from altitude data provided by Digital Terrain Models (DTM), terrestrial gravity data and global geopotential coefficients, respectively. In order to apply this technique, it is necessary to create procedures that compute gravity anomalies and geoid models, by the integration of different wavelengths, and that adjust these models to one local vertical datum. This research presents a developed package called GRAVTool based on MATLAB software to compute local geoid models by RCR technique and its application in a study area. The studied area comprehends the federal district of Brazil, with ~6000 km², wavy relief, heights varying from 600 m to 1340 m, located between the coordinates 48.25ºW, 15.45ºS and 47.33ºW, 16.06ºS. The results of the numerical example on the studied area show the local geoid model computed by the GRAVTool package (Figure), using 1377 terrestrial gravity data, SRTM data with 3 arc second of resolution, and geopotential coefficients of the EIGEN-6C4 model to degree 360. The accuracy of the computed model (σ = ± 0.071 m, RMS = 0.069 m, maximum = 0.178 m and minimum = -0.123 m) matches the uncertainty (σ =± 0.073) of 21 points randomly spaced where the geoid was computed by geometrical leveling technique supported by positioning GNSS. The results were also better than those achieved by Brazilian official regional geoid model (σ = ± 0.099 m, RMS = 0.208 m, maximum = 0.419 m and minimum = -0.040 m).

A study of application of remote sensing to river forecasting. Volume 1: Executive summary

NASA Technical Reports Server (NTRS)

1975-01-01

A project is described whose goal was to define, implement and evaluate a pilot demonstration test to show the practicability of applying remotely sensed data to operational river forecasting in gaged or previously ungaged watersheds. A secondary objective was to provide NASA with documentation describing the computer programs that comprise the streamflow forecasting simulation model used. A computer-based simulation model was adapted to a streamflow forecasting application and implemented in an IBM System/360 Model 44 computer, operating in a dedicated mode, with operator interactive control through a Model 2250 keyboard/graphic CRT terminal. The test site whose hydrologic behavior was simulated is a small basin (365 square kilometers) designated Town Creek near Geraldine, Alabama.

Computation in generalised probabilisitic theories

NASA Astrophysics Data System (ADS)

Lee, Ciarán M.; Barrett, Jonathan

2015-08-01

From the general difficulty of simulating quantum systems using classical systems, and in particular the existence of an efficient quantum algorithm for factoring, it is likely that quantum computation is intrinsically more powerful than classical computation. At present, the best upper bound known for the power of quantum computation is that {{BQP}}\\subseteq {{AWPP}}, where {{AWPP}} is a classical complexity class (known to be included in {{PP}}, hence {{PSPACE}}). This work investigates limits on computational power that are imposed by simple physical, or information theoretic, principles. To this end, we define a circuit-based model of computation in a class of operationally-defined theories more general than quantum theory, and ask: what is the minimal set of physical assumptions under which the above inclusions still hold? We show that given only an assumption of tomographic locality (roughly, that multipartite states and transformations can be characterized by local measurements), efficient computations are contained in {{AWPP}}. This inclusion still holds even without assuming a basic notion of causality (where the notion is, roughly, that probabilities for outcomes cannot depend on future measurement choices). Following Aaronson, we extend the computational model by allowing post-selection on measurement outcomes. Aaronson showed that the corresponding quantum complexity class, {{PostBQP}}, is equal to {{PP}}. Given only the assumption of tomographic locality, the inclusion in {{PP}} still holds for post-selected computation in general theories. Hence in a world with post-selection, quantum theory is optimal for computation in the space of all operational theories. We then consider whether one can obtain relativized complexity results for general theories. It is not obvious how to define a sensible notion of a computational oracle in the general framework that reduces to the standard notion in the quantum case. Nevertheless, it is possible to define computation relative to a ‘classical oracle’. Then, we show there exists a classical oracle relative to which efficient computation in any theory satisfying the causality assumption does not include {{NP}}.

Predictive computation of genomic logic processing functions in embryonic development

PubMed Central

Peter, Isabelle S.; Faure, Emmanuel; Davidson, Eric H.

2012-01-01

Gene regulatory networks (GRNs) control the dynamic spatial patterns of regulatory gene expression in development. Thus, in principle, GRN models may provide system-level, causal explanations of developmental process. To test this assertion, we have transformed a relatively well-established GRN model into a predictive, dynamic Boolean computational model. This Boolean model computes spatial and temporal gene expression according to the regulatory logic and gene interactions specified in a GRN model for embryonic development in the sea urchin. Additional information input into the model included the progressive embryonic geometry and gene expression kinetics. The resulting model predicted gene expression patterns for a large number of individual regulatory genes each hour up to gastrulation (30 h) in four different spatial domains of the embryo. Direct comparison with experimental observations showed that the model predictively computed these patterns with remarkable spatial and temporal accuracy. In addition, we used this model to carry out in silico perturbations of regulatory functions and of embryonic spatial organization. The model computationally reproduced the altered developmental functions observed experimentally. Two major conclusions are that the starting GRN model contains sufficiently complete regulatory information to permit explanation of a complex developmental process of gene expression solely in terms of genomic regulatory code, and that the Boolean model provides a tool with which to test in silico regulatory circuitry and developmental perturbations. PMID:22927416

Application of the MacCormack scheme to overland flow routing for high-spatial resolution distributed hydrological model

NASA Astrophysics Data System (ADS)

Zhang, Ling; Nan, Zhuotong; Liang, Xu; Xu, Yi; Hernández, Felipe; Li, Lianxia

2018-03-01

Although process-based distributed hydrological models (PDHMs) are evolving rapidly over the last few decades, their extensive applications are still challenged by the computational expenses. This study attempted, for the first time, to apply the numerically efficient MacCormack algorithm to overland flow routing in a representative high-spatial resolution PDHM, i.e., the distributed hydrology-soil-vegetation model (DHSVM), in order to improve its computational efficiency. The analytical verification indicates that both the semi and full versions of the MacCormack schemes exhibit robust numerical stability and are more computationally efficient than the conventional explicit linear scheme. The full-version outperforms the semi-version in terms of simulation accuracy when a same time step is adopted. The semi-MacCormack scheme was implemented into DHSVM (version 3.1.2) to solve the kinematic wave equations for overland flow routing. The performance and practicality of the enhanced DHSVM-MacCormack model was assessed by performing two groups of modeling experiments in the Mercer Creek watershed, a small urban catchment near Bellevue, Washington. The experiments show that DHSVM-MacCormack can considerably improve the computational efficiency without compromising the simulation accuracy of the original DHSVM model. More specifically, with the same computational environment and model settings, the computational time required by DHSVM-MacCormack can be reduced to several dozen minutes for a simulation period of three months (in contrast with one day and a half by the original DHSVM model) without noticeable sacrifice of the accuracy. The MacCormack scheme proves to be applicable to overland flow routing in DHSVM, which implies that it can be coupled into other PHDMs for watershed routing to either significantly improve their computational efficiency or to make the kinematic wave routing for high resolution modeling computational feasible.

A strategy for improved computational efficiency of the method of anchored distributions

NASA Astrophysics Data System (ADS)

Over, Matthew William; Yang, Yarong; Chen, Xingyuan; Rubin, Yoram

2013-06-01

This paper proposes a strategy for improving the computational efficiency of model inversion using the method of anchored distributions (MAD) by "bundling" similar model parametrizations in the likelihood function. Inferring the likelihood function typically requires a large number of forward model (FM) simulations for each possible model parametrization; as a result, the process is quite expensive. To ease this prohibitive cost, we present an approximation for the likelihood function called bundling that relaxes the requirement for high quantities of FM simulations. This approximation redefines the conditional statement of the likelihood function as the probability of a set of similar model parametrizations "bundle" replicating field measurements, which we show is neither a model reduction nor a sampling approach to improving the computational efficiency of model inversion. To evaluate the effectiveness of these modifications, we compare the quality of predictions and computational cost of bundling relative to a baseline MAD inversion of 3-D flow and transport model parameters. Additionally, to aid understanding of the implementation we provide a tutorial for bundling in the form of a sample data set and script for the R statistical computing language. For our synthetic experiment, bundling achieved a 35% reduction in overall computational cost and had a limited negative impact on predicted probability distributions of the model parameters. Strategies for minimizing error in the bundling approximation, for enforcing similarity among the sets of model parametrizations, and for identifying convergence of the likelihood function are also presented.

Computational Models of Laryngeal Aerodynamics: Potentials and Numerical Costs.

PubMed

Sadeghi, Hossein; Kniesburges, Stefan; Kaltenbacher, Manfred; Schützenberger, Anne; Döllinger, Michael

2018-02-07

Human phonation is based on the interaction between tracheal airflow and laryngeal dynamics. This fluid-structure interaction is based on the energy exchange between airflow and vocal folds. Major challenges in analyzing the phonatory process in-vivo are the small dimensions and the poor accessibility of the region of interest. For improved analysis of the phonatory process, numerical simulations of the airflow and the vocal fold dynamics have been suggested. Even though most of the models reproduced the phonatory process fairly well, development of comprehensive larynx models is still a subject of research. In the context of clinical application, physiological accuracy and computational model efficiency are of great interest. In this study, a simple numerical larynx model is introduced that incorporates the laryngeal fluid flow. It is based on a synthetic experimental model with silicone vocal folds. The degree of realism was successively increased in separate computational models and each model was simulated for 10 oscillation cycles. Results show that relevant features of the laryngeal flow field, such as glottal jet deflection, develop even when applying rather simple static models with oscillating flow rates. Including further phonatory components such as vocal fold motion, mucosal wave propagation, and ventricular folds, the simulations show phonatory key features like intraglottal flow separation and increased flow rate in presence of ventricular folds. The simulation time on 100 CPU cores ranged between 25 and 290 hours, currently restricting clinical application of these models. Nevertheless, results show high potential of numerical simulations for better understanding of phonatory process. Copyright © 2018 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Computational modeling for prediction of the shear stress of three-dimensional isotropic and aligned fiber networks.

PubMed

Park, Seungman

2017-09-01

Interstitial flow (IF) is a creeping flow through the interstitial space of the extracellular matrix (ECM). IF plays a key role in diverse biological functions, such as tissue homeostasis, cell function and behavior. Currently, most studies that have characterized IF have focused on the permeability of ECM or shear stress distribution on the cells, but less is known about the prediction of shear stress on the individual fibers or fiber networks despite its significance in the alignment of matrix fibers and cells observed in fibrotic or wound tissues. In this study, I developed a computational model to predict shear stress for different structured fibrous networks. To generate isotropic models, a random growth algorithm and a second-order orientation tensor were employed. Then, a three-dimensional (3D) solid model was created using computer-aided design (CAD) software for the aligned models (i.e., parallel, perpendicular and cubic models). Subsequently, a tetrahedral unstructured mesh was generated and flow solutions were calculated by solving equations for mass and momentum conservation for all models. Through the flow solutions, I estimated permeability using Darcy's law. Average shear stress (ASS) on the fibers was calculated by averaging the wall shear stress of the fibers. By using nonlinear surface fitting of permeability, viscosity, velocity, porosity and ASS, I devised new computational models. Overall, the developed models showed that higher porosity induced higher permeability, as previous empirical and theoretical models have shown. For comparison of the permeability, the present computational models were matched well with previous models, which justify our computational approach. ASS tended to increase linearly with respect to inlet velocity and dynamic viscosity, whereas permeability was almost the same. Finally, the developed model nicely predicted the ASS values that had been directly estimated from computational fluid dynamics (CFD). The present computational models will provide new tools for predicting accurate functional properties and designing fibrous porous materials, thereby significantly advancing tissue engineering. Copyright © 2017 Elsevier B.V. All rights reserved.

Light curves for bump Cepheids computed with a dynamically zoned pulsation code

NASA Technical Reports Server (NTRS)

Adams, T. F.; Castor, J. I.; Davis, C. G.

1980-01-01

The dynamically zoned pulsation code developed by Castor, Davis, and Davison was used to recalculate the Goddard model and to calculate three other Cepheid models with the same period (9.8 days). This family of models shows how the bumps and other features of the light and velocity curves change as the mass is varied at constant period. The use of a code that is capable of producing reliable light curves demonstrates that the light and velocity curves for 9.8 day Cepheid models with standard homogeneous compositions do not show bumps like those that are observed unless the mass is significantly lower than the 'evolutionary mass.' The light and velocity curves for the Goddard model presented here are similar to those computed independently by Fischel, Sparks, and Karp. They should be useful as standards for future investigators.

Fast Simulation of Membrane Filtration by Combining Particle Retention Mechanisms and Network Models

NASA Astrophysics Data System (ADS)

Krupp, Armin; Griffiths, Ian; Please, Colin

2016-11-01

Porous membranes are used for their particle retention capabilities in a wide range of industrial filtration processes. The underlying mechanisms for particle retention are complex and often change during the filtration process, making it hard to predict the change in permeability of the membrane during the process. Recently, stochastic network models have been shown to predict the change in permeability based on retention mechanisms, but remain computationally intensive. We show that the averaged behaviour of such a stochastic network model can efficiently be computed using a simple partial differential equation. Moreover, we also show that the geometric structure of the underlying membrane and particle-size distribution can be represented in our model, making it suitable for modelling particle retention in interconnected membranes as well. We conclude by demonstrating the particular application to microfluidic filtration, where the model can be used to efficiently compute a probability density for flux measurements based on the geometry of the pores and particles. A. U. K. is grateful for funding from Pall Corporation and the Mathematical Institute, University of Oxford. I.M.G. gratefully acknowledges support from the Royal Society through a University Research Fellowship.

Estimating average growth trajectories in shape-space using kernel smoothing.

PubMed

Hutton, Tim J; Buxton, Bernard F; Hammond, Peter; Potts, Henry W W

2003-06-01

In this paper, we show how a dense surface point distribution model of the human face can be computed and demonstrate the usefulness of the high-dimensional shape-space for expressing the shape changes associated with growth and aging. We show how average growth trajectories for the human face can be computed in the absence of longitudinal data by using kernel smoothing across a population. A training set of three-dimensional surface scans of 199 male and 201 female subjects of between 0 and 50 years of age is used to build the model.

Age and Pathway Diagnostics for a Stratospheric General Circulation Model

NASA Technical Reports Server (NTRS)

Schoeberl, Mark R.; Douglass, Anne R.; Polansky, Brian

2004-01-01

Using a variety of age diagnostic experiments we examine the stratospheric age spectrum of the Goddard Finite Volume Generd Circulation Model. Pulse tracer release age-of-air computations are compared to forward and backward trajectory computations. These comparisons show good agreement, and the age-of-air also compares well with observed long lived tracers. Pathway diagnostics show how air arrives in the lowermost stratosphere and the age structure of that region. Using tracers with different lifetimes we can estimate the age spectrum - this technique should be useful in diagnosing transport from various trace gas observations.

Computational comparison of quantum-mechanical models for multistep direct reactions

NASA Astrophysics Data System (ADS)

Koning, A. J.; Akkermans, J. M.

1993-02-01

We have carried out a computational comparison of all existing quantum-mechanical models for multistep direct (MSD) reactions. The various MSD models, including the so-called Feshbach-Kerman-Koonin, Tamura-Udagawa-Lenske and Nishioka-Yoshida-Weidenmüller models, have been implemented in a single computer system. All model calculations thus use the same set of parameters and the same numerical techniques; only one adjustable parameter is employed. The computational results have been compared with experimental energy spectra and angular distributions for several nuclear reactions, namely, 90Zr(p,p') at 80 MeV, 209Bi(p,p') at 62 MeV, and 93Nb(n,n') at 25.7 MeV. In addition, the results have been compared with the Kalbach systematics and with semiclassical exciton model calculations. All quantum MSD models provide a good fit to the experimental data. In addition, they reproduce the systematics very well and are clearly better than semiclassical model calculations. We furthermore show that the calculated predictions do not differ very strongly between the various quantum MSD models, leading to the conclusion that the simplest MSD model (the Feshbach-Kerman-Koonin model) is adequate for the analysis of experimental data.

Gray-box reservoir routing to compute flow propagation in operational forecasting and decision support systems

NASA Astrophysics Data System (ADS)

Russano, Euan; Schwanenberg, Dirk; Alvarado Montero, Rodolfo

2017-04-01

Operational forecasting and decision support systems for flood mitigation and the daily management of water resources require computationally efficient flow routing models. If backwater effects do not play an important role, a hydrological routing approach is often a pragmatic choice. It offers a reasonable accuracy at low computational costs in comparison to a more detailed hydraulic model. This work presents a nonlinear reservoir routing scheme as well as its implementation for the flow propagation between the hydro reservoir Três Marias and a downstream inundation-affected city Pirapora in Brazil. We refer to the model as a gray-box approach due to the identification of the parameter k by a data-driven approach for each reservoir of the cascade, instead of using estimates based on physical characteristics. The model reproduces the discharge at the gauge Pirapora, using 15 reservoirs in the cascade. The obtained results are compared with the ones obtained from the full-hydrodynamic model SOBEK. Results show a relatively good performance for the validation period, with a RMSE of 139.48 for the gray-box model, while the full-hydrodynamic model shows a RMSE of 136.67. The simulation time for a period of several years for the full-hydrodynamic took approximately 64s, while the gray-box model only required about 0.50s. This provides a significant speedup of the computation by only a little trade-off in accuracy, pointing at the potential of the simple approach in the context of time-critical, operational applications. Key-words: flow routing, reservoir routing, gray-box model

DISTING: A web application for fast algorithmic computation of alternative indistinguishable linear compartmental models.

PubMed

Davidson, Natalie R; Godfrey, Keith R; Alquaddoomi, Faisal; Nola, David; DiStefano, Joseph J

2017-05-01

We describe and illustrate use of DISTING, a novel web application for computing alternative structurally identifiable linear compartmental models that are input-output indistinguishable from a postulated linear compartmental model. Several computer packages are available for analysing the structural identifiability of such models, but DISTING is the first to be made available for assessing indistinguishability. The computational algorithms embedded in DISTING are based on advanced versions of established geometric and algebraic properties of linear compartmental models, embedded in a user-friendly graphic model user interface. Novel computational tools greatly speed up the overall procedure. These include algorithms for Jacobian matrix reduction, submatrix rank reduction, and parallelization of candidate rank computations in symbolic matrix analysis. The application of DISTING to three postulated models with respectively two, three and four compartments is given. The 2-compartment example is used to illustrate the indistinguishability problem; the original (unidentifiable) model is found to have two structurally identifiable models that are indistinguishable from it. The 3-compartment example has three structurally identifiable indistinguishable models. It is found from DISTING that the four-compartment example has five structurally identifiable models indistinguishable from the original postulated model. This example shows that care is needed when dealing with models that have two or more compartments which are neither perturbed nor observed, because the numbering of these compartments may be arbitrary. DISTING is universally and freely available via the Internet. It is easy to use and circumvents tedious and complicated algebraic analysis previously done by hand. Copyright © 2017 Elsevier B.V. All rights reserved.

Passive dendrites enable single neurons to compute linearly non-separable functions.

PubMed

Cazé, Romain Daniel; Humphries, Mark; Gutkin, Boris

2013-01-01

Local supra-linear summation of excitatory inputs occurring in pyramidal cell dendrites, the so-called dendritic spikes, results in independent spiking dendritic sub-units, which turn pyramidal neurons into two-layer neural networks capable of computing linearly non-separable functions, such as the exclusive OR. Other neuron classes, such as interneurons, may possess only a few independent dendritic sub-units, or only passive dendrites where input summation is purely sub-linear, and where dendritic sub-units are only saturating. To determine if such neurons can also compute linearly non-separable functions, we enumerate, for a given parameter range, the Boolean functions implementable by a binary neuron model with a linear sub-unit and either a single spiking or a saturating dendritic sub-unit. We then analytically generalize these numerical results to an arbitrary number of non-linear sub-units. First, we show that a single non-linear dendritic sub-unit, in addition to the somatic non-linearity, is sufficient to compute linearly non-separable functions. Second, we analytically prove that, with a sufficient number of saturating dendritic sub-units, a neuron can compute all functions computable with purely excitatory inputs. Third, we show that these linearly non-separable functions can be implemented with at least two strategies: one where a dendritic sub-unit is sufficient to trigger a somatic spike; another where somatic spiking requires the cooperation of multiple dendritic sub-units. We formally prove that implementing the latter architecture is possible with both types of dendritic sub-units whereas the former is only possible with spiking dendrites. Finally, we show how linearly non-separable functions can be computed by a generic two-compartment biophysical model and a realistic neuron model of the cerebellar stellate cell interneuron. Taken together our results demonstrate that passive dendrites are sufficient to enable neurons to compute linearly non-separable functions.

Passive Dendrites Enable Single Neurons to Compute Linearly Non-separable Functions

PubMed Central

Cazé, Romain Daniel; Humphries, Mark; Gutkin, Boris

2013-01-01

Local supra-linear summation of excitatory inputs occurring in pyramidal cell dendrites, the so-called dendritic spikes, results in independent spiking dendritic sub-units, which turn pyramidal neurons into two-layer neural networks capable of computing linearly non-separable functions, such as the exclusive OR. Other neuron classes, such as interneurons, may possess only a few independent dendritic sub-units, or only passive dendrites where input summation is purely sub-linear, and where dendritic sub-units are only saturating. To determine if such neurons can also compute linearly non-separable functions, we enumerate, for a given parameter range, the Boolean functions implementable by a binary neuron model with a linear sub-unit and either a single spiking or a saturating dendritic sub-unit. We then analytically generalize these numerical results to an arbitrary number of non-linear sub-units. First, we show that a single non-linear dendritic sub-unit, in addition to the somatic non-linearity, is sufficient to compute linearly non-separable functions. Second, we analytically prove that, with a sufficient number of saturating dendritic sub-units, a neuron can compute all functions computable with purely excitatory inputs. Third, we show that these linearly non-separable functions can be implemented with at least two strategies: one where a dendritic sub-unit is sufficient to trigger a somatic spike; another where somatic spiking requires the cooperation of multiple dendritic sub-units. We formally prove that implementing the latter architecture is possible with both types of dendritic sub-units whereas the former is only possible with spiking dendrites. Finally, we show how linearly non-separable functions can be computed by a generic two-compartment biophysical model and a realistic neuron model of the cerebellar stellate cell interneuron. Taken together our results demonstrate that passive dendrites are sufficient to enable neurons to compute linearly non-separable functions. PMID:23468600

Evaluation of tablet computers for visual function assessment.

PubMed

Bodduluri, Lakshmi; Boon, Mei Ying; Dain, Stephen J

2017-04-01

Recent advances in technology and the increased use of tablet computers for mobile health applications such as vision testing necessitate an understanding of the behavior of the displays of such devices, to facilitate the reproduction of existing or the development of new vision assessment tests. The purpose of this study was to investigate the physical characteristics of one model of tablet computer (iPad mini Retina display) with regard to display consistency across a set of devices (15) and their potential application as clinical vision assessment tools. Once the tablet computer was switched on, it required about 13 min to reach luminance stability, while chromaticity remained constant. The luminance output of the device remained stable until a battery level of 5%. Luminance varied from center to peripheral locations of the display and with viewing angle, whereas the chromaticity did not vary. A minimal (1%) variation in luminance was observed due to temperature, and once again chromaticity remained constant. Also, these devices showed good temporal stability of luminance and chromaticity. All 15 tablet computers showed gamma functions approximating the standard gamma (2.20) and showed similar color gamut sizes, except for the blue primary, which displayed minimal variations. The physical characteristics across the 15 devices were similar and are known, thereby facilitating the use of this model of tablet computer as visual stimulus displays.

Fast multigrid-based computation of the induced electric field for transcranial magnetic stimulation

NASA Astrophysics Data System (ADS)

Laakso, Ilkka; Hirata, Akimasa

2012-12-01

In transcranial magnetic stimulation (TMS), the distribution of the induced electric field, and the affected brain areas, depends on the position of the stimulation coil and the individual geometry of the head and brain. The distribution of the induced electric field in realistic anatomies can be modelled using computational methods. However, existing computational methods for accurately determining the induced electric field in realistic anatomical models have suffered from long computation times, typically in the range of tens of minutes or longer. This paper presents a matrix-free implementation of the finite-element method with a geometric multigrid method that can potentially reduce the computation time to several seconds or less even when using an ordinary computer. The performance of the method is studied by computing the induced electric field in two anatomically realistic models. An idealized two-loop coil is used as the stimulating coil. Multiple computational grid resolutions ranging from 2 to 0.25 mm are used. The results show that, for macroscopic modelling of the electric field in an anatomically realistic model, computational grid resolutions of 1 mm or 2 mm appear to provide good numerical accuracy compared to higher resolutions. The multigrid iteration typically converges in less than ten iterations independent of the grid resolution. Even without parallelization, each iteration takes about 1.0 s or 0.1 s for the 1 and 2 mm resolutions, respectively. This suggests that calculating the electric field with sufficient accuracy in real time is feasible.

Sensitivity Analysis for Coupled Aero-structural Systems

NASA Technical Reports Server (NTRS)

Giunta, Anthony A.

1999-01-01

A novel method has been developed for calculating gradients of aerodynamic force and moment coefficients for an aeroelastic aircraft model. This method uses the Global Sensitivity Equations (GSE) to account for the aero-structural coupling, and a reduced-order modal analysis approach to condense the coupling bandwidth between the aerodynamic and structural models. Parallel computing is applied to reduce the computational expense of the numerous high fidelity aerodynamic analyses needed for the coupled aero-structural system. Good agreement is obtained between aerodynamic force and moment gradients computed with the GSE/modal analysis approach and the same quantities computed using brute-force, computationally expensive, finite difference approximations. A comparison between the computational expense of the GSE/modal analysis method and a pure finite difference approach is presented. These results show that the GSE/modal analysis approach is the more computationally efficient technique if sensitivity analysis is to be performed for two or more aircraft design parameters.

De Broglie-Bohm interpretation of a Hořava-Lifshitz quantum cosmology model

NASA Astrophysics Data System (ADS)

Oliveira-Neto, G.; Martins, L. G.; Monerat, G. A.; Corrêa Silva, E. V.

2018-01-01

In this paper, we consider the De Broglie-Bohm interpretation of a Hořava-Lifshitz quantum cosmology model in the presence of a radiation perfect fluid. We compute the Bohm’s trajectory for the scale factor and show that it never goes to zero. That result gives a strong indication that this model is free from singularities at the quantum level. We also compute the quantum potential. That quantity helps in understanding why the scale factor never vanishes.

Adapting to life: ocean biogeochemical modelling and adaptive remeshing

NASA Astrophysics Data System (ADS)

Hill, J.; Popova, E. E.; Ham, D. A.; Piggott, M. D.; Srokosz, M.

2013-11-01

An outstanding problem in biogeochemical modelling of the ocean is that many of the key processes occur intermittently at small scales, such as the sub-mesoscale, that are not well represented in global ocean models. As an example, state-of-the-art models give values of primary production approximately two orders of magnitude lower than those observed in the ocean's oligotrophic gyres, which cover a third of the Earth's surface. This is partly due to their failure to resolve sub-mesoscale phenomena, which play a significant role in nutrient supply. Simply increasing the resolution of the models may be an inefficient computational solution to this problem. An approach based on recent advances in adaptive mesh computational techniques may offer an alternative. Here the first steps in such an approach are described, using the example of a~simple vertical column (quasi 1-D) ocean biogeochemical model. We present a novel method of simulating ocean biogeochemical behaviour on a vertically adaptive computational mesh, where the mesh changes in response to the biogeochemical and physical state of the system throughout the simulation. We show that the model reproduces the general physical and biological behaviour at three ocean stations (India, Papa and Bermuda) as compared to a high-resolution fixed mesh simulation and to observations. The simulations capture both the seasonal and inter-annual variations. The use of an adaptive mesh does not increase the computational error, but reduces the number of mesh elements by a factor of 2-3, so reducing computational overhead. We then show the potential of this method in two case studies where we change the metric used to determine the varying mesh sizes in order to capture the dynamics of chlorophyll at Bermuda and sinking detritus at Papa. We therefore demonstrate adaptive meshes may provide a~suitable numerical technique for simulating seasonal or transient biogeochemical behaviour at high spatial resolution whilst minimising computational cost.

A Computational Fluid Dynamic Model for a Novel Flash Ironmaking Process

NASA Astrophysics Data System (ADS)

Perez-Fontes, Silvia E.; Sohn, Hong Yong; Olivas-Martinez, Miguel

A computational fluid dynamic model for a novel flash ironmaking process based on the direct gaseous reduction of iron oxide concentrates is presented. The model solves the three-dimensional governing equations including both gas-phase and gas-solid reaction kinetics. The turbulence-chemistry interaction in the gas-phase is modeled by the eddy dissipation concept incorporating chemical kinetics. The particle cloud model is used to track the particle phase in a Lagrangian framework. A nucleation and growth kinetics rate expression is adopted to calculate the reduction rate of magnetite concentrate particles. Benchmark experiments reported in the literature for a nonreacting swirling gas jet and a nonpremixed hydrogen jet flame were simulated for validation. The model predictions showed good agreement with measurements in terms of gas velocity, gas temperature and species concentrations. The relevance of the computational model for the analysis of a bench reactor operation and the design of an industrial-pilot plant is discussed.

Tandem internal models execute motor learning in the cerebellum.

PubMed

Honda, Takeru; Nagao, Soichi; Hashimoto, Yuji; Ishikawa, Kinya; Yokota, Takanori; Mizusawa, Hidehiro; Ito, Masao

2018-06-25

In performing skillful movement, humans use predictions from internal models formed by repetition learning. However, the computational organization of internal models in the brain remains unknown. Here, we demonstrate that a computational architecture employing a tandem configuration of forward and inverse internal models enables efficient motor learning in the cerebellum. The model predicted learning adaptations observed in hand-reaching experiments in humans wearing a prism lens and explained the kinetic components of these behavioral adaptations. The tandem system also predicted a form of subliminal motor learning that was experimentally validated after training intentional misses of hand targets. Patients with cerebellar degeneration disease showed behavioral impairments consistent with tandemly arranged internal models. These findings validate computational tandemization of internal models in motor control and its potential uses in more complex forms of learning and cognition. Copyright © 2018 the Author(s). Published by PNAS.

First-order finite-Larmor-radius fluid modeling of tearing and relaxation in a plasma pincha)

NASA Astrophysics Data System (ADS)

King, J. R.; Sovinec, C. R.; Mirnov, V. V.

2012-05-01

Drift and Hall effects on magnetic tearing, island evolution, and relaxation in pinch configurations are investigated using a non-reduced first-order finite-Larmor-radius (FLR) fluid model with the nonideal magnetohydrodynamics (MHD) with rotation, open discussion (NIMROD) code [C.R. Sovinec and J. R. King, J. Comput. Phys. 229, 5803 (2010)]. An unexpected result with a uniform pressure profile is a drift effect that reduces the growth rate when the ion sound gyroradius (ρs) is smaller than the tearing-layer width. This drift is present only with warm-ion FLR modeling, and analytics show that it arises from ∇B and poloidal curvature represented in the Braginskii gyroviscous stress. Nonlinear single-helicity computations with experimentally relevant ρs values show that the warm-ion gyroviscous effects reduce saturated-island widths. Computations with multiple nonlinearly interacting tearing fluctuations find that m = 1 core-resonant-fluctuation amplitudes are reduced by a factor of two relative to single-fluid modeling by the warm-ion effects. These reduced core-resonant-fluctuation amplitudes compare favorably to edge coil measurements in the Madison Symmetric Torus (MST) reversed-field pinch [R. N. Dexter et al., Fusion Technol. 19, 131 (1991)]. The computations demonstrate that fluctuations induce both MHD- and Hall-dynamo emfs during relaxation events. The presence of a Hall-dynamo emf implies a fluctuation-induced Maxwell stress, and the simulation results show net transport of parallel momentum. The computed magnitude of force densities from the Maxwell and competing Reynolds stresses, and changes in the parallel flow profile, are qualitatively and semi-quantitatively similar to measurements during relaxation in MST.

First-order finite-Larmor-radius fluid modeling of tearing and relaxation in a plasma pinch

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

King, J. R.; Tech-X Corporation, 5621 Arapahoe Ave., Suite A Boulder, Colorado 80303; Sovinec, C. R.

Drift and Hall effects on magnetic tearing, island evolution, and relaxation in pinch configurations are investigated using a non-reduced first-order finite-Larmor-radius (FLR) fluid model with the nonideal magnetohydrodynamics (MHD) with rotation, open discussion (NIMROD) code [C.R. Sovinec and J. R. King, J. Comput. Phys. 229, 5803 (2010)]. An unexpected result with a uniform pressure profile is a drift effect that reduces the growth rate when the ion sound gyroradius ({rho}{sub s}) is smaller than the tearing-layer width. This drift is present only with warm-ion FLR modeling, and analytics show that it arises from {nabla}B and poloidal curvature represented in themore » Braginskii gyroviscous stress. Nonlinear single-helicity computations with experimentally relevant {rho}{sub s} values show that the warm-ion gyroviscous effects reduce saturated-island widths. Computations with multiple nonlinearly interacting tearing fluctuations find that m = 1 core-resonant-fluctuation amplitudes are reduced by a factor of two relative to single-fluid modeling by the warm-ion effects. These reduced core-resonant-fluctuation amplitudes compare favorably to edge coil measurements in the Madison Symmetric Torus (MST) reversed-field pinch [R. N. Dexter et al., Fusion Technol. 19, 131 (1991)]. The computations demonstrate that fluctuations induce both MHD- and Hall-dynamo emfs during relaxation events. The presence of a Hall-dynamo emf implies a fluctuation-induced Maxwell stress, and the simulation results show net transport of parallel momentum. The computed magnitude of force densities from the Maxwell and competing Reynolds stresses, and changes in the parallel flow profile, are qualitatively and semi-quantitatively similar to measurements during relaxation in MST.« less

Vision-Based UAV Flight Control and Obstacle Avoidance

DTIC Science & Technology

2006-01-01

denoted it by Vb = (Vb1, Vb2 , Vb3). Fig. 2 shows the block diagram of the proposed vision-based motion analysis and obstacle avoidance system. We denote...structure analysis often involve computation- intensive computer vision tasks, such as feature extraction and geometric modeling. Computation-intensive...First, we extract a set of features from each block. 2) Second, we compute the distance between these two sets of features. In conventional motion

Directional output distance functions: endogenous directions based on exogenous normalization constraints

USDA-ARS?s Scientific Manuscript database

In this paper we develop a model for computing directional output distance functions with endogenously determined direction vectors. We show how this model is related to the slacks-based directional distance function introduced by Fare and Grosskopf and show how to use the slacks-based function to e...

Modeling resident error-making patterns in detection of mammographic masses using computer-extracted image features: preliminary experiments

NASA Astrophysics Data System (ADS)

Mazurowski, Maciej A.; Zhang, Jing; Lo, Joseph Y.; Kuzmiak, Cherie M.; Ghate, Sujata V.; Yoon, Sora

2014-03-01

Providing high quality mammography education to radiology trainees is essential, as good interpretation skills potentially ensure the highest benefit of screening mammography for patients. We have previously proposed a computer-aided education system that utilizes trainee models, which relate human-assessed image characteristics to interpretation error. We proposed that these models be used to identify the most difficult and therefore the most educationally useful cases for each trainee. In this study, as a next step in our research, we propose to build trainee models that utilize features that are automatically extracted from images using computer vision algorithms. To predict error, we used a logistic regression which accepts imaging features as input and returns error as output. Reader data from 3 experts and 3 trainees were used. Receiver operating characteristic analysis was applied to evaluate the proposed trainee models. Our experiments showed that, for three trainees, our models were able to predict error better than chance. This is an important step in the development of adaptive computer-aided education systems since computer-extracted features will allow for faster and more extensive search of imaging databases in order to identify the most educationally beneficial cases.

Methods for modeling cytoskeletal and DNA filaments

NASA Astrophysics Data System (ADS)

Andrews, Steven S.

2014-02-01

This review summarizes the models that researchers use to represent the conformations and dynamics of cytoskeletal and DNA filaments. It focuses on models that address individual filaments in continuous space. Conformation models include the freely jointed, Gaussian, angle-biased chain (ABC), and wormlike chain (WLC) models, of which the first three bend at discrete joints and the last bends continuously. Predictions from the WLC model generally agree well with experiment. Dynamics models include the Rouse, Zimm, stiff rod, dynamic WLC, and reptation models, of which the first four apply to isolated filaments and the last to entangled filaments. Experiments show that the dynamic WLC and reptation models are most accurate. They also show that biological filaments typically experience strong hydrodynamic coupling and/or constrained motion. Computer simulation methods that address filament dynamics typically compute filament segment velocities from local forces using the Langevin equation and then integrate these velocities with explicit or implicit methods; the former are more versatile and the latter are more efficient. Much remains to be discovered in biological filament modeling. In particular, filament dynamics in living cells are not well understood, and current computational methods are too slow and not sufficiently versatile. Although primarily a review, this paper also presents new statistical calculations for the ABC and WLC models. Additionally, it corrects several discrepancies in the literature about bending and torsional persistence length definitions, and their relations to flexural and torsional rigidities.

Model-Based and Model-Free Pavlovian Reward Learning: Revaluation, Revision and Revelation

PubMed Central

Dayan, Peter; Berridge, Kent C.

2014-01-01

Evidence supports at least two methods for learning about reward and punishment and making predictions for guiding actions. One method, called model-free, progressively acquires cached estimates of the long-run values of circumstances and actions from retrospective experience. The other method, called model-based, uses representations of the environment, expectations and prospective calculations to make cognitive predictions of future value. Extensive attention has been paid to both methods in computational analyses of instrumental learning. By contrast, although a full computational analysis has been lacking, Pavlovian learning and prediction has typically been presumed to be solely model-free. Here, we revise that presumption and review compelling evidence from Pavlovian revaluation experiments showing that Pavlovian predictions can involve their own form of model-based evaluation. In model-based Pavlovian evaluation, prevailing states of the body and brain influence value computations, and thereby produce powerful incentive motivations that can sometimes be quite new. We consider the consequences of this revised Pavlovian view for the computational landscape of prediction, response and choice. We also revisit differences between Pavlovian and instrumental learning in the control of incentive motivation. PMID:24647659

Model-based and model-free Pavlovian reward learning: revaluation, revision, and revelation.

PubMed

Dayan, Peter; Berridge, Kent C

2014-06-01

Evidence supports at least two methods for learning about reward and punishment and making predictions for guiding actions. One method, called model-free, progressively acquires cached estimates of the long-run values of circumstances and actions from retrospective experience. The other method, called model-based, uses representations of the environment, expectations, and prospective calculations to make cognitive predictions of future value. Extensive attention has been paid to both methods in computational analyses of instrumental learning. By contrast, although a full computational analysis has been lacking, Pavlovian learning and prediction has typically been presumed to be solely model-free. Here, we revise that presumption and review compelling evidence from Pavlovian revaluation experiments showing that Pavlovian predictions can involve their own form of model-based evaluation. In model-based Pavlovian evaluation, prevailing states of the body and brain influence value computations, and thereby produce powerful incentive motivations that can sometimes be quite new. We consider the consequences of this revised Pavlovian view for the computational landscape of prediction, response, and choice. We also revisit differences between Pavlovian and instrumental learning in the control of incentive motivation.

Modelling heat transfer during flow through a random packed bed of spheres

NASA Astrophysics Data System (ADS)

Burström, Per E. C.; Frishfelds, Vilnis; Ljung, Anna-Lena; Lundström, T. Staffan; Marjavaara, B. Daniel

2018-04-01

Heat transfer in a random packed bed of monosized iron ore pellets is modelled with both a discrete three-dimensional system of spheres and a continuous Computational Fluid Dynamics (CFD) model. Results show a good agreement between the two models for average values over a cross section of the bed for an even temperature profiles at the inlet. The advantage with the discrete model is that it captures local effects such as decreased heat transfer in sections with low speed. The disadvantage is that it is computationally heavy for larger systems of pellets. If averaged values are sufficient, the CFD model is an attractive alternative that is easy to couple to the physics up- and downstream the packed bed. The good agreement between the discrete and continuous model furthermore indicates that the discrete model may be used also on non-Stokian flow in the transitional region between laminar and turbulent flow, as turbulent effects show little influence of the overall heat transfer rates in the continuous model.

Computer Analysis of Air Pollution from Highways, Streets, and Complex Interchanges

DOT National Transportation Integrated Search

1974-03-01

A detailed computer analysis of air quality for a complex highway interchange was prepared, using an in-house version of the Environmental Protection Agency's Gaussian Highway Line Source Model. This analysis showed that the levels of air pollution n...

Near-wall k-epsilon turbulence modeling

NASA Technical Reports Server (NTRS)

Mansour, N. N.; Kim, J.; Moin, P.

1987-01-01

The flow fields from a turbulent channel simulation are used to compute the budgets for the turbulent kinetic energy (k) and its dissipation rate (epsilon). Data from boundary layer simulations are used to analyze the dependence of the eddy-viscosity damping-function on the Reynolds number and the distance from the wall. The computed budgets are used to test existing near-wall turbulence models of the k-epsilon type. It was found that the turbulent transport models should be modified in the vicinity of the wall. It was also found that existing models for the different terms in the epsilon-budget are adequate in the region from the wall, but need modification near the wall. The channel flow is computed using a k-epsilon model with an eddy-viscosity damping function from the data and no damping functions in the epsilon-equation. These computations show that the k-profile can be adequately predicted, but to correctly predict the epsilon-profile, damping functions in the epsilon-equation are needed.

Dynamic Computation of Change Operations in Version Management of Business Process Models

NASA Astrophysics Data System (ADS)

Küster, Jochen Malte; Gerth, Christian; Engels, Gregor

Version management of business process models requires that changes can be resolved by applying change operations. In order to give a user maximal freedom concerning the application order of change operations, position parameters of change operations must be computed dynamically during change resolution. In such an approach, change operations with computed position parameters must be applicable on the model and dependencies and conflicts of change operations must be taken into account because otherwise invalid models can be constructed. In this paper, we study the concept of partially specified change operations where parameters are computed dynamically. We provide a formalization for partially specified change operations using graph transformation and provide a concept for their applicability. Based on this, we study potential dependencies and conflicts of change operations and show how these can be taken into account within change resolution. Using our approach, a user can resolve changes of business process models without being unnecessarily restricted to a certain order.

Using Predictability for Lexical Segmentation.

PubMed

Çöltekin, Çağrı

2017-09-01

This study investigates a strategy based on predictability of consecutive sub-lexical units in learning to segment a continuous speech stream into lexical units using computational modeling and simulations. Lexical segmentation is one of the early challenges during language acquisition, and it has been studied extensively through psycholinguistic experiments as well as computational methods. However, despite strong empirical evidence, the explicit use of predictability of basic sub-lexical units in models of segmentation is underexplored. This paper presents an incremental computational model of lexical segmentation for exploring the usefulness of predictability for lexical segmentation. We show that the predictability cue is a strong cue for segmentation. Contrary to earlier reports in the literature, the strategy yields state-of-the-art segmentation performance with an incremental computational model that uses only this particular cue in a cognitively plausible setting. The paper also reports an in-depth analysis of the model, investigating the conditions affecting the usefulness of the strategy. Copyright © 2016 Cognitive Science Society, Inc.

Computational upscaling of Drucker-Prager plasticity from micro-CT images of synthetic porous rock

NASA Astrophysics Data System (ADS)

Liu, Jie; Sarout, Joel; Zhang, Minchao; Dautriat, Jeremie; Veveakis, Emmanouil; Regenauer-Lieb, Klaus

2018-01-01

Quantifying rock physical properties is essential for the mining and petroleum industry. Microtomography provides a new way to quantify the relationship between the microstructure and the mechanical and transport properties of a rock. Studies reporting the use microtomographic images to derive permeability and elastic moduli of rocks are common; only rare studies were devoted to yield and failure parameters using this technique. In this study, we simulate the macroscale plastic properties of a synthetic sandstone sample made of calcite-cemented quartz grains using the microscale information obtained from microtomography. The computations rely on the concept of representative volume elements (RVEs). The mechanical RVE is determined using the upper and lower bounds of finite-element computations for elasticity. We present computational upscaling methods from microphysical processes to extract the plasticity parameters of the RVE and compare results to experimental data. The yield stress, cohesion and internal friction angle of the matrix (solid part) of the rock were obtained with reasonable accuracy. Computations of plasticity of a series of models of different volume-sizes showed almost overlapping stress-strain curves, suggesting that the mechanical RVE determined by elastic computations is also valid for plastic yielding. Furthermore, a series of models were created by self-similarly inflating/deflating the porous models, that is keeping a similar structure while achieving different porosity values. The analysis of these models showed that yield stress, cohesion and internal friction angle linearly decrease with increasing porosity in the porosity range between 8 and 28 per cent. The internal friction angle decreases the most significantly, while cohesion remains stable.

Constructing a patient-specific computer model of the upper airway in sleep apnea patients.

PubMed

Dhaliwal, Sandeep S; Hesabgar, Seyyed M; Haddad, Seyyed M H; Ladak, Hanif; Samani, Abbas; Rotenberg, Brian W

2018-01-01

The use of computer simulation to develop a high-fidelity model has been proposed as a novel and cost-effective alternative to help guide therapeutic intervention in sleep apnea surgery. We describe a computer model based on patient-specific anatomy of obstructive sleep apnea (OSA) subjects wherein the percentage and sites of upper airway collapse are compared to findings on drug-induced sleep endoscopy (DISE). Basic science computer model generation. Three-dimensional finite element techniques were undertaken for model development in a pilot study of four OSA patients. Magnetic resonance imaging was used to capture patient anatomy and software employed to outline critical anatomical structures. A finite-element mesh was applied to the volume enclosed by each structure. Linear and hyperelastic soft-tissue properties for various subsites (tonsils, uvula, soft palate, and tongue base) were derived using an inverse finite-element technique from surgical specimens. Each model underwent computer simulation to determine the degree of displacement on various structures within the upper airway, and these findings were compared to DISE exams performed on the four study patients. Computer simulation predictions for percentage of airway collapse and site of maximal collapse show agreement with observed results seen on endoscopic visualization. Modeling the upper airway in OSA patients is feasible and holds promise in aiding patient-specific surgical treatment. NA. Laryngoscope, 128:277-282, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

Tertiary structure-based analysis of microRNA–target interactions

PubMed Central

Gan, Hin Hark; Gunsalus, Kristin C.

2013-01-01

Current computational analysis of microRNA interactions is based largely on primary and secondary structure analysis. Computationally efficient tertiary structure-based methods are needed to enable more realistic modeling of the molecular interactions underlying miRNA-mediated translational repression. We incorporate algorithms for predicting duplex RNA structures, ionic strength effects, duplex entropy and free energy, and docking of duplex–Argonaute protein complexes into a pipeline to model and predict miRNA–target duplex binding energies. To ensure modeling accuracy and computational efficiency, we use an all-atom description of RNA and a continuum description of ionic interactions using the Poisson–Boltzmann equation. Our method predicts the conformations of two constructs of Caenorhabditis elegans let-7 miRNA–target duplexes to an accuracy of ∼3.8 Å root mean square distance of their NMR structures. We also show that the computed duplex formation enthalpies, entropies, and free energies for eight miRNA–target duplexes agree with titration calorimetry data. Analysis of duplex–Argonaute docking shows that structural distortions arising from single-base-pair mismatches in the seed region influence the activity of the complex by destabilizing both duplex hybridization and its association with Argonaute. Collectively, these results demonstrate that tertiary structure-based modeling of miRNA interactions can reveal structural mechanisms not accessible with current secondary structure-based methods. PMID:23417009

Image analysis and modeling in medical image computing. Recent developments and advances.

PubMed

Handels, H; Deserno, T M; Meinzer, H-P; Tolxdorff, T

2012-01-01

Medical image computing is of growing importance in medical diagnostics and image-guided therapy. Nowadays, image analysis systems integrating advanced image computing methods are used in practice e.g. to extract quantitative image parameters or to support the surgeon during a navigated intervention. However, the grade of automation, accuracy, reproducibility and robustness of medical image computing methods has to be increased to meet the requirements in clinical routine. In the focus theme, recent developments and advances in the field of modeling and model-based image analysis are described. The introduction of models in the image analysis process enables improvements of image analysis algorithms in terms of automation, accuracy, reproducibility and robustness. Furthermore, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients. Selected contributions are assembled to present latest advances in the field. The authors were invited to present their recent work and results based on their outstanding contributions to the Conference on Medical Image Computing BVM 2011 held at the University of Lübeck, Germany. All manuscripts had to pass a comprehensive peer review. Modeling approaches and model-based image analysis methods showing new trends and perspectives in model-based medical image computing are described. Complex models are used in different medical applications and medical images like radiographic images, dual-energy CT images, MR images, diffusion tensor images as well as microscopic images are analyzed. The applications emphasize the high potential and the wide application range of these methods. The use of model-based image analysis methods can improve segmentation quality as well as the accuracy and reproducibility of quantitative image analysis. Furthermore, image-based models enable new insights and can lead to a deeper understanding of complex dynamic mechanisms in the human body. Hence, model-based image computing methods are important tools to improve medical diagnostics and patient treatment in future.

Computing chemical organizations in biological networks.

PubMed

Centler, Florian; Kaleta, Christoph; di Fenizio, Pietro Speroni; Dittrich, Peter

2008-07-15

Novel techniques are required to analyze computational models of intracellular processes as they increase steadily in size and complexity. The theory of chemical organizations has recently been introduced as such a technique that links the topology of biochemical reaction network models to their dynamical repertoire. The network is decomposed into algebraically closed and self-maintaining subnetworks called organizations. They form a hierarchy representing all feasible system states including all steady states. We present three algorithms to compute the hierarchy of organizations for network models provided in SBML format. Two of them compute the complete organization hierarchy, while the third one uses heuristics to obtain a subset of all organizations for large models. While the constructive approach computes the hierarchy starting from the smallest organization in a bottom-up fashion, the flux-based approach employs self-maintaining flux distributions to determine organizations. A runtime comparison on 16 different network models of natural systems showed that none of the two exhaustive algorithms is superior in all cases. Studying a 'genome-scale' network model with 762 species and 1193 reactions, we demonstrate how the organization hierarchy helps to uncover the model structure and allows to evaluate the model's quality, for example by detecting components and subsystems of the model whose maintenance is not explained by the model. All data and a Java implementation that plugs into the Systems Biology Workbench is available from http://www.minet.uni-jena.de/csb/prj/ot/tools.

Accelerating the spin-up of the coupled carbon and nitrogen cycle model in CLM4

DOE PAGES

Fang, Yilin; Liu, Chongxuan; Leung, Lai-Yung R.

2015-03-24

The commonly adopted biogeochemistry spin-up process in an Earth system model (ESM) is to run the model for hundreds to thousands of years subject to periodic atmospheric forcing to reach dynamic steady state of the carbon–nitrogen (CN) models. A variety of approaches have been proposed to reduce the computation time of the spin-up process. Significant improvement in computational efficiency has been made recently. However, a long simulation time is still required to reach the common convergence criteria of the coupled carbon–nitrogen model. A gradient projection method was proposed and used to further reduce the computation time after examining the trendmore » of the dominant carbon pools. The Community Land Model version 4 (CLM4) with a carbon and nitrogen component was used in this study. From point-scale simulations, we found that the method can reduce the computation time by 20–69% compared to one of the fastest approaches in the literature. We also found that the cyclic stability of total carbon for some cases differs from that of the periodic atmospheric forcing, and some cases even showed instability. Close examination showed that one case has a carbon periodicity much longer than that of the atmospheric forcing due to the annual fire disturbance that is longer than half a year. The rest was caused by the instability of water table calculation in the hydrology model of CLM4. The instability issue is resolved after we replaced the hydrology scheme in CLM4 with a flow model for variably saturated porous media.« less

A novel semi-transductive learning framework for efficient atypicality detection in chest radiographs

NASA Astrophysics Data System (ADS)

Alzubaidi, Mohammad; Balasubramanian, Vineeth; Patel, Ameet; Panchanathan, Sethuraman; Black, John A., Jr.

2012-03-01

Inductive learning refers to machine learning algorithms that learn a model from a set of training data instances. Any test instance is then classified by comparing it to the learned model. When the set of training instances lend themselves well to modeling, the use of a model substantially reduces the computation cost of classification. However, some training data sets are complex, and do not lend themselves well to modeling. Transductive learning refers to machine learning algorithms that classify test instances by comparing them to all of the training instances, without creating an explicit model. This can produce better classification performance, but at a much higher computational cost. Medical images vary greatly across human populations, constituting a data set that does not lend itself well to modeling. Our previous work showed that the wide variations seen across training sets of "normal" chest radiographs make it difficult to successfully classify test radiographs with an inductive (modeling) approach, and that a transductive approach leads to much better performance in detecting atypical regions. The problem with the transductive approach is its high computational cost. This paper develops and demonstrates a novel semi-transductive framework that can address the unique challenges of atypicality detection in chest radiographs. The proposed framework combines the superior performance of transductive methods with the reduced computational cost of inductive methods. Our results show that the proposed semitransductive approach provides both effective and efficient detection of atypical regions within a set of chest radiographs previously labeled by Mayo Clinic expert thoracic radiologists.

The putative liquid-liquid transition is a liquid-solid transition in atomistic models of water. II

NASA Astrophysics Data System (ADS)

Limmer, David T.; Chandler, David

2013-06-01

This paper extends our earlier studies of free energy functions of density and crystalline order parameters for models of supercooled water, which allows us to examine the possibility of two distinct metastable liquid phases [D. T. Limmer and D. Chandler, J. Chem. Phys. 135, 134503 (2011), 10.1063/1.3643333 and preprint arXiv:1107.0337 (2011)]. Low-temperature reversible free energy surfaces of several different atomistic models are computed: mW water, TIP4P/2005 water, Stillinger-Weber silicon, and ST2 water, the last of these comparing three different treatments of long-ranged forces. In each case, we show that there is one stable or metastable liquid phase, and there is an ice-like crystal phase. The time scales for crystallization in these systems far exceed those of structural relaxation in the supercooled metastable liquid. We show how this wide separation in time scales produces an illusion of a low-temperature liquid-liquid transition. The phenomenon suggesting metastability of two distinct liquid phases is actually coarsening of the ordered ice-like phase, which we elucidate using both analytical theory and computer simulation. For the latter, we describe robust methods for computing reversible free energy surfaces, and we consider effects of electrostatic boundary conditions. We show that sensible alterations of models and boundary conditions produce no qualitative changes in low-temperature phase behaviors of these systems, only marginal changes in equations of state. On the other hand, we show that altering sampling time scales can produce large and qualitative non-equilibrium effects. Recent reports of evidence of a liquid-liquid critical point in computer simulations of supercooled water are considered in this light.

The putative liquid-liquid transition is a liquid-solid transition in atomistic models of water. II.

PubMed

Limmer, David T; Chandler, David

2013-06-07

This paper extends our earlier studies of free energy functions of density and crystalline order parameters for models of supercooled water, which allows us to examine the possibility of two distinct metastable liquid phases [D. T. Limmer and D. Chandler, J. Chem. Phys. 135, 134503 (2011) and preprint arXiv:1107.0337 (2011)]. Low-temperature reversible free energy surfaces of several different atomistic models are computed: mW water, TIP4P/2005 water, Stillinger-Weber silicon, and ST2 water, the last of these comparing three different treatments of long-ranged forces. In each case, we show that there is one stable or metastable liquid phase, and there is an ice-like crystal phase. The time scales for crystallization in these systems far exceed those of structural relaxation in the supercooled metastable liquid. We show how this wide separation in time scales produces an illusion of a low-temperature liquid-liquid transition. The phenomenon suggesting metastability of two distinct liquid phases is actually coarsening of the ordered ice-like phase, which we elucidate using both analytical theory and computer simulation. For the latter, we describe robust methods for computing reversible free energy surfaces, and we consider effects of electrostatic boundary conditions. We show that sensible alterations of models and boundary conditions produce no qualitative changes in low-temperature phase behaviors of these systems, only marginal changes in equations of state. On the other hand, we show that altering sampling time scales can produce large and qualitative non-equilibrium effects. Recent reports of evidence of a liquid-liquid critical point in computer simulations of supercooled water are considered in this light.

Taking error into account when fitting models using Approximate Bayesian Computation.

PubMed

van der Vaart, Elske; Prangle, Dennis; Sibly, Richard M

2018-03-01

Stochastic computer simulations are often the only practical way of answering questions relating to ecological management. However, due to their complexity, such models are difficult to calibrate and evaluate. Approximate Bayesian Computation (ABC) offers an increasingly popular approach to this problem, widely applied across a variety of fields. However, ensuring the accuracy of ABC's estimates has been difficult. Here, we obtain more accurate estimates by incorporating estimation of error into the ABC protocol. We show how this can be done where the data consist of repeated measures of the same quantity and errors may be assumed to be normally distributed and independent. We then derive the correct acceptance probabilities for a probabilistic ABC algorithm, and update the coverage test with which accuracy is assessed. We apply this method, which we call error-calibrated ABC, to a toy example and a realistic 14-parameter simulation model of earthworms that is used in environmental risk assessment. A comparison with exact methods and the diagnostic coverage test show that our approach improves estimation of parameter values and their credible intervals for both models. © 2017 by the Ecological Society of America.

Numerical Study On Propulsion Performance Of The Parabolic Laser Thruster With Elongate Cylinder Nozzle

NASA Astrophysics Data System (ADS)

Cheng, Fuqiang; Hong, Yanji; Li, Qian; Wen, Ming

2011-11-01

Laser thrusters with a single nozzle, e.g. parabolic or conical, failed to constrict the flow field of high pressure effectively, resulting in poor propulsive performance. Under the condition of air-breathing mode, parabolic thruster models with an elongate cylinder nozzle were studied numerically by building a physical computation model. Initially, to verify the computation model, the influence of cylinder length on the momentum coupling coefficient was computed and compared with the experiments, which shows a good congruence. A model of diameter 20 mm and cylindrical length 80 mm obtains about 627.7 N/MW at single pulse energy density 1.5 J/cm2. Then, the influence of expanding angle of the parabolic nozzle on propulsion performance was gained for different laser pulse energies, and the evolution process of the flow field was analyzed. The results show: as the expanding angel increases, the momentum coupling coefficient increases remarkably at first and descends relative slowly after reaching a peak value; moreover, the peak positions stay constant around 33° with little variation when laser energy differs.

Structural Acoustic Physics Based Modeling of Curved Composite Shells

DTIC Science & Technology

2017-09-19

Results show that the finite element computational models accurately match analytical calculations, and that the composite material studied in this...products. 15. SUBJECT TERMS Finite Element Analysis, Structural Acoustics, Fiber-Reinforced Composites, Physics-Based Modeling 16. SECURITY...2 4 FINITE ELEMENT MODEL DESCRIPTION

An attempt to obtain a detailed declination chart from the United States magnetic anomaly map

USGS Publications Warehouse

Alldredge, L.R.

1989-01-01

Modern declination charts of the United States show almost no details. It was hoped that declination details could be derived from the information contained in the existing magnetic anomaly map of the United States. This could be realized only if all of the survey data were corrected to a common epoch, at which time a main-field vector model was known, before the anomaly values were computed. Because this was not done, accurate declination values cannot be determined. In spite of this conclusion, declination values were computed using a common main-field model for the entire United States to see how well they compared with observed values. The computed detailed declination values were found to compare less favourably with observed values of declination than declination values computed from the IGRF 1985 model itself. -from Author

Aeroelastic Calculations Using CFD for a Typical Business Jet Model

NASA Technical Reports Server (NTRS)

Gibbons, Michael D.

1996-01-01

Two time-accurate Computational Fluid Dynamics (CFD) codes were used to compute several flutter points for a typical business jet model. The model consisted of a rigid fuselage with a flexible semispan wing and was tested in the Transonic Dynamics Tunnel at NASA Langley Research Center where experimental flutter data were obtained from M(sub infinity) = 0.628 to M(sub infinity) = 0.888. The computational results were computed using CFD codes based on the inviscid TSD equation (CAP-TSD) and the Euler/Navier-Stokes equations (CFL3D-AE). Comparisons are made between analytical results and with experiment where appropriate. The results presented here show that the Navier-Stokes method is required near the transonic dip due to the strong viscous effects while the TSD and Euler methods used here provide good results at the lower Mach numbers.

Development of Reduced-Order Models for Aeroelastic and Flutter Prediction Using the CFL3Dv6.0 Code

NASA Technical Reports Server (NTRS)

Silva, Walter A.; Bartels, Robert E.

2002-01-01

A reduced-order model (ROM) is developed for aeroelastic analysis using the CFL3D version 6.0 computational fluid dynamics (CFD) code, recently developed at the NASA Langley Research Center. This latest version of the flow solver includes a deforming mesh capability, a modal structural definition for nonlinear aeroelastic analyses, and a parallelization capability that provides a significant increase in computational efficiency. Flutter results for the AGARD 445.6 Wing computed using CFL3D v6.0 are presented, including discussion of associated computational costs. Modal impulse responses of the unsteady aerodynamic system are then computed using the CFL3Dv6 code and transformed into state-space form. Important numerical issues associated with the computation of the impulse responses are presented. The unsteady aerodynamic state-space ROM is then combined with a state-space model of the structure to create an aeroelastic simulation using the MATLAB/SIMULINK environment. The MATLAB/SIMULINK ROM is used to rapidly compute aeroelastic transients including flutter. The ROM shows excellent agreement with the aeroelastic analyses computed using the CFL3Dv6.0 code directly.

Optimal control strategy for a novel computer virus propagation model on scale-free networks

NASA Astrophysics Data System (ADS)

Zhang, Chunming; Huang, Haitao

2016-06-01

This paper aims to study the combined impact of reinstalling system and network topology on the spread of computer viruses over the Internet. Based on scale-free network, this paper proposes a novel computer viruses propagation model-SLBOSmodel. A systematic analysis of this new model shows that the virus-free equilibrium is globally asymptotically stable when its spreading threshold is less than one; nevertheless, it is proved that the viral equilibrium is permanent if the spreading threshold is greater than one. Then, the impacts of different model parameters on spreading threshold are analyzed. Next, an optimally controlled SLBOS epidemic model on complex networks is also studied. We prove that there is an optimal control existing for the control problem. Some numerical simulations are finally given to illustrate the main results.

Noise optimization of a regenerative automotive fuel pump

NASA Astrophysics Data System (ADS)

Wang, J. F.; Feng, H. H.; Mou, X. L.; Huang, Y. X.

2017-03-01

The regenerative pump used in automotive is facing a noise problem. To understand the mechanism in detail, Computational Fluid Dynamics (CFD) and Computational Acoustic Analysis (CAA) together were used to understand the fluid and acoustic characteristics of the fuel pump using ANSYS-CFX 15.0 and LMS Virtual. Lab Rev12, respectively. The CFD model and acoustical model were validated by mass flow rate test and sound pressure test, respectively. Comparing the computational and experimental results shows that sound pressure levels at the observer position are consistent at high frequencies, especially at blade passing frequency. After validating the models, several numerical models were analyzed in the study for noise improvement. It is observed that for configuration having greater number of impeller blades, noise level was significantly improved at blade passing frequency, when compared to that of the original model.

PDB_REDO: automated re-refinement of X-ray structure models in the PDB.

PubMed

Joosten, Robbie P; Salzemann, Jean; Bloch, Vincent; Stockinger, Heinz; Berglund, Ann-Charlott; Blanchet, Christophe; Bongcam-Rudloff, Erik; Combet, Christophe; Da Costa, Ana L; Deleage, Gilbert; Diarena, Matteo; Fabbretti, Roberto; Fettahi, Géraldine; Flegel, Volker; Gisel, Andreas; Kasam, Vinod; Kervinen, Timo; Korpelainen, Eija; Mattila, Kimmo; Pagni, Marco; Reichstadt, Matthieu; Breton, Vincent; Tickle, Ian J; Vriend, Gert

2009-06-01

Structural biology, homology modelling and rational drug design require accurate three-dimensional macromolecular coordinates. However, the coordinates in the Protein Data Bank (PDB) have not all been obtained using the latest experimental and computational methods. In this study a method is presented for automated re-refinement of existing structure models in the PDB. A large-scale benchmark with 16 807 PDB entries showed that they can be improved in terms of fit to the deposited experimental X-ray data as well as in terms of geometric quality. The re-refinement protocol uses TLS models to describe concerted atom movement. The resulting structure models are made available through the PDB_REDO databank (http://www.cmbi.ru.nl/pdb_redo/). Grid computing techniques were used to overcome the computational requirements of this endeavour.

Influence of computational domain size on the pattern formation of the phase field crystals

NASA Astrophysics Data System (ADS)

Starodumov, Ilya; Galenko, Peter; Alexandrov, Dmitri; Kropotin, Nikolai

2017-04-01

Modeling of crystallization process by the phase field crystal method (PFC) represents one of the important directions of modern computational materials science. This method makes it possible to research the formation of stable or metastable crystal structures. In this paper, we study the effect of computational domain size on the crystal pattern formation obtained as a result of computer simulation by the PFC method. In the current report, we show that if the size of a computational domain is changed, the result of modeling may be a structure in metastable phase instead of pure stable state. The authors present a possible theoretical justification for the observed effect and provide explanations on the possible modification of the PFC method to account for this phenomenon.

Improved Gaussian Beam-Scattering Algorithm

NASA Technical Reports Server (NTRS)

Lock, James A.

1995-01-01

The localized model of the beam-shape coefficients for Gaussian beam-scattering theory by a spherical particle provides a great simplification in the numerical implementation of the theory. We derive an alternative form for the localized coefficients that is more convenient for computer computations and that provides physical insight into the details of the scattering process. We construct a FORTRAN program for Gaussian beam scattering with the localized model and compare its computer run time on a personal computer with that of a traditional Mie scattering program and with three other published methods for computing Gaussian beam scattering. We show that the analytical form of the beam-shape coefficients makes evident the fact that the excitation rate of morphology-dependent resonances is greatly enhanced for far off-axis incidence of the Gaussian beam.

Regional ionospheric model for improvement of navigation position with EGNOS

NASA Astrophysics Data System (ADS)

Swiatek, Anna; Tomasik, Lukasz; Jaworski, Leszek

The problem of insufficient accuracy of EGNOS correction for the territory of Poland, located at the edge of EGNOS range is well known. The EEI PECS project (EGNOS EUPOS Integration) assumed improving the EGNOS correction by using the GPS observations from Polish ASG-EUPOS stations. A ionospheric delay parameter is a part of EGNOS correction. The comparative analysis of TEC values obtained from EGNOS and regional permanent GNSS stations showed the systematic shift. The TEC from EGNOS correction is underestimated related to computed regional TEC value. The new-‘improved’ corrections computed based on regional model were substituted for the EGNOS correction for suitable message. Dynamic measurements managed using the Mobile GPS Laboratory (MGL), showed the improvement of navigation position with TEC regional model.

Parameter estimation methods for gene circuit modeling from time-series mRNA data: a comparative study.

PubMed

Fan, Ming; Kuwahara, Hiroyuki; Wang, Xiaolei; Wang, Suojin; Gao, Xin

2015-11-01

Parameter estimation is a challenging computational problem in the reverse engineering of biological systems. Because advances in biotechnology have facilitated wide availability of time-series gene expression data, systematic parameter estimation of gene circuit models from such time-series mRNA data has become an important method for quantitatively dissecting the regulation of gene expression. By focusing on the modeling of gene circuits, we examine here the performance of three types of state-of-the-art parameter estimation methods: population-based methods, online methods and model-decomposition-based methods. Our results show that certain population-based methods are able to generate high-quality parameter solutions. The performance of these methods, however, is heavily dependent on the size of the parameter search space, and their computational requirements substantially increase as the size of the search space increases. In comparison, online methods and model decomposition-based methods are computationally faster alternatives and are less dependent on the size of the search space. Among other things, our results show that a hybrid approach that augments computationally fast methods with local search as a subsequent refinement procedure can substantially increase the quality of their parameter estimates to the level on par with the best solution obtained from the population-based methods while maintaining high computational speed. These suggest that such hybrid methods can be a promising alternative to the more commonly used population-based methods for parameter estimation of gene circuit models when limited prior knowledge about the underlying regulatory mechanisms makes the size of the parameter search space vastly large. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Effect of Anatomically Realistic Full-Head Model on Activation of Cortical Neurons in Subdural Cortical Stimulation—A Computational Study

NASA Astrophysics Data System (ADS)

Seo, Hyeon; Kim, Donghyeon; Jun, Sung Chan

2016-06-01

Electrical brain stimulation (EBS) is an emerging therapy for the treatment of neurological disorders, and computational modeling studies of EBS have been used to determine the optimal parameters for highly cost-effective electrotherapy. Recent notable growth in computing capability has enabled researchers to consider an anatomically realistic head model that represents the full head and complex geometry of the brain rather than the previous simplified partial head model (extruded slab) that represents only the precentral gyrus. In this work, subdural cortical stimulation (SuCS) was found to offer a better understanding of the differential activation of cortical neurons in the anatomically realistic full-head model than in the simplified partial-head models. We observed that layer 3 pyramidal neurons had comparable stimulation thresholds in both head models, while layer 5 pyramidal neurons showed a notable discrepancy between the models; in particular, layer 5 pyramidal neurons demonstrated asymmetry in the thresholds and action potential initiation sites in the anatomically realistic full-head model. Overall, the anatomically realistic full-head model may offer a better understanding of layer 5 pyramidal neuronal responses. Accordingly, the effects of using the realistic full-head model in SuCS are compelling in computational modeling studies, even though this modeling requires substantially more effort.

Simulation of tidal flow and circulation patterns in the Loxahatchee River Estuary, southeastern Florida

USGS Publications Warehouse

Russell, G.M.; Goodwin, C.R.

1987-01-01

Results of a two-dimensional, vertically averaged, computer simulation model of the Loxahatchee River estuary show that under typical low freshwater inflow and vertically well mixed conditions, water circulation is dominated by freshwater inflow rather than by tidal influence. The model can simulate tidal flow and circulation in the Loxahatchee River estuary under typical low freshwater inflow and vertically well mixed conditions, but is limited, however, to low-flow and well mixed conditions. Computed patterns of residual water transport show a consistent seaward flow from the northwest fork through the central embayment and out Jupiter Inlet to the Atlantic Ocean. A large residual seaward flow was computed from the North Intracoastal Waterway to the inlet channel. Although the tide produces large flood and ebb flows in the estuary, tide-induced residual transport rates are low in comparison with freshwater-induced residual transport. Model investigations of partly mixed or stratified conditions in the estuary need to await development of systems capable of simulating three-dimensional flow patterns. (Author 's abstract)

Dynamic visual attention: motion direction versus motion magnitude

NASA Astrophysics Data System (ADS)

Bur, A.; Wurtz, P.; Müri, R. M.; Hügli, H.

2008-02-01

Defined as an attentive process in the context of visual sequences, dynamic visual attention refers to the selection of the most informative parts of video sequence. This paper investigates the contribution of motion in dynamic visual attention, and specifically compares computer models designed with the motion component expressed either as the speed magnitude or as the speed vector. Several computer models, including static features (color, intensity and orientation) and motion features (magnitude and vector) are considered. Qualitative and quantitative evaluations are performed by comparing the computer model output with human saliency maps obtained experimentally from eye movement recordings. The model suitability is evaluated in various situations (synthetic and real sequences, acquired with fixed and moving camera perspective), showing advantages and inconveniences of each method as well as preferred domain of application.

Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models.

PubMed

Molléro, Roch; Pennec, Xavier; Delingette, Hervé; Garny, Alan; Ayache, Nicholas; Sermesant, Maxime

2018-02-01

Personalised computational models of the heart are of increasing interest for clinical applications due to their discriminative and predictive abilities. However, the simulation of a single heartbeat with a 3D cardiac electromechanical model can be long and computationally expensive, which makes some practical applications, such as the estimation of model parameters from clinical data (the personalisation), very slow. Here we introduce an original multifidelity approach between a 3D cardiac model and a simplified "0D" version of this model, which enables to get reliable (and extremely fast) approximations of the global behaviour of the 3D model using 0D simulations. We then use this multifidelity approximation to speed-up an efficient parameter estimation algorithm, leading to a fast and computationally efficient personalisation method of the 3D model. In particular, we show results on a cohort of 121 different heart geometries and measurements. Finally, an exploitable code of the 0D model with scripts to perform parameter estimation will be released to the community.

Double plasma resonance instability as a source of solar zebra emission

NASA Astrophysics Data System (ADS)

Benáček, J.; Karlický, M.

2018-03-01

Context. The double plasma resonance (DPR) instability plays a basic role in the generation of solar radio zebras. In the plasma, consisting of the loss-cone type distribution of hot electrons and much denser and colder background plasma, this instability generates the upper-hybrid waves, which are then transformed into the electromagnetic waves and observed as radio zebras. Aims: In the present paper we numerically study the double plasma resonance instability from the point of view of the zebra interpretation. Methods: We use a 3-dimensional electromagnetic particle-in-cell (3D PIC) relativistic model. We use this model in two versions: (a) a spatially extended "multi-mode" model and (b) a spatially limited "specific-mode" model. While the multi-mode model is used for detailed computations and verifications of the results obtained by the "specific-mode" model, the specific-mode model is used for computations in a broad range of model parameters, which considerably save computational time. For an analysis of the computational results, we developed software tools in Python. Results: First using the multi-mode model, we study details of the double plasma resonance instability. We show how the distribution function of hot electrons changes during this instability. Then we show that there is a very good agreement between results obtained by the multi-mode and specific-mode models, which is caused by a dominance of the wave with the maximal growth rate. Therefore, for computations in a broad range of model parameters, we use the specific-mode model. We compute the maximal growth rates of the double plasma resonance instability with a dependence on the ratio between the upper-hybrid ωUH and electron-cyclotron ωce frequency. We vary temperatures of both the hot and background plasma components and study their effects on the resulting growth rates. The results are compared with the analytical ones. We find a very good agreement between numerical and analytical growth rates. We also compute saturation energies of the upper-hybrid waves in a very broad range of parameters. We find that the saturation energies of the upper-hybrid waves show maxima and minima at almost the same values of ωUH/ωce as the growth rates, but with a higher contrast between them than the growth rate maxima and minima. The contrast between saturation energy maxima and minima increases when the temperature of hot electrons increases. Furthermore, we find that the saturation energy of the upper-hybrid waves is proportional to the density of hot electrons. The maximum saturated energy can be up to one percent of the kinetic energy of hot electrons. Finally we find that the saturation energy maxima in the interval of ωUH/ωce = 3-18 decrease according to the exponential function. All these findings can be used in the interpretation of solar radio zebras.

Evaluation of two models for predicting elemental accumulation by arthropods

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

Webster, J.R.; Crossley, D.A. Jr.

1978-06-15

Two different models have been proposed for predicting elemental accumulation by arthropods. Parameters of both models can be quantified from radioisotope elimination experiments. Our analysis of the 2 models shows that both predict identical elemental accumulation for a whole organism, though differing in the accumulation in body and gut. We quantified both models with experimental data from /sup 134/Cs and /sup 85/Sr elimination by crickets. Computer simulations of radioisotope accumulation were then compared with actual accumulation experiments. Neither model showed exact fit to the experimental data, though both showed the general pattern of elemental accumulation.

Computational fluid dynamic on the temperature simulation of air preheat effect combustion in propane turbulent flame

NASA Astrophysics Data System (ADS)

Elwina; Yunardi; Bindar, Yazid

2018-04-01

this paper presents results obtained from the application of a computational fluid dynamics (CFD) code Fluent 6.3 to modelling of temperature in propane flames with and without air preheat. The study focuses to investigate the effect of air preheat temperature on the temperature of the flame. A standard k-ε model and Eddy Dissipation model are utilized to represent the flow field and combustion of the flame being investigated, respectively. The results of calculations are compared with experimental data of propane flame taken from literature. The results of the study show that a combination of the standard k-ε turbulence model and eddy dissipation model is capable of producing reasonable predictions of temperature, particularly in axial profile of all three flames. Both experimental works and numerical simulation showed that increasing the temperature of the combustion air significantly increases the flame temperature.

Multi-chain Markov chain Monte Carlo methods for computationally expensive models

NASA Astrophysics Data System (ADS)

Huang, M.; Ray, J.; Ren, H.; Hou, Z.; Bao, J.

2017-12-01

Markov chain Monte Carlo (MCMC) methods are used to infer model parameters from observational data. The parameters are inferred as probability densities, thus capturing estimation error due to sparsity of the data, and the shortcomings of the model. Multiple communicating chains executing the MCMC method have the potential to explore the parameter space better, and conceivably accelerate the convergence to the final distribution. We present results from tests conducted with the multi-chain method to show how the acceleration occurs i.e., for loose convergence tolerances, the multiple chains do not make much of a difference. The ensemble of chains also seems to have the ability to accelerate the convergence of a few chains that might start from suboptimal starting points. Finally, we show the performance of the chains in the estimation of O(10) parameters using computationally expensive forward models such as the Community Land Model, where the sampling burden is distributed over multiple chains.

Design and analysis of a novel latch system implementing fiber-reinforced composite materials

NASA Astrophysics Data System (ADS)

Guevara Arreola, Francisco Javier

The use of fiber-reinforced composite materials have increased in the last four decades in high technology applications due to their exceptional mechanical properties and low weight. In the automotive industry carbon fiber have become popular exclusively in luxury cars because of its high cost. However, Carbon-glass hybrid composites offer an effective alternative to designers to implement fiber-reinforced composites into several conventional applications without a considerable price increase maintaining most of their mechanical properties. A door latch system is a complex mechanism that is under high loading conditions during car accidents such as side impacts and rollovers. Therefore, the Department of Transportation in The United States developed a series of tests that every door latch system comply in order to be installed in a vehicle. The implementation of fiber-reinforced composite materials in a door latch system was studied by analyzing the material behavior during the FMVSS No. 206 transverse test using computational efforts and experimental testing. Firstly, a computational model of the current forkbolt and detent structure was developed. Several efforts were conducted in order to create an effective and time efficient model. Two simplified models were implemented with two different contact interaction approaches. 9 composite materials were studied in forkbolt and 5 in detent including woven carbon fiber, unidirectional carbon fiber, woven carbon-glass fiber hybrid composites and unidirectional carbon-glass fiber hybrid composites. The computational model results showed that woven fiber-reinforced composite materials were stiffer than the unidirectional fiber-reinforced composite materials. For instance, a forkbolt made of woven carbon fibers was 20% stiffer than a forkbolt made of unidirectional fibers symmetrically stacked in 0° and 90° alternating directions. Furthermore, Hybrid composite materials behaved as expected in forkbolt noticing a decline in the load-displacement slopes while the percentage of glass fiber increased. In the other hand, results showed that a detent made of only glass fiber layers was preferable than a carbon-glass fiber hybrid detent due to the high stresses shown in carbon fiber layers. Ultimately, forkbolt and detent were redesigned according to their functionality and test results. It was observed that the new design was stiffer than the original by showing a steeper load-displacement curve. Subsequently, an experimental procedure was performed in order to correlate computational model results. Fiber-reinforced composite forkbolt and detent were waterjet cut from a composite laminate manufactured by Vacuum Assisted Resin Transfer Molding (VART) process. Then, samples were tested according to the computational model. Six testing sample combinations of forkbolt and detent were tested including the top three woven iterations forkbolts from the computational model paired with woven and unidirectional glass fiber detents. Test results showed a stiffness drop of 15% when the carbon fiber percentage decreases from 100% to 75%. Also, it was observed that woven glass fiber detent was superior to the unidirectional glass fiber detent by presenting a forkbolt-detent stiffness 38% higher. Moreover, the new design of forkbolt and detent were tested showing a stiffness increment of 29%. Furthermore, it was observed that fiber-reinforced composite forkbolt and detent did not reach the desired load of 5000 N. However, the redesigned forkbolt made of 100% woven carbon fiber and the redesign detent made of 100% woven glass fiber were close to reach that load. The design review based on test results performed (DRBTR) showed that components did not fail where the computational model concluded to be the areas with the highest maximum principal stress. In contrast to the computational model, all samples failed at the contact area between forkbolt and detent.

Textual emotion recognition for enhancing enterprise computing

NASA Astrophysics Data System (ADS)

Quan, Changqin; Ren, Fuji

2016-05-01

The growing interest in affective computing (AC) brings a lot of valuable research topics that can meet different application demands in enterprise systems. The present study explores a sub area of AC techniques - textual emotion recognition for enhancing enterprise computing. Multi-label emotion recognition in text is able to provide a more comprehensive understanding of emotions than single label emotion recognition. A representation of 'emotion state in text' is proposed to encompass the multidimensional emotions in text. It ensures the description in a formal way of the configurations of basic emotions as well as of the relations between them. Our method allows recognition of the emotions for the words bear indirect emotions, emotion ambiguity and multiple emotions. We further investigate the effect of word order for emotional expression by comparing the performances of bag-of-words model and sequence model for multi-label sentence emotion recognition. The experiments show that the classification results under sequence model are better than under bag-of-words model. And homogeneous Markov model showed promising results of multi-label sentence emotion recognition. This emotion recognition system is able to provide a convenient way to acquire valuable emotion information and to improve enterprise competitive ability in many aspects.

Multi-Strain Deterministic Chaos in Dengue Epidemiology, A Challenge for Computational Mathematics

NASA Astrophysics Data System (ADS)

Aguiar, Maíra; Kooi, Bob W.; Stollenwerk, Nico

2009-09-01

Recently, we have analysed epidemiological models of competing strains of pathogens and hence differences in transmission for first versus secondary infection due to interaction of the strains with previously aquired immunities, as has been described for dengue fever, known as antibody dependent enhancement (ADE). These models show a rich variety of dynamics through bifurcations up to deterministic chaos. Including temporary cross-immunity even enlarges the parameter range of such chaotic attractors, and also gives rise to various coexisting attractors, which are difficult to identify by standard numerical bifurcation programs using continuation methods. A combination of techniques, including classical bifurcation plots and Lyapunov exponent spectra has to be applied in comparison to get further insight into such dynamical structures. Especially, Lyapunov spectra, which quantify the predictability horizon in the epidemiological system, are computationally very demanding. We show ways to speed up computations of such Lyapunov spectra by a factor of more than ten by parallelizing previously used sequential C programs. Such fast computations of Lyapunov spectra will be especially of use in future investigations of seasonally forced versions of the present models, as they are needed for data analysis.

Computer Model Predicts the Movement of Dust

NASA Technical Reports Server (NTRS)

2002-01-01

A new computer model of the atmosphere can now actually pinpoint where global dust events come from, and can project where they're going. The model may help scientists better evaluate the impact of dust on human health, climate, ocean carbon cycles, ecosystems, and atmospheric chemistry. Also, by seeing where dust originates and where it blows people with respiratory problems can get advanced warning of approaching dust clouds. 'The model is physically more realistic than previous ones,' said Mian Chin, a co-author of the study and an Earth and atmospheric scientist at Georgia Tech and the Goddard Space Flight Center (GSFC) in Greenbelt, Md. 'It is able to reproduce the short term day-to-day variations and long term inter-annual variations of dust concentrations and distributions that are measured from field experiments and observed from satellites.' The above images show both aerosols measured from space (left) and the movement of aerosols predicted by computer model for the same date (right). For more information, read New Computer Model Tracks and Predicts Paths Of Earth's Dust Images courtesy Paul Giroux, Georgia Tech/NASA Goddard Space Flight Center

On the use of inexact, pruned hardware in atmospheric modelling

PubMed Central

Düben, Peter D.; Joven, Jaume; Lingamneni, Avinash; McNamara, Hugh; De Micheli, Giovanni; Palem, Krishna V.; Palmer, T. N.

2014-01-01

Inexact hardware design, which advocates trading the accuracy of computations in exchange for significant savings in area, power and/or performance of computing hardware, has received increasing prominence in several error-tolerant application domains, particularly those involving perceptual or statistical end-users. In this paper, we evaluate inexact hardware for its applicability in weather and climate modelling. We expand previous studies on inexact techniques, in particular probabilistic pruning, to floating point arithmetic units and derive several simulated set-ups of pruned hardware with reasonable levels of error for applications in atmospheric modelling. The set-up is tested on the Lorenz ‘96 model, a toy model for atmospheric dynamics, using software emulation for the proposed hardware. The results show that large parts of the computation tolerate the use of pruned hardware blocks without major changes in the quality of short- and long-time diagnostics, such as forecast errors and probability density functions. This could open the door to significant savings in computational cost and to higher resolution simulations with weather and climate models. PMID:24842031

Computational multicore on two-layer 1D shallow water equations for erodible dambreak

NASA Astrophysics Data System (ADS)

Simanjuntak, C. A.; Bagustara, B. A. R. H.; Gunawan, P. H.

2018-03-01

The simulation of erodible dambreak using two-layer shallow water equations and SCHR scheme are elaborated in this paper. The results show that the two-layer SWE model in a good agreement with the data experiment which is performed by Louvain-la-Neuve Université Catholique de Louvain. Moreover, the parallel algorithm with multicore architecture are given in the results. The results show that Computer I with processor Intel(R) Core(TM) i5-2500 CPU Quad-Core has the best performance to accelerate the computational time. Moreover, Computer III with processor AMD A6-5200 APU Quad-Core is observed has higher speedup and efficiency. The speedup and efficiency of Computer III with number of grids 3200 are 3.716050530 times and 92.9% respectively.

3D simulations of early blood vessel formation

NASA Astrophysics Data System (ADS)

Cavalli, F.; Gamba, A.; Naldi, G.; Semplice, M.; Valdembri, D.; Serini, G.

2007-08-01

Blood vessel networks form by spontaneous aggregation of individual cells migrating toward vascularization sites (vasculogenesis). A successful theoretical model of two-dimensional experimental vasculogenesis has been recently proposed, showing the relevance of percolation concepts and of cell cross-talk (chemotactic autocrine loop) to the understanding of this self-aggregation process. Here we study the natural 3D extension of the computational model proposed earlier, which is relevant for the investigation of the genuinely three-dimensional process of vasculogenesis in vertebrate embryos. The computational model is based on a multidimensional Burgers equation coupled with a reaction diffusion equation for a chemotactic factor and a mass conservation law. The numerical approximation of the computational model is obtained by high order relaxed schemes. Space and time discretization are performed by using TVD schemes and, respectively, IMEX schemes. Due to the computational costs of realistic simulations, we have implemented the numerical algorithm on a cluster for parallel computation. Starting from initial conditions mimicking the experimentally observed ones, numerical simulations produce network-like structures qualitatively similar to those observed in the early stages of in vivo vasculogenesis. We develop the computation of critical percolative indices as a robust measure of the network geometry as a first step towards the comparison of computational and experimental data.

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

Tamrin, Mohd Izzuddin Mohd; Turaev, Sherzod; Sembok, Tengku Mohd Tengku

There are tremendous works in biotechnology especially in area of DNA molecules. The computer society is attempting to develop smaller computing devices through computational models which are based on the operations performed on the DNA molecules. A Watson-Crick automaton, a theoretical model for DNA based computation, has two reading heads, and works on double-stranded sequences of the input related by a complementarity relation similar with the Watson-Crick complementarity of DNA nucleotides. Over the time, several variants of Watson-Crick automata have been introduced and investigated. However, they cannot be used as suitable DNA based computational models for molecular stochastic processes andmore » fuzzy processes that are related to important practical problems such as molecular parsing, gene disease detection, and food authentication. In this paper we define new variants of Watson-Crick automata, called weighted Watson-Crick automata, developing theoretical models for molecular stochastic and fuzzy processes. We define weighted Watson-Crick automata adapting weight restriction mechanisms associated with formal grammars and automata. We also study the generative capacities of weighted Watson-Crick automata, including probabilistic and fuzzy variants. We show that weighted variants of Watson-Crick automata increase their generative power.« less

Multi-objective reverse logistics model for integrated computer waste management.

PubMed

Ahluwalia, Poonam Khanijo; Nema, Arvind K

2006-12-01

This study aimed to address the issues involved in the planning and design of a computer waste management system in an integrated manner. A decision-support tool is presented for selecting an optimum configuration of computer waste management facilities (segregation, storage, treatment/processing, reuse/recycle and disposal) and allocation of waste to these facilities. The model is based on an integer linear programming method with the objectives of minimizing environmental risk as well as cost. The issue of uncertainty in the estimated waste quantities from multiple sources is addressed using the Monte Carlo simulation technique. An illustrated example of computer waste management in Delhi, India is presented to demonstrate the usefulness of the proposed model and to study tradeoffs between cost and risk. The results of the example problem show that it is possible to reduce the environmental risk significantly by a marginal increase in the available cost. The proposed model can serve as a powerful tool to address the environmental problems associated with exponentially growing quantities of computer waste which are presently being managed using rudimentary methods of reuse, recovery and disposal by various small-scale vendors.

Weighted Watson-Crick automata

NASA Astrophysics Data System (ADS)

Tamrin, Mohd Izzuddin Mohd; Turaev, Sherzod; Sembok, Tengku Mohd Tengku

2014-07-01

There are tremendous works in biotechnology especially in area of DNA molecules. The computer society is attempting to develop smaller computing devices through computational models which are based on the operations performed on the DNA molecules. A Watson-Crick automaton, a theoretical model for DNA based computation, has two reading heads, and works on double-stranded sequences of the input related by a complementarity relation similar with the Watson-Crick complementarity of DNA nucleotides. Over the time, several variants of Watson-Crick automata have been introduced and investigated. However, they cannot be used as suitable DNA based computational models for molecular stochastic processes and fuzzy processes that are related to important practical problems such as molecular parsing, gene disease detection, and food authentication. In this paper we define new variants of Watson-Crick automata, called weighted Watson-Crick automata, developing theoretical models for molecular stochastic and fuzzy processes. We define weighted Watson-Crick automata adapting weight restriction mechanisms associated with formal grammars and automata. We also study the generative capacities of weighted Watson-Crick automata, including probabilistic and fuzzy variants. We show that weighted variants of Watson-Crick automata increase their generative power.

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

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

Potok, Thomas E; Schuman, Catherine D; Young, Steven R

Current Deep Learning models use highly optimized convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers with a fairly simple layered network topology, i.e., highly connected layers, without intra-layer connections. Complex topologies have been proposed, but are intractable to train on current systems. Building the topologies of the deep learning network requires hand tuning, and implementing the network in hardware is expensive in both cost and power. In this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing (HPC) to automatically determinemore » network topology, and neuromorphic computing for a low-power hardware implementation. Due to input size limitations of current quantum computers we use the MNIST dataset for our evaluation. The results show the possibility of using the three architectures in tandem to explore complex deep learning networks that are untrainable using a von Neumann architecture. We show that a quantum computer can find high quality values of intra-layer connections and weights, while yielding a tractable time result as the complexity of the network increases; a high performance computer can find optimal layer-based topologies; and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware. This represents a new capability that is not feasible with current von Neumann architecture. It potentially enables the ability to solve very complicated problems unsolvable with current computing technologies.« less

Bayesian Modeling for Identification and Estimation of the Learning Effects of Pointing Tasks

NASA Astrophysics Data System (ADS)

Kyo, Koki

Recently, in the field of human-computer interaction, a model containing the systematic factor and human factor has been proposed to evaluate the performance of the input devices of a computer. This is called the SH-model. In this paper, in order to extend the range of application of the SH-model, we propose some new models based on the Box-Cox transformation and apply a Bayesian modeling method for identification and estimation of the learning effects of pointing tasks. We consider the parameters describing the learning effect as random variables and introduce smoothness priors for them. Illustrative results show that the newly-proposed models work well.

Orientation Examples Showing Application of the C.A.M.P.U.S. Simulation Model.

ERIC Educational Resources Information Center

Hansen, B. L.; Barron, J. G.

This pamphlet contains information and examples intended to show how the University of Toronto C.A.M.P.U.S. model operates. C.A.M.P.U.S. (Comprehensive Analytical Method for Planning in the University Sphere) is a computer model which processes projected enrollment statistics and other necessary information in such a way as to yield time-based…

A FAST BAYESIAN METHOD FOR UPDATING AND FORECASTING HOURLY OZONE LEVELS

EPA Science Inventory

A Bayesian hierarchical space-time model is proposed by combining information from real-time ambient AIRNow air monitoring data, and output from a computer simulation model known as the Community Multi-scale Air Quality (Eta-CMAQ) forecast model. A model validation analysis shows...

Identification of Computational and Experimental Reduced-Order Models

NASA Technical Reports Server (NTRS)

Silva, Walter A.; Hong, Moeljo S.; Bartels, Robert E.; Piatak, David J.; Scott, Robert C.

2003-01-01

The identification of computational and experimental reduced-order models (ROMs) for the analysis of unsteady aerodynamic responses and for efficient aeroelastic analyses is presented. For the identification of a computational aeroelastic ROM, the CFL3Dv6.0 computational fluid dynamics (CFD) code is used. Flutter results for the AGARD 445.6 Wing and for a Rigid Semispan Model (RSM) computed using CFL3Dv6.0 are presented, including discussion of associated computational costs. Modal impulse responses of the unsteady aerodynamic system are computed using the CFL3Dv6.0 code and transformed into state-space form. The unsteady aerodynamic state-space ROM is then combined with a state-space model of the structure to create an aeroelastic simulation using the MATLAB/SIMULINK environment. The MATLAB/SIMULINK ROM is then used to rapidly compute aeroelastic transients, including flutter. The ROM shows excellent agreement with the aeroelastic analyses computed using the CFL3Dv6.0 code directly. For the identification of experimental unsteady pressure ROMs, results are presented for two configurations: the RSM and a Benchmark Supercritical Wing (BSCW). Both models were used to acquire unsteady pressure data due to pitching oscillations on the Oscillating Turntable (OTT) system at the Transonic Dynamics Tunnel (TDT). A deconvolution scheme involving a step input in pitch and the resultant step response in pressure, for several pressure transducers, is used to identify the unsteady pressure impulse responses. The identified impulse responses are then used to predict the pressure responses due to pitching oscillations at several frequencies. Comparisons with the experimental data are then presented.

Robust tuning of robot control systems

NASA Technical Reports Server (NTRS)

Minis, I.; Uebel, M.

1992-01-01

The computed torque control problem is examined for a robot arm with flexible, geared, joint drive systems which are typical in many industrial robots. The standard computed torque algorithm is not directly applicable to this class of manipulators because of the dynamics introduced by the joint drive system. The proposed approach to computed torque control combines a computed torque algorithm with torque controller at each joint. Three such control schemes are proposed. The first scheme uses the joint torque control system currently implemented on the robot arm and a novel form of the computed torque algorithm. The other two use the standard computed torque algorithm and a novel model following torque control system based on model following techniques. Standard tasks and performance indices are used to evaluate the performance of the controllers. Both numerical simulations and experiments are used in evaluation. The study shows that all three proposed systems lead to improved tracking performance over a conventional PD controller.

Fast neuromimetic object recognition using FPGA outperforms GPU implementations.

PubMed

Orchard, Garrick; Martin, Jacob G; Vogelstein, R Jacob; Etienne-Cummings, Ralph

2013-08-01

Recognition of objects in still images has traditionally been regarded as a difficult computational problem. Although modern automated methods for visual object recognition have achieved steadily increasing recognition accuracy, even the most advanced computational vision approaches are unable to obtain performance equal to that of humans. This has led to the creation of many biologically inspired models of visual object recognition, among them the hierarchical model and X (HMAX) model. HMAX is traditionally known to achieve high accuracy in visual object recognition tasks at the expense of significant computational complexity. Increasing complexity, in turn, increases computation time, reducing the number of images that can be processed per unit time. In this paper we describe how the computationally intensive and biologically inspired HMAX model for visual object recognition can be modified for implementation on a commercial field-programmable aate Array, specifically the Xilinx Virtex 6 ML605 evaluation board with XC6VLX240T FPGA. We show that with minor modifications to the traditional HMAX model we can perform recognition on images of size 128 × 128 pixels at a rate of 190 images per second with a less than 1% loss in recognition accuracy in both binary and multiclass visual object recognition tasks.

Design of Intelligent Robot as A Tool for Teaching Media Based on Computer Interactive Learning and Computer Assisted Learning to Improve the Skill of University Student

NASA Astrophysics Data System (ADS)

Zuhrie, M. S.; Basuki, I.; Asto B, I. G. P.; Anifah, L.

2018-01-01

The focus of the research is the teaching module which incorporates manufacturing, planning mechanical designing, controlling system through microprocessor technology and maneuverability of the robot. Computer interactive and computer-assisted learning is strategies that emphasize the use of computers and learning aids (computer assisted learning) in teaching and learning activity. This research applied the 4-D model research and development. The model is suggested by Thiagarajan, et.al (1974). 4-D Model consists of four stages: Define Stage, Design Stage, Develop Stage, and Disseminate Stage. This research was conducted by applying the research design development with an objective to produce a tool of learning in the form of intelligent robot modules and kit based on Computer Interactive Learning and Computer Assisted Learning. From the data of the Indonesia Robot Contest during the period of 2009-2015, it can be seen that the modules that have been developed confirm the fourth stage of the research methods of development; disseminate method. The modules which have been developed for students guide students to produce Intelligent Robot Tool for Teaching Based on Computer Interactive Learning and Computer Assisted Learning. Results of students’ responses also showed a positive feedback to relate to the module of robotics and computer-based interactive learning.

Biological modelling of a computational spiking neural network with neuronal avalanches.

PubMed

Li, Xiumin; Chen, Qing; Xue, Fangzheng

2017-06-28

In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed neuronal avalanches. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. However, the critical region for high performance is narrow and sensitive for spiking neural networks (SNNs). In this paper, we investigated the role of the critical state in neural computations based on liquid-state machines, a biologically plausible computational neural network model for real-time computing. The computational performance of an SNN when operating at the critical state and, in particular, with spike-timing-dependent plasticity for updating synaptic weights is investigated. The network is found to show the best computational performance when it is subjected to critical dynamic states. Moreover, the active-neuron-dominant structure refined from synaptic learning can remarkably enhance the robustness of the critical state and further improve computational accuracy. These results may have important implications in the modelling of spiking neural networks with optimal computational performance.This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'. © 2017 The Author(s).

Biological modelling of a computational spiking neural network with neuronal avalanches

NASA Astrophysics Data System (ADS)

Li, Xiumin; Chen, Qing; Xue, Fangzheng

2017-05-01

In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed neuronal avalanches. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. However, the critical region for high performance is narrow and sensitive for spiking neural networks (SNNs). In this paper, we investigated the role of the critical state in neural computations based on liquid-state machines, a biologically plausible computational neural network model for real-time computing. The computational performance of an SNN when operating at the critical state and, in particular, with spike-timing-dependent plasticity for updating synaptic weights is investigated. The network is found to show the best computational performance when it is subjected to critical dynamic states. Moreover, the active-neuron-dominant structure refined from synaptic learning can remarkably enhance the robustness of the critical state and further improve computational accuracy. These results may have important implications in the modelling of spiking neural networks with optimal computational performance. This article is part of the themed issue `Mathematical methods in medicine: neuroscience, cardiology and pathology'.

Computational fluid dynamics analysis of cyclist aerodynamics: performance of different turbulence-modelling and boundary-layer modelling approaches.

PubMed

Defraeye, Thijs; Blocken, Bert; Koninckx, Erwin; Hespel, Peter; Carmeliet, Jan

2010-08-26

This study aims at assessing the accuracy of computational fluid dynamics (CFD) for applications in sports aerodynamics, for example for drag predictions of swimmers, cyclists or skiers, by evaluating the applied numerical modelling techniques by means of detailed validation experiments. In this study, a wind-tunnel experiment on a scale model of a cyclist (scale 1:2) is presented. Apart from three-component forces and moments, also high-resolution surface pressure measurements on the scale model's surface, i.e. at 115 locations, are performed to provide detailed information on the flow field. These data are used to compare the performance of different turbulence-modelling techniques, such as steady Reynolds-averaged Navier-Stokes (RANS), with several k-epsilon and k-omega turbulence models, and unsteady large-eddy simulation (LES), and also boundary-layer modelling techniques, namely wall functions and low-Reynolds number modelling (LRNM). The commercial CFD code Fluent 6.3 is used for the simulations. The RANS shear-stress transport (SST) k-omega model shows the best overall performance, followed by the more computationally expensive LES. Furthermore, LRNM is clearly preferred over wall functions to model the boundary layer. This study showed that there are more accurate alternatives for evaluating flow around bluff bodies with CFD than the standard k-epsilon model combined with wall functions, which is often used in CFD studies in sports. 2010 Elsevier Ltd. All rights reserved.

A Taylor Expansion-Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling

NASA Astrophysics Data System (ADS)

Mo, Shaoxing; Lu, Dan; Shi, Xiaoqing; Zhang, Guannan; Ye, Ming; Wu, Jianfeng; Wu, Jichun

2017-12-01

Global sensitivity analysis (GSA) and uncertainty quantification (UQ) for groundwater modeling are challenging because of the model complexity and significant computational requirements. To reduce the massive computational cost, a cheap-to-evaluate surrogate model is usually constructed to approximate and replace the expensive groundwater models in the GSA and UQ. Constructing an accurate surrogate requires actual model simulations on a number of parameter samples. Thus, a robust experimental design strategy is desired to locate informative samples so as to reduce the computational cost in surrogate construction and consequently to improve the efficiency in the GSA and UQ. In this study, we develop a Taylor expansion-based adaptive design (TEAD) that aims to build an accurate global surrogate model with a small training sample size. TEAD defines a novel hybrid score function to search informative samples, and a robust stopping criterion to terminate the sample search that guarantees the resulted approximation errors satisfy the desired accuracy. The good performance of TEAD in building global surrogate models is demonstrated in seven analytical functions with different dimensionality and complexity in comparison to two widely used experimental design methods. The application of the TEAD-based surrogate method in two groundwater models shows that the TEAD design can effectively improve the computational efficiency of GSA and UQ for groundwater modeling.

The Effect of Nondeterministic Parameters on Shock-Associated Noise Prediction Modeling

NASA Technical Reports Server (NTRS)

Dahl, Milo D.; Khavaran, Abbas

2010-01-01

Engineering applications for aircraft noise prediction contain models for physical phenomenon that enable solutions to be computed quickly. These models contain parameters that have an uncertainty not accounted for in the solution. To include uncertainty in the solution, nondeterministic computational methods are applied. Using prediction models for supersonic jet broadband shock-associated noise, fixed model parameters are replaced by probability distributions to illustrate one of these methods. The results show the impact of using nondeterministic parameters both on estimating the model output uncertainty and on the model spectral level prediction. In addition, a global sensitivity analysis is used to determine the influence of the model parameters on the output, and to identify the parameters with the least influence on model output.

An Online Gravity Modeling Method Applied for High Precision Free-INS

PubMed Central

Wang, Jing; Yang, Gongliu; Li, Jing; Zhou, Xiao

2016-01-01

For real-time solution of inertial navigation system (INS), the high-degree spherical harmonic gravity model (SHM) is not applicable because of its time and space complexity, in which traditional normal gravity model (NGM) has been the dominant technique for gravity compensation. In this paper, a two-dimensional second-order polynomial model is derived from SHM according to the approximate linear characteristic of regional disturbing potential. Firstly, deflections of vertical (DOVs) on dense grids are calculated with SHM in an external computer. And then, the polynomial coefficients are obtained using these DOVs. To achieve global navigation, the coefficients and applicable region of polynomial model are both updated synchronously in above computer. Compared with high-degree SHM, the polynomial model takes less storage and computational time at the expense of minor precision. Meanwhile, the model is more accurate than NGM. Finally, numerical test and INS experiment show that the proposed method outperforms traditional gravity models applied for high precision free-INS. PMID:27669261

An Online Gravity Modeling Method Applied for High Precision Free-INS.

PubMed

Wang, Jing; Yang, Gongliu; Li, Jing; Zhou, Xiao

2016-09-23

For real-time solution of inertial navigation system (INS), the high-degree spherical harmonic gravity model (SHM) is not applicable because of its time and space complexity, in which traditional normal gravity model (NGM) has been the dominant technique for gravity compensation. In this paper, a two-dimensional second-order polynomial model is derived from SHM according to the approximate linear characteristic of regional disturbing potential. Firstly, deflections of vertical (DOVs) on dense grids are calculated with SHM in an external computer. And then, the polynomial coefficients are obtained using these DOVs. To achieve global navigation, the coefficients and applicable region of polynomial model are both updated synchronously in above computer. Compared with high-degree SHM, the polynomial model takes less storage and computational time at the expense of minor precision. Meanwhile, the model is more accurate than NGM. Finally, numerical test and INS experiment show that the proposed method outperforms traditional gravity models applied for high precision free-INS.

A study of modelling simplifications in ground vibration predictions for railway traffic at grade

NASA Astrophysics Data System (ADS)

Germonpré, M.; Degrande, G.; Lombaert, G.

2017-10-01

Accurate computational models are required to predict ground-borne vibration due to railway traffic. Such models generally require a substantial computational effort. Therefore, much research has focused on developing computationally efficient methods, by either exploiting the regularity of the problem geometry in the direction along the track or assuming a simplified track structure. This paper investigates the modelling errors caused by commonly made simplifications of the track geometry. A case study is presented investigating a ballasted track in an excavation. The soil underneath the ballast is stiffened by a lime treatment. First, periodic track models with different cross sections are analyzed, revealing that a prediction of the rail receptance only requires an accurate representation of the soil layering directly underneath the ballast. A much more detailed representation of the cross sectional geometry is required, however, to calculate vibration transfer from track to free field. Second, simplifications in the longitudinal track direction are investigated by comparing 2.5D and periodic track models. This comparison shows that the 2.5D model slightly overestimates the track stiffness, while the transfer functions between track and free field are well predicted. Using a 2.5D model to predict the response during a train passage leads to an overestimation of both train-track interaction forces and free field vibrations. A combined periodic/2.5D approach is therefore proposed in this paper. First, the dynamic axle loads are computed by solving the train-track interaction problem with a periodic model. Next, the vibration transfer to the free field is computed with a 2.5D model. This combined periodic/2.5D approach only introduces small modelling errors compared to an approach in which a periodic model is used in both steps, while significantly reducing the computational cost.

Combining computational models, semantic annotations and simulation experiments in a graph database

PubMed Central

Henkel, Ron; Wolkenhauer, Olaf; Waltemath, Dagmar

2015-01-01

Model repositories such as the BioModels Database, the CellML Model Repository or JWS Online are frequently accessed to retrieve computational models of biological systems. However, their storage concepts support only restricted types of queries and not all data inside the repositories can be retrieved. In this article we present a storage concept that meets this challenge. It grounds on a graph database, reflects the models’ structure, incorporates semantic annotations and simulation descriptions and ultimately connects different types of model-related data. The connections between heterogeneous model-related data and bio-ontologies enable efficient search via biological facts and grant access to new model features. The introduced concept notably improves the access of computational models and associated simulations in a model repository. This has positive effects on tasks such as model search, retrieval, ranking, matching and filtering. Furthermore, our work for the first time enables CellML- and Systems Biology Markup Language-encoded models to be effectively maintained in one database. We show how these models can be linked via annotations and queried. Database URL: https://sems.uni-rostock.de/projects/masymos/ PMID:25754863

Strategies for efficient numerical implementation of hybrid multi-scale agent-based models to describe biological systems

PubMed Central

Cilfone, Nicholas A.; Kirschner, Denise E.; Linderman, Jennifer J.

2015-01-01

Biologically related processes operate across multiple spatiotemporal scales. For computational modeling methodologies to mimic this biological complexity, individual scale models must be linked in ways that allow for dynamic exchange of information across scales. A powerful methodology is to combine a discrete modeling approach, agent-based models (ABMs), with continuum models to form hybrid models. Hybrid multi-scale ABMs have been used to simulate emergent responses of biological systems. Here, we review two aspects of hybrid multi-scale ABMs: linking individual scale models and efficiently solving the resulting model. We discuss the computational choices associated with aspects of linking individual scale models while simultaneously maintaining model tractability. We demonstrate implementations of existing numerical methods in the context of hybrid multi-scale ABMs. Using an example model describing Mycobacterium tuberculosis infection, we show relative computational speeds of various combinations of numerical methods. Efficient linking and solution of hybrid multi-scale ABMs is key to model portability, modularity, and their use in understanding biological phenomena at a systems level. PMID:26366228

Computation of load performance and other parameters of extra high speed modified Lundell alternators from 3D-FE magnetic field solutions

NASA Technical Reports Server (NTRS)

Wang, R.; Demerdash, N. A.

1992-01-01

The combined magnetic vector potential - magnetic scalar potential method of computation of 3D magnetic fields by finite elements, introduced in a companion paper, in combination with state modeling in the abc-frame of reference, are used for global 3D magnetic field analysis and machine performance computation under rated load and overload condition in an example 14.3 kVA modified Lundell alternator. The results vividly demonstrate the 3D nature of the magnetic field in such machines, and show how this model can be used as an excellent tool for computation of flux density distributions, armature current and voltage waveform profiles and harmonic contents, as well as computation of torque profiles and ripples. Use of the model in gaining insight into locations of regions in the magnetic circuit with heavy degrees of saturation is demonstrated. Experimental results which correlate well with the simulations of the load case are given.

Multiscale Modeling of UHTC: Thermal Conductivity

NASA Technical Reports Server (NTRS)

Lawson, John W.; Murry, Daw; Squire, Thomas; Bauschlicher, Charles W.

2012-01-01

We are developing a multiscale framework in computational modeling for the ultra high temperature ceramics (UHTC) ZrB2 and HfB2. These materials are characterized by high melting point, good strength, and reasonable oxidation resistance. They are candidate materials for a number of applications in extreme environments including sharp leading edges of hypersonic aircraft. In particular, we used a combination of ab initio methods, atomistic simulations and continuum computations to obtain insights into fundamental properties of these materials. Ab initio methods were used to compute basic structural, mechanical and thermal properties. From these results, a database was constructed to fit a Tersoff style interatomic potential suitable for atomistic simulations. These potentials were used to evaluate the lattice thermal conductivity of single crystals and the thermal resistance of simple grain boundaries. Finite element method (FEM) computations using atomistic results as inputs were performed with meshes constructed on SEM images thereby modeling the realistic microstructure. These continuum computations showed the reduction in thermal conductivity due to the grain boundary network.

Web-Based Real Time Earthquake Forecasting and Personal Risk Management

NASA Astrophysics Data System (ADS)

Rundle, J. B.; Holliday, J. R.; Graves, W. R.; Turcotte, D. L.; Donnellan, A.

2012-12-01

Earthquake forecasts have been computed by a variety of countries and economies world-wide for over two decades. For the most part, forecasts have been computed for insurance, reinsurance and underwriters of catastrophe bonds. One example is the Working Group on California Earthquake Probabilities that has been responsible for the official California earthquake forecast since 1988. However, in a time of increasingly severe global financial constraints, we are now moving inexorably towards personal risk management, wherein mitigating risk is becoming the responsibility of individual members of the public. Under these circumstances, open access to a variety of web-based tools, utilities and information is a necessity. Here we describe a web-based system that has been operational since 2009 at www.openhazards.com and www.quakesim.org. Models for earthquake physics and forecasting require input data, along with model parameters. The models we consider are the Natural Time Weibull (NTW) model for regional earthquake forecasting, together with models for activation and quiescence. These models use small earthquakes ('seismicity-based models") to forecast the occurrence of large earthquakes, either through varying rates of small earthquake activity, or via an accumulation of this activity over time. These approaches use data-mining algorithms combined with the ANSS earthquake catalog. The basic idea is to compute large earthquake probabilities using the number of small earthquakes that have occurred in a region since the last large earthquake. Each of these approaches has computational challenges associated with computing forecast information in real time. Using 25 years of data from the ANSS California-Nevada catalog of earthquakes, we show that real-time forecasting is possible at a grid scale of 0.1o. We have analyzed the performance of these models using Reliability/Attributes and standard Receiver Operating Characteristic (ROC) tests. We show how the Reliability and ROC tests allow us to judge data completeness and estimate error. It is clear from much of the analysis that data quality is a major limitation on the accurate computation of earthquake probabilities. We discuss the challenges and pitfalls in serving up these datasets over the web.

Assessment of traffic noise levels in urban areas using different soft computing techniques.

PubMed

Tomić, J; Bogojević, N; Pljakić, M; Šumarac-Pavlović, D

2016-10-01

Available traffic noise prediction models are usually based on regression analysis of experimental data, and this paper presents the application of soft computing techniques in traffic noise prediction. Two mathematical models are proposed and their predictions are compared to data collected by traffic noise monitoring in urban areas, as well as to predictions of commonly used traffic noise models. The results show that application of evolutionary algorithms and neural networks may improve process of development, as well as accuracy of traffic noise prediction.

Physical analysis and second-order modelling of an unsteady turbulent flow - The oscillating boundary layer on a flat plate

NASA Technical Reports Server (NTRS)

Ha Minh, H.; Viegas, J. R.; Rubesin, M. W.; Spalart, P.; Vandromme, D. D.

1989-01-01

The turbulent boundary layer under a freestream whose velocity varies sinusoidally in time around a zero mean is computed using two second order turbulence closure models. The time or phase dependent behavior of the Reynolds stresses are analyzed and results are compared to those of a previous SPALART-BALDWIN direct simulation. Comparisons show that the second order modeling is quite satisfactory for almost all phase angles, except in the relaminarization period where the computations lead to a relatively high wall shear stress.

Self-Shadowing of a Spacecraft in the Computation of Surface Forces. An Example in Planetary Geodesy

NASA Astrophysics Data System (ADS)

Balmino, G.; Marty, J. C.

2018-03-01

We describe in details the algorithms used in modelling the self-shadowing between spacecraft components, which appears when computing the surface forces as precisely as possible and especially when moving parts are involved. This becomes necessary in planetary geodesy inverse problems using more and more precise orbital information to derive fundamental parameters of geophysical interest. Examples are given with two Mars orbiters, which show significant improvement on drag and solar radiation pressure model multiplying factors, a prerequisite for improving in turn the determination of other global models.

Connected word recognition using a cascaded neuro-computational model

NASA Astrophysics Data System (ADS)

Hoya, Tetsuya; van Leeuwen, Cees

2016-10-01

We propose a novel framework for processing a continuous speech stream that contains a varying number of words, as well as non-speech periods. Speech samples are segmented into word-tokens and non-speech periods. An augmented version of an earlier-proposed, cascaded neuro-computational model is used for recognising individual words within the stream. Simulation studies using both a multi-speaker-dependent and speaker-independent digit string database show that the proposed method yields a recognition performance comparable to that obtained by a benchmark approach using hidden Markov models with embedded training.

On the Stefan Problem with Volumetric Energy Generation

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

John Crepeau; Ali Siahpush; Blaine Spotten

2009-11-01

This paper presents results of solid-liquid phase change, driven by volumetric energy generation, in a vertical cylinder. We show excellent agreement between a quasi-static, approximate analytical solution valid for Stefan numbers less than one, and a computational model solved using the CFD code FLUENT®. A computational study also shows the effect that the volumetric energy generation has on both the mushy zone thickness and convection in the melt during phase change.

Adapting to life: ocean biogeochemical modelling and adaptive remeshing

NASA Astrophysics Data System (ADS)

Hill, J.; Popova, E. E.; Ham, D. A.; Piggott, M. D.; Srokosz, M.

2014-05-01

An outstanding problem in biogeochemical modelling of the ocean is that many of the key processes occur intermittently at small scales, such as the sub-mesoscale, that are not well represented in global ocean models. This is partly due to their failure to resolve sub-mesoscale phenomena, which play a significant role in vertical nutrient supply. Simply increasing the resolution of the models may be an inefficient computational solution to this problem. An approach based on recent advances in adaptive mesh computational techniques may offer an alternative. Here the first steps in such an approach are described, using the example of a simple vertical column (quasi-1-D) ocean biogeochemical model. We present a novel method of simulating ocean biogeochemical behaviour on a vertically adaptive computational mesh, where the mesh changes in response to the biogeochemical and physical state of the system throughout the simulation. We show that the model reproduces the general physical and biological behaviour at three ocean stations (India, Papa and Bermuda) as compared to a high-resolution fixed mesh simulation and to observations. The use of an adaptive mesh does not increase the computational error, but reduces the number of mesh elements by a factor of 2-3. Unlike previous work the adaptivity metric used is flexible and we show that capturing the physical behaviour of the model is paramount to achieving a reasonable solution. Adding biological quantities to the adaptivity metric further refines the solution. We then show the potential of this method in two case studies where we change the adaptivity metric used to determine the varying mesh sizes in order to capture the dynamics of chlorophyll at Bermuda and sinking detritus at Papa. We therefore demonstrate that adaptive meshes may provide a suitable numerical technique for simulating seasonal or transient biogeochemical behaviour at high vertical resolution whilst minimising the number of elements in the mesh. More work is required to move this to fully 3-D simulations.

Multi-GPU hybrid programming accelerated three-dimensional phase-field model in binary alloy

NASA Astrophysics Data System (ADS)

Zhu, Changsheng; Liu, Jieqiong; Zhu, Mingfang; Feng, Li

2018-03-01

In the process of dendritic growth simulation, the computational efficiency and the problem scales have extremely important influence on simulation efficiency of three-dimensional phase-field model. Thus, seeking for high performance calculation method to improve the computational efficiency and to expand the problem scales has a great significance to the research of microstructure of the material. A high performance calculation method based on MPI+CUDA hybrid programming model is introduced. Multi-GPU is used to implement quantitative numerical simulations of three-dimensional phase-field model in binary alloy under the condition of multi-physical processes coupling. The acceleration effect of different GPU nodes on different calculation scales is explored. On the foundation of multi-GPU calculation model that has been introduced, two optimization schemes, Non-blocking communication optimization and overlap of MPI and GPU computing optimization, are proposed. The results of two optimization schemes and basic multi-GPU model are compared. The calculation results show that the use of multi-GPU calculation model can improve the computational efficiency of three-dimensional phase-field obviously, which is 13 times to single GPU, and the problem scales have been expanded to 8193. The feasibility of two optimization schemes is shown, and the overlap of MPI and GPU computing optimization has better performance, which is 1.7 times to basic multi-GPU model, when 21 GPUs are used.

Computer Language For Optimization Of Design

NASA Technical Reports Server (NTRS)

Scotti, Stephen J.; Lucas, Stephen H.

1991-01-01

SOL is computer language geared to solution of design problems. Includes mathematical modeling and logical capabilities of computer language like FORTRAN; also includes additional power of nonlinear mathematical programming methods at language level. SOL compiler takes SOL-language statements and generates equivalent FORTRAN code and system calls. Provides syntactic and semantic checking for recovery from errors and provides detailed reports containing cross-references to show where each variable used. Implemented on VAX/VMS computer systems. Requires VAX FORTRAN compiler to produce executable program.

[Birth and death process of computer viruses].

PubMed

Segawa, Katsunori; Nakano, Tatsuya; Nakata, Kotoko; Hayashi, Yuzuru

2006-01-01

The daily variations in the number of computer viruses found attaching to e-mails and the number of accesses to the home page of a national institute in Japan are examined. The power spectral densities (PSD) of the variation in the computer viruses show a time-correlation characteristic of Markov process, but the daily access number does not (identified as white noise). Like biological viruses, the variation in the computer viruses can be described by the birth-and-death model known as a Markov process.

Dilatation-dissipation corrections for advanced turbulence models

NASA Technical Reports Server (NTRS)

Wilcox, David C.

1992-01-01

This paper analyzes dilatation-dissipation based compressibility corrections for advanced turbulence models. Numerical computations verify that the dilatation-dissipation corrections devised by Sarkar and Zeman greatly improve both the k-omega and k-epsilon model predicted effect of Mach number on spreading rate. However, computations with the k-gamma model also show that the Sarkar/Zeman terms cause an undesired reduction in skin friction for the compressible flat-plate boundary layer. A perturbation solution for the compressible wall layer shows that the Sarkar and Zeman terms reduce the effective von Karman constant in the law of the wall. This is the source of the inaccurate k-gamma model skin-friction predictions for the flat-plate boundary layer. The perturbation solution also shows that the k-epsilon model has an inherent flaw for compressible boundary layers that is not compensated for by the dilatation-dissipation corrections. A compressibility modification for k-gamma and k-epsilon models is proposed that is similar to those of Sarkar and Zeman. The new compressibility term permits accurate predictions for the compressible mixing layer, flat-plate boundary layer, and a shock separated flow with the same values for all closure coefficients.

Mathematical Description of Complex Chemical Kinetics and Application to CFD Modeling Codes

NASA Technical Reports Server (NTRS)

Bittker, D. A.

1993-01-01

A major effort in combustion research at the present time is devoted to the theoretical modeling of practical combustion systems. These include turbojet and ramjet air-breathing engines as well as ground-based gas-turbine power generating systems. The ability to use computational modeling extensively in designing these products not only saves time and money, but also helps designers meet the quite rigorous environmental standards that have been imposed on all combustion devices. The goal is to combine the very complex solution of the Navier-Stokes flow equations with realistic turbulence and heat-release models into a single computer code. Such a computational fluid-dynamic (CFD) code simulates the coupling of fluid mechanics with the chemistry of combustion to describe the practical devices. This paper will focus on the task of developing a simplified chemical model which can predict realistic heat-release rates as well as species composition profiles, and is also computationally rapid. We first discuss the mathematical techniques used to describe a complex, multistep fuel oxidation chemical reaction and develop a detailed mechanism for the process. We then show how this mechanism may be reduced and simplified to give an approximate model which adequately predicts heat release rates and a limited number of species composition profiles, but is computationally much faster than the original one. Only such a model can be incorporated into a CFD code without adding significantly to long computation times. Finally, we present some of the recent advances in the development of these simplified chemical mechanisms.

Mathematical description of complex chemical kinetics and application to CFD modeling codes

NASA Technical Reports Server (NTRS)

Bittker, D. A.

1993-01-01

A major effort in combustion research at the present time is devoted to the theoretical modeling of practical combustion systems. These include turbojet and ramjet air-breathing engines as well as ground-based gas-turbine power generating systems. The ability to use computational modeling extensively in designing these products not only saves time and money, but also helps designers meet the quite rigorous environmental standards that have been imposed on all combustion devices. The goal is to combine the very complex solution of the Navier-Stokes flow equations with realistic turbulence and heat-release models into a single computer code. Such a computational fluid-dynamic (CFD) code simulates the coupling of fluid mechanics with the chemistry of combustion to describe the practical devices. This paper will focus on the task of developing a simplified chemical model which can predict realistic heat-release rates as well as species composition profiles, and is also computationally rapid. We first discuss the mathematical techniques used to describe a complex, multistep fuel oxidation chemical reaction and develop a detailed mechanism for the process. We then show how this mechanism may be reduced and simplified to give an approximate model which adequately predicts heat release rates and a limited number of species composition profiles, but is computationally much faster than the original one. Only such a model can be incorporated into a CFD code without adding significantly to long computation times. Finally, we present some of the recent advances in the development of these simplified chemical mechanisms.

Computed 3D visualisation of an extinct cephalopod using computer tomographs.

PubMed

Lukeneder, Alexander

2012-08-01

The first 3D visualisation of a heteromorph cephalopod species from the Southern Alps (Dolomites, northern Italy) is presented. Computed tomography, palaeontological data and 3D reconstructions were included in the production of a movie, which shows a life reconstruction of the extinct organism. This detailed reconstruction is according to the current knowledge of the shape and mode of life as well as habitat of this animal. The results are based on the most complete shell known thus far of the genus Dissimilites . Object-based combined analyses from computed tomography and various computed 3D facility programmes help to understand morphological details as well as their ontogentical changes in fossil material. In this study, an additional goal was to show changes in locomotion during different ontogenetic phases of such fossil, marine shell-bearing animals (ammonoids). Hence, the presented models and tools can serve as starting points for discussions on morphology and locomotion of extinct cephalopods in general, and of the genus Dissimilites in particular. The heteromorph ammonoid genus Dissimilites is interpreted here as an active swimmer of the Tethyan Ocean. This study portrays non-destructive methods of 3D visualisation applied on palaeontological material, starting with computed tomography resulting in animated, high-quality video clips. The here presented 3D geometrical models and animation, which are based on palaeontological material, demonstrate the wide range of applications, analytical techniques and also outline possible limitations of 3D models in earth sciences and palaeontology. The realistic 3D models and motion pictures can easily be shared amongst palaeontologists. Data, images and short clips can be discussed online and, if necessary, adapted in morphological details and motion-style to better represent the cephalopod animal.

Computed 3D visualisation of an extinct cephalopod using computer tomographs

NASA Astrophysics Data System (ADS)

Lukeneder, Alexander

2012-08-01

The first 3D visualisation of a heteromorph cephalopod species from the Southern Alps (Dolomites, northern Italy) is presented. Computed tomography, palaeontological data and 3D reconstructions were included in the production of a movie, which shows a life reconstruction of the extinct organism. This detailed reconstruction is according to the current knowledge of the shape and mode of life as well as habitat of this animal. The results are based on the most complete shell known thus far of the genus Dissimilites. Object-based combined analyses from computed tomography and various computed 3D facility programmes help to understand morphological details as well as their ontogentical changes in fossil material. In this study, an additional goal was to show changes in locomotion during different ontogenetic phases of such fossil, marine shell-bearing animals (ammonoids). Hence, the presented models and tools can serve as starting points for discussions on morphology and locomotion of extinct cephalopods in general, and of the genus Dissimilites in particular. The heteromorph ammonoid genus Dissimilites is interpreted here as an active swimmer of the Tethyan Ocean. This study portrays non-destructive methods of 3D visualisation applied on palaeontological material, starting with computed tomography resulting in animated, high-quality video clips. The here presented 3D geometrical models and animation, which are based on palaeontological material, demonstrate the wide range of applications, analytical techniques and also outline possible limitations of 3D models in earth sciences and palaeontology. The realistic 3D models and motion pictures can easily be shared amongst palaeontologists. Data, images and short clips can be discussed online and, if necessary, adapted in morphological details and motion-style to better represent the cephalopod animal.

Computed 3D visualisation of an extinct cephalopod using computer tomographs

PubMed Central

Lukeneder, Alexander

2012-01-01

The first 3D visualisation of a heteromorph cephalopod species from the Southern Alps (Dolomites, northern Italy) is presented. Computed tomography, palaeontological data and 3D reconstructions were included in the production of a movie, which shows a life reconstruction of the extinct organism. This detailed reconstruction is according to the current knowledge of the shape and mode of life as well as habitat of this animal. The results are based on the most complete shell known thus far of the genus Dissimilites. Object-based combined analyses from computed tomography and various computed 3D facility programmes help to understand morphological details as well as their ontogentical changes in fossil material. In this study, an additional goal was to show changes in locomotion during different ontogenetic phases of such fossil, marine shell-bearing animals (ammonoids). Hence, the presented models and tools can serve as starting points for discussions on morphology and locomotion of extinct cephalopods in general, and of the genus Dissimilites in particular. The heteromorph ammonoid genus Dissimilites is interpreted here as an active swimmer of the Tethyan Ocean. This study portrays non-destructive methods of 3D visualisation applied on palaeontological material, starting with computed tomography resulting in animated, high-quality video clips. The here presented 3D geometrical models and animation, which are based on palaeontological material, demonstrate the wide range of applications, analytical techniques and also outline possible limitations of 3D models in earth sciences and palaeontology. The realistic 3D models and motion pictures can easily be shared amongst palaeontologists. Data, images and short clips can be discussed online and, if necessary, adapted in morphological details and motion-style to better represent the cephalopod animal. PMID:24850976

GRAVTool, Advances on the Package to Compute Geoid Model path by the Remove-Compute-Restore Technique, Following Helmert's Condensation Method

NASA Astrophysics Data System (ADS)

Marotta, G. S.

2017-12-01

Currently, there are several methods to determine geoid models. They can be based on terrestrial gravity data, geopotential coefficients, astrogeodetic data or a combination of them. Among the techniques to compute a precise geoid model, the Remove Compute Restore (RCR) has been widely applied. It considers short, medium and long wavelengths derived from altitude data provided by Digital Terrain Models (DTM), terrestrial gravity data and Global Geopotential Model (GGM), respectively. In order to apply this technique, it is necessary to create procedures that compute gravity anomalies and geoid models, by the integration of different wavelengths, and adjust these models to one local vertical datum. This research presents the advances on the package called GRAVTool to compute geoid models path by the RCR, following Helmert's condensation method, and its application in a study area. The studied area comprehends the federal district of Brazil, with 6000 km², wavy relief, heights varying from 600 m to 1340 m, located between the coordinates 48.25ºW, 15.45ºS and 47.33ºW, 16.06ºS. The results of the numerical example on the studied area show a geoid model computed by the GRAVTool package, after analysis of the density, DTM and GGM values, more adequate to the reference values used on the study area. The accuracy of the computed model (σ = ± 0.058 m, RMS = 0.067 m, maximum = 0.124 m and minimum = -0.155 m), using density value of 2.702 g/cm³ ±0.024 g/cm³, DTM SRTM Void Filled 3 arc-second and GGM EIGEN-6C4 up to degree and order 250, matches the uncertainty (σ =± 0.073) of 26 points randomly spaced where the geoid was computed by geometrical leveling technique supported by positioning GNSS. The results were also better than those achieved by Brazilian official regional geoid model (σ = ± 0.076 m, RMS = 0.098 m, maximum = 0.320 m and minimum = -0.061 m).

Symplectic multi-particle tracking on GPUs

NASA Astrophysics Data System (ADS)

Liu, Zhicong; Qiang, Ji

2018-05-01

A symplectic multi-particle tracking model is implemented on the Graphic Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) language. The symplectic tracking model can preserve phase space structure and reduce non-physical effects in long term simulation, which is important for beam property evaluation in particle accelerators. Though this model is computationally expensive, it is very suitable for parallelization and can be accelerated significantly by using GPUs. In this paper, we optimized the implementation of the symplectic tracking model on both single GPU and multiple GPUs. Using a single GPU processor, the code achieves a factor of 2-10 speedup for a range of problem sizes compared with the time on a single state-of-the-art Central Processing Unit (CPU) node with similar power consumption and semiconductor technology. It also shows good scalability on a multi-GPU cluster at Oak Ridge Leadership Computing Facility. In an application to beam dynamics simulation, the GPU implementation helps save more than a factor of two total computing time in comparison to the CPU implementation.

Exploring Asynchronous Many-Task Runtime Systems toward Extreme Scales

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

Knight, Samuel; Baker, Gavin Matthew; Gamell, Marc

2015-10-01

Major exascale computing reports indicate a number of software challenges to meet the dramatic change of system architectures in near future. While several-orders-of-magnitude increase in parallelism is the most commonly cited of those, hurdles also include performance heterogeneity of compute nodes across the system, increased imbalance between computational capacity and I/O capabilities, frequent system interrupts, and complex hardware architectures. Asynchronous task-parallel programming models show a great promise in addressing these issues, but are not yet fully understood nor developed su ciently for computational science and engineering application codes. We address these knowledge gaps through quantitative and qualitative exploration of leadingmore » candidate solutions in the context of engineering applications at Sandia. In this poster, we evaluate MiniAero code ported to three leading candidate programming models (Charm++, Legion and UINTAH) to examine the feasibility of these models that permits insertion of new programming model elements into an existing code base.« less

Towards a Universal Calving Law: Modeling Ice Shelves Using Damage Mechanics

NASA Astrophysics Data System (ADS)

Whitcomb, M.; Bassis, J. N.; Price, S. F.; Lipscomb, W. H.

2017-12-01

Modeling iceberg calving from ice shelves and ice tongues is a particularly difficult problem in glaciology because of the wide range of observed calving rates. Ice shelves naturally calve large tabular icebergs at infrequent intervals, but may instead calve smaller bergs regularly or disintegrate due to hydrofracturing in warmer conditions. Any complete theory of iceberg calving in ice shelves must be able to generate realistic calving rate values depending on the magnitudes of the external forcings. Here we show that a simple damage evolution law, which represents crevasse distributions as a continuum field, produces reasonable estimates of ice shelf calving rates when added to the Community Ice Sheet Model (CISM). Our damage formulation is based on a linear stability analysis and depends upon the bulk stress and strain rate in the ice shelf, as well as the surface and basal melt rates. The basal melt parameter in our model enhances crevasse growth near the ice shelf terminus, leading to an increased iceberg production rate. This implies that increasing ocean temperatures underneath ice shelves will drive ice shelf retreat, as has been observed in the Amundsen and Bellingshausen Seas. We show that our model predicts broadly correct calving rates for ice tongues ranging in length from 10 km (Erebus) to over 100 km (Drygalski), by matching the computed steady state lengths to observations. In addition, we apply the model to idealized Antarctic ice shelves and show that we can also predict realistic ice shelf extents. Our damage mechanics model provides a promising, computationally efficient way to compute calving fluxes and links ice shelf stability to climate forcing.

Development of a Personalized Educational Computer Game Based on Students' Learning Styles

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Sung, Han-Yu; Hung, Chun-Ming; Huang, Iwen; Tsai, Chin-Chung

2012-01-01

In recent years, many researchers have been engaged in the development of educational computer games; however, previous studies have indicated that, without supportive models that take individual students' learning needs or difficulties into consideration, students might only show temporary interest during the learning process, and their learning…

Iterative Refinement of a Binding Pocket Model: Active Computational Steering of Lead Optimization

PubMed Central

2012-01-01

Computational approaches for binding affinity prediction are most frequently demonstrated through cross-validation within a series of molecules or through performance shown on a blinded test set. Here, we show how such a system performs in an iterative, temporal lead optimization exercise. A series of gyrase inhibitors with known synthetic order formed the set of molecules that could be selected for “synthesis.” Beginning with a small number of molecules, based only on structures and activities, a model was constructed. Compound selection was done computationally, each time making five selections based on confident predictions of high activity and five selections based on a quantitative measure of three-dimensional structural novelty. Compound selection was followed by model refinement using the new data. Iterative computational candidate selection produced rapid improvements in selected compound activity, and incorporation of explicitly novel compounds uncovered much more diverse active inhibitors than strategies lacking active novelty selection. PMID:23046104

Noise Estimation in Electroencephalogram Signal by Using Volterra Series Coefficients

PubMed Central

Hassani, Malihe; Karami, Mohammad Reza

2015-01-01

The Volterra model is widely used for nonlinearity identification in practical applications. In this paper, we employed Volterra model to find the nonlinearity relation between electroencephalogram (EEG) signal and the noise that is a novel approach to estimate noise in EEG signal. We show that by employing this method. We can considerably improve the signal to noise ratio by the ratio of at least 1.54. An important issue in implementing Volterra model is its computation complexity, especially when the degree of nonlinearity is increased. Hence, in many applications it is urgent to reduce the complexity of computation. In this paper, we use the property of EEG signal and propose a new and good approximation of delayed input signal to its adjacent samples in order to reduce the computation of finding Volterra series coefficients. The computation complexity is reduced by the ratio of at least 1/3 when the filter memory is 3. PMID:26284176

Understanding Islamist political violence through computational social simulation

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

Watkins, Jennifer H; Mackerrow, Edward P; Patelli, Paolo G

Understanding the process that enables political violence is of great value in reducing the future demand for and support of violent opposition groups. Methods are needed that allow alternative scenarios and counterfactuals to be scientifically researched. Computational social simulation shows promise in developing 'computer experiments' that would be unfeasible or unethical in the real world. Additionally, the process of modeling and simulation reveals and challenges assumptions that may not be noted in theories, exposes areas where data is not available, and provides a rigorous, repeatable, and transparent framework for analyzing the complex dynamics of political violence. This paper demonstrates themore » computational modeling process using two simulation techniques: system dynamics and agent-based modeling. The benefits and drawbacks of both techniques are discussed. In developing these social simulations, we discovered that the social science concepts and theories needed to accurately simulate the associated psychological and social phenomena were lacking.« less

Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity

PubMed Central

Nessler, Bernhard; Pfeiffer, Michael; Buesing, Lars; Maass, Wolfgang

2013-01-01

The principles by which networks of neurons compute, and how spike-timing dependent plasticity (STDP) of synaptic weights generates and maintains their computational function, are unknown. Preceding work has shown that soft winner-take-all (WTA) circuits, where pyramidal neurons inhibit each other via interneurons, are a common motif of cortical microcircuits. We show through theoretical analysis and computer simulations that Bayesian computation is induced in these network motifs through STDP in combination with activity-dependent changes in the excitability of neurons. The fundamental components of this emergent Bayesian computation are priors that result from adaptation of neuronal excitability and implicit generative models for hidden causes that are created in the synaptic weights through STDP. In fact, a surprising result is that STDP is able to approximate a powerful principle for fitting such implicit generative models to high-dimensional spike inputs: Expectation Maximization. Our results suggest that the experimentally observed spontaneous activity and trial-to-trial variability of cortical neurons are essential features of their information processing capability, since their functional role is to represent probability distributions rather than static neural codes. Furthermore it suggests networks of Bayesian computation modules as a new model for distributed information processing in the cortex. PMID:23633941

Model-Averaged ℓ1 Regularization using Markov Chain Monte Carlo Model Composition

PubMed Central

Fraley, Chris; Percival, Daniel

2014-01-01

Bayesian Model Averaging (BMA) is an effective technique for addressing model uncertainty in variable selection problems. However, current BMA approaches have computational difficulty dealing with data in which there are many more measurements (variables) than samples. This paper presents a method for combining ℓ1 regularization and Markov chain Monte Carlo model composition techniques for BMA. By treating the ℓ1 regularization path as a model space, we propose a method to resolve the model uncertainty issues arising in model averaging from solution path point selection. We show that this method is computationally and empirically effective for regression and classification in high-dimensional datasets. We apply our technique in simulations, as well as to some applications that arise in genomics. PMID:25642001

Performance of hybrid programming models for multiscale cardiac simulations: preparing for petascale computation.

PubMed

Pope, Bernard J; Fitch, Blake G; Pitman, Michael C; Rice, John J; Reumann, Matthias

2011-10-01

Future multiscale and multiphysics models that support research into human disease, translational medical science, and treatment can utilize the power of high-performance computing (HPC) systems. We anticipate that computationally efficient multiscale models will require the use of sophisticated hybrid programming models, mixing distributed message-passing processes [e.g., the message-passing interface (MPI)] with multithreading (e.g., OpenMP, Pthreads). The objective of this study is to compare the performance of such hybrid programming models when applied to the simulation of a realistic physiological multiscale model of the heart. Our results show that the hybrid models perform favorably when compared to an implementation using only the MPI and, furthermore, that OpenMP in combination with the MPI provides a satisfactory compromise between performance and code complexity. Having the ability to use threads within MPI processes enables the sophisticated use of all processor cores for both computation and communication phases. Considering that HPC systems in 2012 will have two orders of magnitude more cores than what was used in this study, we believe that faster than real-time multiscale cardiac simulations can be achieved on these systems.

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

NASA Technical Reports Server (NTRS)

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

1989-01-01

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

Investigation of Climate Change Impact on Water Resources for an Alpine Basin in Northern Italy: Implications for Evapotranspiration Modeling Complexity

PubMed Central

Ravazzani, Giovanni; Ghilardi, Matteo; Mendlik, Thomas; Gobiet, Andreas; Corbari, Chiara; Mancini, Marco

2014-01-01

Assessing the future effects of climate change on water availability requires an understanding of how precipitation and evapotranspiration rates will respond to changes in atmospheric forcing. Use of simplified hydrological models is required beacause of lack of meteorological forcings with the high space and time resolutions required to model hydrological processes in mountains river basins, and the necessity of reducing the computational costs. The main objective of this study was to quantify the differences between a simplified hydrological model, which uses only precipitation and temperature to compute the hydrological balance when simulating the impact of climate change, and an enhanced version of the model, which solves the energy balance to compute the actual evapotranspiration. For the meteorological forcing of future scenario, at-site bias-corrected time series based on two regional climate models were used. A quantile-based error-correction approach was used to downscale the regional climate model simulations to a point scale and to reduce its error characteristics. The study shows that a simple temperature-based approach for computing the evapotranspiration is sufficiently accurate for performing hydrological impact investigations of climate change for the Alpine river basin which was studied. PMID:25285917

Investigation of climate change impact on water resources for an Alpine basin in northern Italy: implications for evapotranspiration modeling complexity.

PubMed

Ravazzani, Giovanni; Ghilardi, Matteo; Mendlik, Thomas; Gobiet, Andreas; Corbari, Chiara; Mancini, Marco

2014-01-01

Assessing the future effects of climate change on water availability requires an understanding of how precipitation and evapotranspiration rates will respond to changes in atmospheric forcing. Use of simplified hydrological models is required because of lack of meteorological forcings with the high space and time resolutions required to model hydrological processes in mountains river basins, and the necessity of reducing the computational costs. The main objective of this study was to quantify the differences between a simplified hydrological model, which uses only precipitation and temperature to compute the hydrological balance when simulating the impact of climate change, and an enhanced version of the model, which solves the energy balance to compute the actual evapotranspiration. For the meteorological forcing of future scenario, at-site bias-corrected time series based on two regional climate models were used. A quantile-based error-correction approach was used to downscale the regional climate model simulations to a point scale and to reduce its error characteristics. The study shows that a simple temperature-based approach for computing the evapotranspiration is sufficiently accurate for performing hydrological impact investigations of climate change for the Alpine river basin which was studied.

Stochastic Computations in Cortical Microcircuit Models

PubMed Central

Maass, Wolfgang

2013-01-01

Experimental data from neuroscience suggest that a substantial amount of knowledge is stored in the brain in the form of probability distributions over network states and trajectories of network states. We provide a theoretical foundation for this hypothesis by showing that even very detailed models for cortical microcircuits, with data-based diverse nonlinear neurons and synapses, have a stationary distribution of network states and trajectories of network states to which they converge exponentially fast from any initial state. We demonstrate that this convergence holds in spite of the non-reversibility of the stochastic dynamics of cortical microcircuits. We further show that, in the presence of background network oscillations, separate stationary distributions emerge for different phases of the oscillation, in accordance with experimentally reported phase-specific codes. We complement these theoretical results by computer simulations that investigate resulting computation times for typical probabilistic inference tasks on these internally stored distributions, such as marginalization or marginal maximum-a-posteriori estimation. Furthermore, we show that the inherent stochastic dynamics of generic cortical microcircuits enables them to quickly generate approximate solutions to difficult constraint satisfaction problems, where stored knowledge and current inputs jointly constrain possible solutions. This provides a powerful new computing paradigm for networks of spiking neurons, that also throws new light on how networks of neurons in the brain could carry out complex computational tasks such as prediction, imagination, memory recall and problem solving. PMID:24244126

Modeling of urban solid waste management system: The case of Dhaka city

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

Sufian, M.A.; Bala, B.K.

2007-07-01

This paper presents a system dynamics computer model to predict solid waste generation, collection capacity and electricity generation from solid waste and to assess the needs for waste management of the urban city of Dhaka, Bangladesh. Simulated results show that solid waste generation, collection capacity and electricity generation potential from solid waste increase with time. Population, uncleared waste, untreated waste, composite index and public concern are projected to increase with time for Dhaka city. Simulated results also show that increasing the budget for collection capacity alone does not improve environmental quality; rather an increased budget is required for both collectionmore » and treatment of solid wastes of Dhaka city. Finally, this model can be used as a computer laboratory for urban solid waste management (USWM) policy analysis.« less

Real-time simulation of biological soft tissues: a PGD approach.

PubMed

Niroomandi, S; González, D; Alfaro, I; Bordeu, F; Leygue, A; Cueto, E; Chinesta, F

2013-05-01

We introduce here a novel approach for the numerical simulation of nonlinear, hyperelastic soft tissues at kilohertz feedback rates necessary for haptic rendering. This approach is based upon the use of proper generalized decomposition techniques, a generalization of PODs. Proper generalized decomposition techniques can be considered as a means of a priori model order reduction and provides a physics-based meta-model without the need for prior computer experiments. The suggested strategy is thus composed of an offline phase, in which a general meta-model is computed, and an online evaluation phase in which the results are obtained at real time. Results are provided that show the potential of the proposed technique, together with some benchmark test that shows the accuracy of the method. Copyright © 2013 John Wiley & Sons, Ltd.

R&D Project on Algebra Software Seen to Show Promise

ERIC Educational Resources Information Center

Trotter, Andrew

2007-01-01

Computer software that shows students visual models of mathematical concepts--and lets them manipulate those models by doing math--has a certain intuitive appeal. Now, recent research on SimCalc Mathworlds, one of the pioneering examples of such software, is providing some of the best evidence so far that the approach can lead to gains in student…

Quantification of the effect of cross-shear and applied nominal contact pressure on the wear of moderately cross-linked polyethylene.

PubMed

Abdelgaied, Abdellatif; Brockett, Claire L; Liu, Feng; Jennings, Louise M; Fisher, John; Jin, Zhongmin

2013-01-01

Polyethylene wear is a great concern in total joint replacement. It is now considered a major limiting factor to the long life of such prostheses. Cross-linking has been introduced to reduce the wear of ultra-high-molecular-weight polyethylene (UHMWPE). Computational models have been used extensively for wear prediction and optimization of artificial knee designs. However, in order to be independent and have general applicability and predictability, computational wear models should be based on inputs from independent experimentally determined wear parameters (wear factors or wear coefficients). The objective of this study was to investigate moderately cross-linked UHMWPE, using a multidirectional pin-on-plate wear test machine, under a wide range of applied nominal contact pressure (from 1 to 11 MPa) and under five different kinematic inputs, varying from a purely linear track to a maximum rotation of +/- 55 degrees. A computational model, based on a direct simulation of the multidirectional pin-on-plate wear tester, was developed to quantify the degree of cross-shear (CS) of the polyethylene pins articulating against the metallic plates. The moderately cross-linked UHMWPE showed wear factors less than half of that reported in the literature for, the conventional UHMWPE, under the same loading and kinematic inputs. In addition, under high applied nominal contact stress, the moderately crosslinked UHMWPE wear showed lower dependence on the degree of CS compared to that under low applied nominal contact stress. The calculated wear coefficients were found to be independent of the applied nominal contact stress, in contrast to the wear factors that were shown to be highly pressure dependent. This study provided independent wear data for inputs into computational models for moderately cross-linked polyethylene and supported the application of wear coefficient-based computational wear models.

Evidence in Support of the Independent Channel Model Describing the Sensorimotor Control of Human Stance Using a Humanoid Robot

PubMed Central

Pasma, Jantsje H.; Assländer, Lorenz; van Kordelaar, Joost; de Kam, Digna; Mergner, Thomas; Schouten, Alfred C.

2018-01-01

The Independent Channel (IC) model is a commonly used linear balance control model in the frequency domain to analyze human balance control using system identification and parameter estimation. The IC model is a rudimentary and noise-free description of balance behavior in the frequency domain, where a stable model representation is not guaranteed. In this study, we conducted firstly time-domain simulations with added noise, and secondly robot experiments by implementing the IC model in a real-world robot (PostuRob II) to test the validity and stability of the model in the time domain and for real world situations. Balance behavior of seven healthy participants was measured during upright stance by applying pseudorandom continuous support surface rotations. System identification and parameter estimation were used to describe the balance behavior with the IC model in the frequency domain. The IC model with the estimated parameters from human experiments was implemented in Simulink for computer simulations including noise in the time domain and robot experiments using the humanoid robot PostuRob II. Again, system identification and parameter estimation were used to describe the simulated balance behavior. Time series, Frequency Response Functions, and estimated parameters from human experiments, computer simulations, and robot experiments were compared with each other. The computer simulations showed similar balance behavior and estimated control parameters compared to the human experiments, in the time and frequency domain. Also, the IC model was able to control the humanoid robot by keeping it upright, but showed small differences compared to the human experiments in the time and frequency domain, especially at high frequencies. We conclude that the IC model, a descriptive model in the frequency domain, can imitate human balance behavior also in the time domain, both in computer simulations with added noise and real world situations with a humanoid robot. This provides further evidence that the IC model is a valid description of human balance control. PMID:29615886

Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation

NASA Astrophysics Data System (ADS)

Fer, I.; Kelly, R.; Andrews, T.; Dietze, M.; Richardson, A. D.

2016-12-01

Our ability to forecast ecosystems is limited by how well we parameterize ecosystem models. Direct measurements for all model parameters are not always possible and inverse estimation of these parameters through Bayesian methods is computationally costly. A solution to computational challenges of Bayesian calibration is to approximate the posterior probability surface using a Gaussian Process that emulates the complex process-based model. Here we report the integration of this method within an ecoinformatics toolbox, Predictive Ecosystem Analyzer (PEcAn), and its application with two ecosystem models: SIPNET and ED2.1. SIPNET is a simple model, allowing application of MCMC methods both to the model itself and to its emulator. We used both approaches to assimilate flux (CO2 and latent heat), soil respiration, and soil carbon data from Bartlett Experimental Forest. This comparison showed that emulator is reliable in terms of convergence to the posterior distribution. A 10000-iteration MCMC analysis with SIPNET itself required more than two orders of magnitude greater computation time than an MCMC run of same length with its emulator. This difference would be greater for a more computationally demanding model. Validation of the emulator-calibrated SIPNET against both the assimilated data and out-of-sample data showed improved fit and reduced uncertainty around model predictions. We next applied the validated emulator method to the ED2, whose complexity precludes standard Bayesian data assimilation. We used the ED2 emulator to assimilate demographic data from a network of inventory plots. For validation of the calibrated ED2, we compared the model to results from Empirical Succession Mapping (ESM), a novel synthesis of successional patterns in Forest Inventory and Analysis data. Our results revealed that while the pre-assimilation ED2 formulation cannot capture the emergent demographic patterns from ESM analysis, constrained model parameters controlling demographic processes increased their agreement considerably.

Evidence in Support of the Independent Channel Model Describing the Sensorimotor Control of Human Stance Using a Humanoid Robot.

PubMed

Pasma, Jantsje H; Assländer, Lorenz; van Kordelaar, Joost; de Kam, Digna; Mergner, Thomas; Schouten, Alfred C

2018-01-01

The Independent Channel (IC) model is a commonly used linear balance control model in the frequency domain to analyze human balance control using system identification and parameter estimation. The IC model is a rudimentary and noise-free description of balance behavior in the frequency domain, where a stable model representation is not guaranteed. In this study, we conducted firstly time-domain simulations with added noise, and secondly robot experiments by implementing the IC model in a real-world robot (PostuRob II) to test the validity and stability of the model in the time domain and for real world situations. Balance behavior of seven healthy participants was measured during upright stance by applying pseudorandom continuous support surface rotations. System identification and parameter estimation were used to describe the balance behavior with the IC model in the frequency domain. The IC model with the estimated parameters from human experiments was implemented in Simulink for computer simulations including noise in the time domain and robot experiments using the humanoid robot PostuRob II. Again, system identification and parameter estimation were used to describe the simulated balance behavior. Time series, Frequency Response Functions, and estimated parameters from human experiments, computer simulations, and robot experiments were compared with each other. The computer simulations showed similar balance behavior and estimated control parameters compared to the human experiments, in the time and frequency domain. Also, the IC model was able to control the humanoid robot by keeping it upright, but showed small differences compared to the human experiments in the time and frequency domain, especially at high frequencies. We conclude that the IC model, a descriptive model in the frequency domain, can imitate human balance behavior also in the time domain, both in computer simulations with added noise and real world situations with a humanoid robot. This provides further evidence that the IC model is a valid description of human balance control.

A unified internal model theory to resolve the paradox of active versus passive self-motion sensation

PubMed Central

Angelaki, Dora E

2017-01-01

Brainstem and cerebellar neurons implement an internal model to accurately estimate self-motion during externally generated (‘passive’) movements. However, these neurons show reduced responses during self-generated (‘active’) movements, indicating that predicted sensory consequences of motor commands cancel sensory signals. Remarkably, the computational processes underlying sensory prediction during active motion and their relationship to internal model computations during passive movements remain unknown. We construct a Kalman filter that incorporates motor commands into a previously established model of optimal passive self-motion estimation. The simulated sensory error and feedback signals match experimentally measured neuronal responses during active and passive head and trunk rotations and translations. We conclude that a single sensory internal model can combine motor commands with vestibular and proprioceptive signals optimally. Thus, although neurons carrying sensory prediction error or feedback signals show attenuated modulation, the sensory cues and internal model are both engaged and critically important for accurate self-motion estimation during active head movements. PMID:29043978

Computer-aided design of the human aortic root.

PubMed

Ovcharenko, E A; Klyshnikov, K U; Vlad, A R; Sizova, I N; Kokov, A N; Nushtaev, D V; Yuzhalin, A E; Zhuravleva, I U

2014-11-01

The development of computer-based 3D models of the aortic root is one of the most important problems in constructing the prostheses for transcatheter aortic valve implantation. In the current study, we analyzed data from 117 patients with and without aortic valve disease and computed tomography data from 20 patients without aortic valvular diseases in order to estimate the average values of the diameter of the aortic annulus and other aortic root parameters. Based on these data, we developed a 3D model of human aortic root with unique geometry. Furthermore, in this study we show that by applying different material properties to the aortic annulus zone in our model, we can significantly improve the quality of the results of finite element analysis. To summarize, here we present four 3D models of human aortic root with unique geometry based on computational analysis of ECHO and CT data. We suggest that our models can be utilized for the development of better prostheses for transcatheter aortic valve implantation. Copyright © 2014 Elsevier Ltd. All rights reserved.

Further Investigation of the Support System Effects and Wing Twist on the NASA Common Research Model

NASA Technical Reports Server (NTRS)

Rivers, Melissa B.; Hunter, Craig A.; Campbell, Richard L.

2012-01-01

An experimental investigation of the NASA Common Research Model was conducted in the NASA Langley National Transonic Facility and NASA Ames 11-foot Transonic Wind Tunnel Facility for use in the Drag Prediction Workshop. As data from the experimental investigations was collected, a large difference in moment values was seen between the experiment and computational data from the 4th Drag Prediction Workshop. This difference led to a computational assessment to investigate model support system interference effects on the Common Research Model. The results from this investigation showed that the addition of the support system to the computational cases did increase the pitching moment so that it more closely matched the experimental results, but there was still a large discrepancy in pitching moment. This large discrepancy led to an investigation into the shape of the as-built model, which in turn led to a change in the computational grids and re-running of all the previous support system cases. The results of these cases are the focus of this paper.

Predictive codes of familiarity and context during the perceptual learning of facial identities

NASA Astrophysics Data System (ADS)

Apps, Matthew A. J.; Tsakiris, Manos

2013-11-01

Face recognition is a key component of successful social behaviour. However, the computational processes that underpin perceptual learning and recognition as faces transition from unfamiliar to familiar are poorly understood. In predictive coding, learning occurs through prediction errors that update stimulus familiarity, but recognition is a function of both stimulus and contextual familiarity. Here we show that behavioural responses on a two-option face recognition task can be predicted by the level of contextual and facial familiarity in a computational model derived from predictive-coding principles. Using fMRI, we show that activity in the superior temporal sulcus varies with the contextual familiarity in the model, whereas activity in the fusiform face area covaries with the prediction error parameter that updated facial familiarity. Our results characterize the key computations underpinning the perceptual learning of faces, highlighting that the functional properties of face-processing areas conform to the principles of predictive coding.

Computational and Experimental Study of the Structure of Diffusion Flames of Jet Fuel and Its Surrogates at Pressures up to 40 ATM

DTIC Science & Technology

2012-11-21

examination of some of the aromatics show that the model captures well benzene from toluene decomposition in BF, but underpredicts styrene and ethylbenzene ...critical toluene pyrolysis products and stable soot precursors were compared with computational models using two semi-detailed chemical mechanisms... ethylbenzene , which at least one of the mechanisms reproduces quite well. The largest measured species in the incipiently sooting flame is indene, whose

Coherence in the Visual Imagination.

PubMed

Vertolli, Michael O; Kelly, Matthew A; Davies, Jim

2018-04-01

An incoherent visualization is when aspects of different senses of a word (e.g., the biological "mouse" vs. the computer "mouse") are present in the same visualization (e.g., a visualization of a biological mouse in the same image with a computer tower). We describe and implement a new model of creating contextual coherence in the visual imagination called Coherencer, based on the SOILIE model of imagination. We show that Coherencer is able to generate scene descriptions that are more coherent than SOILIE's original approach as well as a parallel connectionist algorithm that is considered competitive in the literature on general coherence. We also show that co-occurrence probabilities are a better association representation than holographic vectors and that better models of coherence improve the resulting output independent of the association type that is used. Theoretically, we show that Coherencer is consistent with other models of cognitive generation. In particular, Coherencer is a similar, but more cognitively plausible model than the C 3 model of concept combination created by Costello and Keane (2000). We show that Coherencer is also consistent with both the modal schematic indices of perceptual symbol systems theory (Barsalou, 1999) and the amodal contextual constraints of Thagard's (2002) theory of coherence. Finally, we describe how Coherencer is consistent with contemporary research on the hippocampus, and we show evidence that the process of making a visualization coherent is serial. Copyright © 2017 Cognitive Science Society, Inc.

Identification of High-Risk Plaques Destined to Cause Acute Coronary Syndrome Using Coronary Computed Tomographic Angiography and Computational Fluid Dynamics.

PubMed

Lee, Joo Myung; Choi, Gilwoo; Koo, Bon-Kwon; Hwang, Doyeon; Park, Jonghanne; Zhang, Jinlong; Kim, Kyung-Jin; Tong, Yaliang; Kim, Hyun Jin; Grady, Leo; Doh, Joon-Hyung; Nam, Chang-Wook; Shin, Eun-Seok; Cho, Young-Seok; Choi, Su-Yeon; Chun, Eun Ju; Choi, Jin-Ho; Nørgaard, Bjarne L; Christiansen, Evald H; Niemen, Koen; Otake, Hiromasa; Penicka, Martin; de Bruyne, Bernard; Kubo, Takashi; Akasaka, Takashi; Narula, Jagat; Douglas, Pamela S; Taylor, Charles A; Kim, Hyo-Soo

2018-03-14

We investigated the utility of noninvasive hemodynamic assessment in the identification of high-risk plaques that caused subsequent acute coronary syndrome (ACS). ACS is a critical event that impacts the prognosis of patients with coronary artery disease. However, the role of hemodynamic factors in the development of ACS is not well-known. Seventy-two patients with clearly documented ACS and available coronary computed tomographic angiography (CTA) acquired between 1 month and 2 years before the development of ACS were included. In 66 culprit and 150 nonculprit lesions as a case-control design, the presence of adverse plaque characteristics (APC) was assessed and hemodynamic parameters (fractional flow reserve derived by coronary computed tomographic angiography [FFR CT ], change in FFR CT across the lesion [△FFR CT ], wall shear stress [WSS], and axial plaque stress) were analyzed using computational fluid dynamics. The best cut-off values for FFR CT , △FFR CT , WSS, and axial plaque stress were used to define the presence of adverse hemodynamic characteristics (AHC). The incremental discriminant and reclassification abilities for ACS prediction were compared among 3 models (model 1: percent diameter stenosis [%DS] and lesion length, model 2: model 1 + APC, and model 3: model 2 + AHC). The culprit lesions showed higher %DS (55.5 ± 15.4% vs. 43.1 ± 15.0%; p < 0.001) and higher prevalence of APC (80.3% vs. 42.0%; p < 0.001) than nonculprit lesions. Regarding hemodynamic parameters, culprit lesions showed lower FFR CT and higher △FFR CT , WSS, and axial plaque stress than nonculprit lesions (all p values <0.01). Among the 3 models, model 3, which included hemodynamic parameters, showed the highest c-index, and better discrimination (concordance statistic [c-index] 0.789 vs. 0.747; p = 0.014) and reclassification abilities (category-free net reclassification index 0.287; p = 0.047; relative integrated discrimination improvement 0.368; p < 0.001) than model 2. Lesions with both APC and AHC showed significantly higher risk of the culprit for subsequent ACS than those with no APC/AHC (hazard ratio: 11.75; 95% confidence interval: 2.85 to 48.51; p = 0.001) and with either APC or AHC (hazard ratio: 3.22; 95% confidence interval: 1.86 to 5.55; p < 0.001). Noninvasive hemodynamic assessment enhanced the identification of high-risk plaques that subsequently caused ACS. The integration of noninvasive hemodynamic assessments may improve the identification of culprit lesions for future ACS. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary CT Angiography and Computational Fluid Dynamic [EMERALD]; NCT02374775). Copyright © 2018 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Development of a Computational Chemical Vapor Deposition Model: Applications to Indium Nitride and Dicyanovinylaniline

NASA Technical Reports Server (NTRS)

Cardelino, Carlos

1999-01-01

A computational chemical vapor deposition (CVD) model is presented, that couples chemical reaction mechanisms with fluid dynamic simulations for vapor deposition experiments. The chemical properties of the systems under investigation are evaluated using quantum, molecular and statistical mechanics models. The fluid dynamic computations are performed using the CFD-ACE program, which can simulate multispecies transport, heat and mass transfer, gas phase chemistry, chemistry of adsorbed species, pulsed reactant flow and variable gravity conditions. Two experimental setups are being studied, in order to fabricate films of: (a) indium nitride (InN) from the gas or surface phase reaction of trimethylindium and ammonia; and (b) 4-(1,1)dicyanovinyl-dimethylaminoaniline (DCVA) by vapor deposition. Modeling of these setups requires knowledge of three groups of properties: thermodynamic properties (heat capacity), transport properties (diffusion, viscosity, and thermal conductivity), and kinetic properties (rate constants for all possible elementary chemical reactions). These properties are evaluated using computational methods whenever experimental data is not available for the species or for the elementary reactions. The chemical vapor deposition model is applied to InN and DCVA. Several possible InN mechanisms are proposed and analyzed. The CVD model simulations of InN show that the deposition rate of InN is more efficient when pulsing chemistry is used under conditions of high pressure and microgravity. An analysis of the chemical properties of DCVA show that DCVA dimers may form under certain conditions of physical vapor transport. CVD simulations of the DCVA system suggest that deposition of the DCVA dimer may play a small role in the film and crystal growth processes.

Kaiser Permanente-Sandia National Health Care Model: Phase 1 prototype final report. Part 2 -- Domain analysis

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

Edwards, D.; Yoshimura, A.; Butler, D.

This report describes the results of a Cooperative Research and Development Agreement between Sandia National Laboratories and Kaiser Permanente Southern California to develop a prototype computer model of Kaiser Permanente`s health care delivery system. As a discrete event simulation, SimHCO models for each of 100,000 patients the progression of disease, individual resource usage, and patient choices in a competitive environment. SimHCO is implemented in the object-oriented programming language C{sup 2}, stressing reusable knowledge and reusable software components. The versioned implementation of SimHCO showed that the object-oriented framework allows the program to grow in complexity in an incremental way. Furthermore, timingmore » calculations showed that SimHCO runs in a reasonable time on typical workstations, and that a second phase model will scale proportionally and run within the system constraints of contemporary computer technology.« less

NOTE: Acceleration of Monte Carlo-based scatter compensation for cardiac SPECT

NASA Astrophysics Data System (ADS)

Sohlberg, A.; Watabe, H.; Iida, H.

2008-07-01

Single proton emission computed tomography (SPECT) images are degraded by photon scatter making scatter compensation essential for accurate reconstruction. Reconstruction-based scatter compensation with Monte Carlo (MC) modelling of scatter shows promise for accurate scatter correction, but it is normally hampered by long computation times. The aim of this work was to accelerate the MC-based scatter compensation using coarse grid and intermittent scatter modelling. The acceleration methods were compared to un-accelerated implementation using MC-simulated projection data of the mathematical cardiac torso (MCAT) phantom modelling 99mTc uptake and clinical myocardial perfusion studies. The results showed that when combined the acceleration methods reduced the reconstruction time for 10 ordered subset expectation maximization (OS-EM) iterations from 56 to 11 min without a significant reduction in image quality indicating that the coarse grid and intermittent scatter modelling are suitable for MC-based scatter compensation in cardiac SPECT.

The Perspective Structure of Visual Space

PubMed Central

2015-01-01

Luneburg’s model has been the reference for experimental studies of visual space for almost seventy years. His claim for a curved visual space has been a source of inspiration for visual scientists as well as philosophers. The conclusion of many experimental studies has been that Luneburg’s model does not describe visual space in various tasks and conditions. Remarkably, no alternative model has been suggested. The current study explores perspective transformations of Euclidean space as a model for visual space. Computations show that the geometry of perspective spaces is considerably different from that of Euclidean space. Collinearity but not parallelism is preserved in perspective space and angles are not invariant under translation and rotation. Similar relationships have shown to be properties of visual space. Alley experiments performed early in the nineteenth century have been instrumental in hypothesizing curved visual spaces. Alleys were computed in perspective space and compared with reconstructed alleys of Blumenfeld. Parallel alleys were accurately described by perspective geometry. Accurate distance alleys were derived from parallel alleys by adjusting the interstimulus distances according to the size-distance invariance hypothesis. Agreement between computed and experimental alleys and accommodation of experimental results that rejected Luneburg’s model show that perspective space is an appropriate model for how we perceive orientations and angles. The model is also appropriate for perceived distance ratios between stimuli but fails to predict perceived distances. PMID:27648222

Feedforward object-vision models only tolerate small image variations compared to human

PubMed Central

Ghodrati, Masoud; Farzmahdi, Amirhossein; Rajaei, Karim; Ebrahimpour, Reza; Khaligh-Razavi, Seyed-Mahdi

2014-01-01

Invariant object recognition is a remarkable ability of primates' visual system that its underlying mechanism has constantly been under intense investigations. Computational modeling is a valuable tool toward understanding the processes involved in invariant object recognition. Although recent computational models have shown outstanding performances on challenging image databases, they fail to perform well in image categorization under more complex image variations. Studies have shown that making sparse representation of objects by extracting more informative visual features through a feedforward sweep can lead to higher recognition performances. Here, however, we show that when the complexity of image variations is high, even this approach results in poor performance compared to humans. To assess the performance of models and humans in invariant object recognition tasks, we built a parametrically controlled image database consisting of several object categories varied in different dimensions and levels, rendered from 3D planes. Comparing the performance of several object recognition models with human observers shows that only in low-level image variations the models perform similar to humans in categorization tasks. Furthermore, the results of our behavioral experiments demonstrate that, even under difficult experimental conditions (i.e., briefly presented masked stimuli with complex image variations), human observers performed outstandingly well, suggesting that the models are still far from resembling humans in invariant object recognition. Taken together, we suggest that learning sparse informative visual features, although desirable, is not a complete solution for future progresses in object-vision modeling. We show that this approach is not of significant help in solving the computational crux of object recognition (i.e., invariant object recognition) when the identity-preserving image variations become more complex. PMID:25100986

Analysis of a Multi-Fidelity Surrogate for Handling Real Gas Equations of State

NASA Astrophysics Data System (ADS)

Ouellet, Frederick; Park, Chanyoung; Rollin, Bertrand; Balachandar, S.

2017-06-01

The explosive dispersal of particles is a complex multiphase and multi-species fluid flow problem. In these flows, the detonation products of the explosive must be treated as real gas while the ideal gas equation of state is used for the surrounding air. As the products expand outward from the detonation point, they mix with ambient air and create a mixing region where both state equations must be satisfied. One of the most accurate, yet computationally expensive, methods to handle this problem is an algorithm that iterates between both equations of state until pressure and thermal equilibrium are achieved inside of each computational cell. This work aims to use a multi-fidelity surrogate model to replace this process. A Kriging model is used to produce a curve fit which interpolates selected data from the iterative algorithm using Bayesian statistics. We study the model performance with respect to the iterative method in simulations using a finite volume code. The model's (i) computational speed, (ii) memory requirements and (iii) computational accuracy are analyzed to show the benefits of this novel approach. Also, optimizing the combination of model accuracy and computational speed through the choice of sampling points is explained. This work was supported by the U.S. Department of Energy, National Nuclear Security Administration, Advanced Simulation and Computing Program as a Cooperative Agreement under the Predictive Science Academic Alliance Program under Contract No. DE-NA0002378.

Optimization of tomographic reconstruction workflows on geographically distributed resources

PubMed Central

Bicer, Tekin; Gürsoy, Doǧa; Kettimuthu, Rajkumar; De Carlo, Francesco; Foster, Ian T.

2016-01-01

New technological advancements in synchrotron light sources enable data acquisitions at unprecedented levels. This emergent trend affects not only the size of the generated data but also the need for larger computational resources. Although beamline scientists and users have access to local computational resources, these are typically limited and can result in extended execution times. Applications that are based on iterative processing as in tomographic reconstruction methods require high-performance compute clusters for timely analysis of data. Here, time-sensitive analysis and processing of Advanced Photon Source data on geographically distributed resources are focused on. Two main challenges are considered: (i) modeling of the performance of tomographic reconstruction workflows and (ii) transparent execution of these workflows on distributed resources. For the former, three main stages are considered: (i) data transfer between storage and computational resources, (i) wait/queue time of reconstruction jobs at compute resources, and (iii) computation of reconstruction tasks. These performance models allow evaluation and estimation of the execution time of any given iterative tomographic reconstruction workflow that runs on geographically distributed resources. For the latter challenge, a workflow management system is built, which can automate the execution of workflows and minimize the user interaction with the underlying infrastructure. The system utilizes Globus to perform secure and efficient data transfer operations. The proposed models and the workflow management system are evaluated by using three high-performance computing and two storage resources, all of which are geographically distributed. Workflows were created with different computational requirements using two compute-intensive tomographic reconstruction algorithms. Experimental evaluation shows that the proposed models and system can be used for selecting the optimum resources, which in turn can provide up to 3.13× speedup (on experimented resources). Moreover, the error rates of the models range between 2.1 and 23.3% (considering workflow execution times), where the accuracy of the model estimations increases with higher computational demands in reconstruction tasks. PMID:27359149

Random noise effects in pulse-mode digital multilayer neural networks.

PubMed

Kim, Y C; Shanblatt, M A

1995-01-01

A pulse-mode digital multilayer neural network (DMNN) based on stochastic computing techniques is implemented with simple logic gates as basic computing elements. The pulse-mode signal representation and the use of simple logic gates for neural operations lead to a massively parallel yet compact and flexible network architecture, well suited for VLSI implementation. Algebraic neural operations are replaced by stochastic processes using pseudorandom pulse sequences. The distributions of the results from the stochastic processes are approximated using the hypergeometric distribution. Synaptic weights and neuron states are represented as probabilities and estimated as average pulse occurrence rates in corresponding pulse sequences. A statistical model of the noise (error) is developed to estimate the relative accuracy associated with stochastic computing in terms of mean and variance. Computational differences are then explained by comparison to deterministic neural computations. DMNN feedforward architectures are modeled in VHDL using character recognition problems as testbeds. Computational accuracy is analyzed, and the results of the statistical model are compared with the actual simulation results. Experiments show that the calculations performed in the DMNN are more accurate than those anticipated when Bernoulli sequences are assumed, as is common in the literature. Furthermore, the statistical model successfully predicts the accuracy of the operations performed in the DMNN.

Numerical Experiments with a Turbulent Single-Mode Rayleigh-Taylor Instability

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

Cloutman, L.D.

2000-04-01

Direct numerical simulation is a powerful tool for studying turbulent flows. Unfortunately, it is also computationally expensive and often beyond the reach of the largest, fastest computers. Consequently, a variety of turbulence models have been devised to allow tractable and affordable simulations of averaged flow fields. Unfortunately, these present a variety of practical difficulties, including the incorporation of varying degrees of empiricism and phenomenology, which leads to a lack of universality. This unsatisfactory state of affairs has led to the speculation that one can avoid the expense and bother of using a turbulence model by relying on the grid andmore » numerical diffusion of the computational fluid dynamics algorithm to introduce a spectral cutoff on the flow field and to provide dissipation at the grid scale, thereby mimicking two main effects of a large eddy simulation model. This paper shows numerical examples of a single-mode Rayleigh-Taylor instability in which this procedure produces questionable results. We then show a dramatic improvement when two simple subgrid-scale models are employed. This study also illustrates the extreme sensitivity to initial conditions that is a common feature of turbulent flows.« less

Mapping snow depth return levels: smooth spatial modeling versus station interpolation

NASA Astrophysics Data System (ADS)

Blanchet, J.; Lehning, M.

2010-12-01

For adequate risk management in mountainous countries, hazard maps for extreme snow events are needed. This requires the computation of spatial estimates of return levels. In this article we use recent developments in extreme value theory and compare two main approaches for mapping snow depth return levels from in situ measurements. The first one is based on the spatial interpolation of pointwise extremal distributions (the so-called Generalized Extreme Value distribution, GEV henceforth) computed at station locations. The second one is new and based on the direct estimation of a spatially smooth GEV distribution with the joint use of all stations. We compare and validate the different approaches for modeling annual maximum snow depth measured at 100 sites in Switzerland during winters 1965-1966 to 2007-2008. The results show a better performance of the smooth GEV distribution fitting, in particular where the station network is sparser. Smooth return level maps can be computed from the fitted model without any further interpolation. Their regional variability can be revealed by removing the altitudinal dependent covariates in the model. We show how return levels and their regional variability are linked to the main climatological patterns of Switzerland.

Mechanism and computational model for Lyman-α-radiation generation by high-intensity-laser four-wave mixing in Kr-Ar gas

NASA Astrophysics Data System (ADS)

Louchev, Oleg A.; Bakule, Pavel; Saito, Norihito; Wada, Satoshi; Yokoyama, Koji; Ishida, Katsuhiko; Iwasaki, Masahiko

2011-09-01

We present a theoretical model combined with a computational study of a laser four-wave mixing process under optical discharge in which the non-steady-state four-wave amplitude equations are integrated with the kinetic equations of initial optical discharge and electron avalanche ionization in Kr-Ar gas. The model is validated by earlier experimental data showing strong inhibition of the generation of pulsed, tunable Lyman-α (Ly-α) radiation when using sum-difference frequency mixing of 212.6 nm and tunable infrared radiation (820-850 nm). The rigorous computational approach to the problem reveals the possibility and mechanism of strong auto-oscillations in sum-difference resonant Ly-α generation due to the combined effect of (i) 212.6-nm (2+1)-photon ionization producing initial electrons, followed by (ii) the electron avalanche dominated by 843-nm radiation, and (iii) the final breakdown of the phase matching condition. The model shows that the final efficiency of Ly-α radiation generation can achieve a value of ˜5×10-4 which is restricted by the total combined absorption of the fundamental and generated radiation.

Quantum simulations with noisy quantum computers

NASA Astrophysics Data System (ADS)

Gambetta, Jay

Quantum computing is a new computational paradigm that is expected to lie beyond the standard model of computation. This implies a quantum computer can solve problems that can't be solved by a conventional computer with tractable overhead. To fully harness this power we need a universal fault-tolerant quantum computer. However the overhead in building such a machine is high and a full solution appears to be many years away. Nevertheless, we believe that we can build machines in the near term that cannot be emulated by a conventional computer. It is then interesting to ask what these can be used for. In this talk we will present our advances in simulating complex quantum systems with noisy quantum computers. We will show experimental implementations of this on some small quantum computers.

Using generalized additive (mixed) models to analyze single case designs.

PubMed

Shadish, William R; Zuur, Alain F; Sullivan, Kristynn J

2014-04-01

This article shows how to apply generalized additive models and generalized additive mixed models to single-case design data. These models excel at detecting the functional form between two variables (often called trend), that is, whether trend exists, and if it does, what its shape is (e.g., linear and nonlinear). In many respects, however, these models are also an ideal vehicle for analyzing single-case designs because they can consider level, trend, variability, overlap, immediacy of effect, and phase consistency that single-case design researchers examine when interpreting a functional relation. We show how these models can be implemented in a wide variety of ways to test whether treatment is effective, whether cases differ from each other, whether treatment effects vary over cases, and whether trend varies over cases. We illustrate diagnostic statistics and graphs, and we discuss overdispersion of data in detail, with examples of quasibinomial models for overdispersed data, including how to compute dispersion and quasi-AIC fit indices in generalized additive models. We show how generalized additive mixed models can be used to estimate autoregressive models and random effects and discuss the limitations of the mixed models compared to generalized additive models. We provide extensive annotated syntax for doing all these analyses in the free computer program R. Copyright © 2013 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Development of Parallel Code for the Alaska Tsunami Forecast Model

NASA Astrophysics Data System (ADS)

Bahng, B.; Knight, W. R.; Whitmore, P.

2014-12-01

The Alaska Tsunami Forecast Model (ATFM) is a numerical model used to forecast propagation and inundation of tsunamis generated by earthquakes and other means in both the Pacific and Atlantic Oceans. At the U.S. National Tsunami Warning Center (NTWC), the model is mainly used in a pre-computed fashion. That is, results for hundreds of hypothetical events are computed before alerts, and are accessed and calibrated with observations during tsunamis to immediately produce forecasts. ATFM uses the non-linear, depth-averaged, shallow-water equations of motion with multiply nested grids in two-way communications between domains of each parent-child pair as waves get closer to coastal waters. Even with the pre-computation the task becomes non-trivial as sub-grid resolution gets finer. Currently, the finest resolution Digital Elevation Models (DEM) used by ATFM are 1/3 arc-seconds. With a serial code, large or multiple areas of very high resolution can produce run-times that are unrealistic even in a pre-computed approach. One way to increase the model performance is code parallelization used in conjunction with a multi-processor computing environment. NTWC developers have undertaken an ATFM code-parallelization effort to streamline the creation of the pre-computed database of results with the long term aim of tsunami forecasts from source to high resolution shoreline grids in real time. Parallelization will also permit timely regeneration of the forecast model database with new DEMs; and, will make possible future inclusion of new physics such as the non-hydrostatic treatment of tsunami propagation. The purpose of our presentation is to elaborate on the parallelization approach and to show the compute speed increase on various multi-processor systems.

Numerical study of a separating and reattaching flow by using Reynolds-stress tubulence closure

NASA Technical Reports Server (NTRS)

Amano, R. S.; Goel, P.

1983-01-01

The numerical study of the Reynolds-stress turbulence closure for separating, reattaching, recirculating and redeveloping flow is summarized. The calculations were made for two different closure models of pressure - strain correlation. The results were compared with the experimental data. Furthermore, these results were compared with the computations made by using the one layer and three layer treatment of k-epsilon turbulence model which were developed. Generally the computations by the Reynolds-stress model show better results than those by the k-epsilon model, in particular, some improvement was noticed in the redeveloping region of the separating and reattaching flow in a pipe with sudden expansion.

Graphical Models for Ordinal Data

PubMed Central

Guo, Jian; Levina, Elizaveta; Michailidis, George; Zhu, Ji

2014-01-01

A graphical model for ordinal variables is considered, where it is assumed that the data are generated by discretizing the marginal distributions of a latent multivariate Gaussian distribution. The relationships between these ordinal variables are then described by the underlying Gaussian graphical model and can be inferred by estimating the corresponding concentration matrix. Direct estimation of the model is computationally expensive, but an approximate EM-like algorithm is developed to provide an accurate estimate of the parameters at a fraction of the computational cost. Numerical evidence based on simulation studies shows the strong performance of the algorithm, which is also illustrated on data sets on movie ratings and an educational survey. PMID:26120267

Quantum Computation Using Optically Coupled Quantum Dot Arrays

NASA Technical Reports Server (NTRS)

Pradhan, Prabhakar; Anantram, M. P.; Wang, K. L.; Roychowhury, V. P.; Saini, Subhash (Technical Monitor)

1998-01-01

A solid state model for quantum computation has potential advantages in terms of the ease of fabrication, characterization, and integration. The fundamental requirements for a quantum computer involve the realization of basic processing units (qubits), and a scheme for controlled switching and coupling among the qubits, which enables one to perform controlled operations on qubits. We propose a model for quantum computation based on optically coupled quantum dot arrays, which is computationally similar to the atomic model proposed by Cirac and Zoller. In this model, individual qubits are comprised of two coupled quantum dots, and an array of these basic units is placed in an optical cavity. Switching among the states of the individual units is done by controlled laser pulses via near field interaction using the NSOM technology. Controlled rotations involving two or more qubits are performed via common cavity mode photon. We have calculated critical times, including the spontaneous emission and switching times, and show that they are comparable to the best times projected for other proposed models of quantum computation. We have also shown the feasibility of accessing individual quantum dots using the NSOM technology by calculating the photon density at the tip, and estimating the power necessary to perform the basic controlled operations. We are currently in the process of estimating the decoherence times for this system; however, we have formulated initial arguments which seem to indicate that the decoherence times will be comparable, if not longer, than many other proposed models.

Using multi-criteria analysis of simulation models to understand complex biological systems

Treesearch

Maureen C. Kennedy; E. David Ford

2011-01-01

Scientists frequently use computer-simulation models to help solve complex biological problems. Typically, such models are highly integrated, they produce multiple outputs, and standard methods of model analysis are ill suited for evaluating them. We show how multi-criteria optimization with Pareto optimality allows for model outputs to be compared to multiple system...

Systems Based Study of the Therapeutic Potential of Small Charged Molecules for the Inhibition of IL-1 Mediated Cartilage Degradation

PubMed Central

Kar, Saptarshi; Smith, David W.; Gardiner, Bruce S.; Grodzinsky, Alan J.

2016-01-01

Inflammatory cytokines are key drivers of cartilage degradation in post-traumatic osteoarthritis. Cartilage degradation mediated by these inflammatory cytokines has been extensively investigated using in vitro experimental systems. Based on one such study, we have developed a computational model to quantitatively assess the impact of charged small molecules intended to inhibit IL-1 mediated cartilage degradation. We primarily focus on the simplest possible computational model of small molecular interaction with the IL-1 system—direct binding of the small molecule to the active site on the IL-1 molecule itself. We first use the model to explore the uptake and release kinetics of the small molecule inhibitor by cartilage tissue. Our results show that negatively charged small molecules are excluded from the negatively charged cartilage tissue and have uptake kinetics in the order of hours. In contrast, the positively charged small molecules are drawn into the cartilage with uptake and release timescales ranging from hours to days. Using our calibrated computational model, we subsequently explore the effect of small molecule charge and binding constant on the rate of cartilage degradation. The results from this analysis indicate that the small molecules are most effective in inhibiting cartilage degradation if they are either positively charged and/or bind strongly to IL-1α, or both. Furthermore, our results showed that the cartilage structural homeostasis can be restored by the small molecule if administered within six days following initial tissue exposure to IL-1α. We finally extended the scope of the computational model by simulating the competitive inhibition of cartilage degradation by the small molecule. Results from this model show that small molecules are more efficient in inhibiting cartilage degradation by binding directly to IL-1α rather than binding to IL-1α receptors. The results from this study can be used as a template for the design and development of more pharmacologically effective osteoarthritis drugs, and to investigate possible therapeutic options. PMID:27977731

Mental health assessment: Inference, explanation, and coherence.

PubMed

Thagard, Paul; Larocque, Laurette

2018-06-01

Mental health professionals such as psychiatrists and psychotherapists assess their patients by identifying disorders that explain their symptoms. This assessment requires an inference to the best explanation that compares different disorders with respect to how well they explain the available evidence. Such comparisons are captured by the theory of explanatory coherence that states 7 principles for evaluating competing hypotheses in the light of evidence. The computational model ECHO shows how explanatory coherence can be efficiently computed. We show the applicability of explanatory coherence to mental health assessment by modelling a case of psychiatric interviewing and a case of psychotherapeutic evaluation. We argue that this approach is more plausible than Bayesian inference and hermeneutic interpretation. © 2018 John Wiley & Sons, Ltd.

Static and Dynamic Model Update of an Inflatable/Rigidizable Torus Structure

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Reaves, mercedes C.

2006-01-01

The present work addresses the development of an experimental and computational procedure for validating finite element models. A torus structure, part of an inflatable/rigidizable Hexapod, is used to demonstrate the approach. Because of fabrication, materials, and geometric uncertainties, a statistical approach combined with optimization is used to modify key model parameters. Static test results are used to update stiffness parameters and dynamic test results are used to update the mass distribution. Updated parameters are computed using gradient and non-gradient based optimization algorithms. Results show significant improvements in model predictions after parameters are updated. Lessons learned in the areas of test procedures, modeling approaches, and uncertainties quantification are presented.

Computer simulation of the coffee leaf miner using sexual Penna aging model

NASA Astrophysics Data System (ADS)

de Oliveira, A. C. S.; Martins, S. G. F.; Zacarias, M. S.

2008-01-01

Forecast models based on climatic conditions are of great interest in Integrated Pest Management (IPM) programs. The success of these models depends, among other factors, on the knowledge of the temperature effect on the pests’ population dynamics. In this direction, a computer simulation was made for the population dynamics of the coffee leaf miner, L. coffeella, at different temperatures, considering experimental data relative to the pest. The age structure was inserted into the dynamics through sexual Penna Model. The results obtained, such as life expectancy, growth rate and annual generations’ number, in agreement to those in laboratory and field conditions, show that the simulation can be used as a forecast model for controlling L. coffeella.

Turbulent shear layers in confining channels

NASA Astrophysics Data System (ADS)

Benham, Graham P.; Castrejon-Pita, Alfonso A.; Hewitt, Ian J.; Please, Colin P.; Style, Rob W.; Bird, Paul A. D.

2018-06-01

We present a simple model for the development of shear layers between parallel flows in confining channels. Such flows are important across a wide range of topics from diffusers, nozzles and ducts to urban air flow and geophysical fluid dynamics. The model approximates the flow in the shear layer as a linear profile separating uniform-velocity streams. Both the channel geometry and wall drag affect the development of the flow. The model shows good agreement with both particle image velocimetry experiments and computational turbulence modelling. The simplicity and low computational cost of the model allows it to be used for benchmark predictions and design purposes, which we demonstrate by investigating optimal pressure recovery in diffusers with non-uniform inflow.

OnGuard, a Computational Platform for Quantitative Kinetic Modeling of Guard Cell Physiology1[W][OA

PubMed Central

Hills, Adrian; Chen, Zhong-Hua; Amtmann, Anna; Blatt, Michael R.; Lew, Virgilio L.

2012-01-01

Stomatal guard cells play a key role in gas exchange for photosynthesis while minimizing transpirational water loss from plants by opening and closing the stomatal pore. Foliar gas exchange has long been incorporated into mathematical models, several of which are robust enough to recapitulate transpirational characteristics at the whole-plant and community levels. Few models of stomata have been developed from the bottom up, however, and none are sufficiently generalized to be widely applicable in predicting stomatal behavior at a cellular level. We describe here the construction of computational models for the guard cell, building on the wealth of biophysical and kinetic knowledge available for guard cell transport, signaling, and homeostasis. The OnGuard software was constructed with the HoTSig library to incorporate explicitly all of the fundamental properties for transporters at the plasma membrane and tonoplast, the salient features of osmolite metabolism, and the major controls of cytosolic-free Ca2+ concentration and pH. The library engenders a structured approach to tier and interrelate computational elements, and the OnGuard software allows ready access to parameters and equations ‘on the fly’ while enabling the network of components within each model to interact computationally. We show that an OnGuard model readily achieves stability in a set of physiologically sensible baseline or Reference States; we also show the robustness of these Reference States in adjusting to changes in environmental parameters and the activities of major groups of transporters both at the tonoplast and plasma membrane. The following article addresses the predictive power of the OnGuard model to generate unexpected and counterintuitive outputs. PMID:22635116

Laboratory modeling and analysis of aircraft-lightning interactions

NASA Technical Reports Server (NTRS)

Turner, C. D.; Trost, T. F.

1982-01-01

Modeling studies of the interaction of a delta wing aircraft with direct lightning strikes were carried out using an approximate scale model of an F-106B. The model, which is three feet in length, is subjected to direct injection of fast current pulses supplied by wires, which simulate the lightning channel and are attached at various locations on the model. Measurements are made of the resulting transient electromagnetic fields using time derivative sensors. The sensor outputs are sampled and digitized by computer. The noise level is reduced by averaging the sensor output from ten input pulses at each sample time. Computer analysis of the measured fields includes Fourier transformation and the computation of transfer functions for the model. Prony analysis is also used to determine the natural frequencies of the model. Comparisons of model natural frequencies extracted by Prony analysis with those for in flight direct strike data usually show lower damping in the in flight case. This is indicative of either a lightning channel with a higher impedance than the wires on the model, only one attachment point, or short streamers instead of a long channel.

Prediction of the structure of fuel sprays in gas turbine combustors

NASA Technical Reports Server (NTRS)

Shuen, J. S.

1985-01-01

The structure of fuel sprays in a combustion chamber is theoretically investigated using computer models of current interest. Three representative spray models are considered: (1) a locally homogeneous flow (LHF) model, which assumes infinitely fast interphase transport rates; (2) a deterministic separated flow (DSF) model, which considers finite rates of interphase transport but ignores effects of droplet/turbulence interactions; and (3) a stochastic separated flow (SSF) model, which considers droplet/turbulence interactions using random sampling for turbulence properties in conjunction with random-walk computations for droplet motion and transport. Two flow conditions are studied to investigate the influence of swirl on droplet life histories and the effects of droplet/turbulence interactions on flow properties. Comparison of computed results with the experimental data show that general features of the flow structure can be predicted with reasonable accuracy using the two separated flow models. In contrast, the LHF model overpredicts the rate of development of the flow. While the SSF model provides better agreement with measurements than the DSF model, definitive evaluation of the significance of droplet/turbulence interaction is not achieved due to uncertainties in the spray initial conditions.

Computational Modeling and Mathematics Applied to the Physical Sciences.

ERIC Educational Resources Information Center

National Academy of Sciences - National Research Council, Washington, DC.

One aim of this report is to show and emphasize that in the computational approaches to most of today's pressing and challenging scientific and technological problems, the mathematical aspects cannot and should not be considered in isolation. Following an introductory chapter, chapter 2 discusses a number of typical problems leading to…

Structural Equation Model to Validate: Mathematics-Computer Interaction, Computer Confidence, Mathematics Commitment, Mathematics Motivation and Mathematics Confidence

ERIC Educational Resources Information Center

Garcia-Santillán, Arturo; Moreno-Garcia, Elena; Escalera-Chávez, Milka E.; Rojas-Kramer, Carlos A.; Pozos-Texon, Felipe

2016-01-01

Most mathematics students show a definite tendency toward an attitudinal deficiency, which can be primarily understood as intolerance to the matter, affecting their scholar performance adversely. In addition, information and communication technologies have been gradually included within the process of teaching mathematics. Such adoption of…

Design and Development Computer-Based E-Learning Teaching Material for Improving Mathematical Understanding Ability and Spatial Sense of Junior High School Students

NASA Astrophysics Data System (ADS)

Nurjanah; Dahlan, J. A.; Wibisono, Y.

2017-02-01

This paper aims to make a design and development computer-based e-learning teaching material for improving mathematical understanding ability and spatial sense of junior high school students. Furthermore, the particular aims are (1) getting teaching material design, evaluation model, and intrument to measure mathematical understanding ability and spatial sense of junior high school students; (2) conducting trials computer-based e-learning teaching material model, asessment, and instrument to develop mathematical understanding ability and spatial sense of junior high school students; (3) completing teaching material models of computer-based e-learning, assessment, and develop mathematical understanding ability and spatial sense of junior high school students; (4) resulting research product is teaching materials of computer-based e-learning. Furthermore, the product is an interactive learning disc. The research method is used of this study is developmental research which is conducted by thought experiment and instruction experiment. The result showed that teaching materials could be used very well. This is based on the validation of computer-based e-learning teaching materials, which is validated by 5 multimedia experts. The judgement result of face and content validity of 5 validator shows that the same judgement result to the face and content validity of each item test of mathematical understanding ability and spatial sense. The reliability test of mathematical understanding ability and spatial sense are 0,929 and 0,939. This reliability test is very high. While the validity of both tests have a high and very high criteria.

Mathematical and computational model for the analysis of micro hybrid rocket motor

NASA Astrophysics Data System (ADS)

Stoia-Djeska, Marius; Mingireanu, Florin

2012-11-01

The hybrid rockets use a two-phase propellant system. In the present work we first develop a simplified model of the coupling of the hybrid combustion process with the complete unsteady flow, starting from the combustion port and ending with the nozzle. The physical and mathematical model are adapted to the simulations of micro hybrid rocket motors. The flow model is based on the one-dimensional Euler equations with source terms. The flow equations and the fuel regression rate law are solved in a coupled manner. The platform of the numerical simulations is an implicit fourth-order Runge-Kutta second order cell-centred finite volume method. The numerical results obtained with this model show a good agreement with published experimental and numerical results. The computational model developed in this work is simple, computationally efficient and offers the advantage of taking into account a large number of functional and constructive parameters that are used by the engineers.

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

NASA Astrophysics Data System (ADS)

Raghupathy, Arun; Ghia, Karman; Ghia, Urmila

2008-11-01

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

Model reduction for agent-based social simulation: coarse-graining a civil violence model.

PubMed

Zou, Yu; Fonoberov, Vladimir A; Fonoberova, Maria; Mezic, Igor; Kevrekidis, Ioannis G

2012-06-01

Agent-based modeling (ABM) constitutes a powerful computational tool for the exploration of phenomena involving emergent dynamic behavior in the social sciences. This paper demonstrates a computer-assisted approach that bridges the significant gap between the single-agent microscopic level and the macroscopic (coarse-grained population) level, where fundamental questions must be rationally answered and policies guiding the emergent dynamics devised. Our approach will be illustrated through an agent-based model of civil violence. This spatiotemporally varying ABM incorporates interactions between a heterogeneous population of citizens [active (insurgent), inactive, or jailed] and a population of police officers. Detailed simulations exhibit an equilibrium punctuated by periods of social upheavals. We show how to effectively reduce the agent-based dynamics to a stochastic model with only two coarse-grained degrees of freedom: the number of jailed citizens and the number of active ones. The coarse-grained model captures the ABM dynamics while drastically reducing the computation time (by a factor of approximately 20).

Model reduction for agent-based social simulation: Coarse-graining a civil violence model

NASA Astrophysics Data System (ADS)

Zou, Yu; Fonoberov, Vladimir A.; Fonoberova, Maria; Mezic, Igor; Kevrekidis, Ioannis G.

2012-06-01

Agent-based modeling (ABM) constitutes a powerful computational tool for the exploration of phenomena involving emergent dynamic behavior in the social sciences. This paper demonstrates a computer-assisted approach that bridges the significant gap between the single-agent microscopic level and the macroscopic (coarse-grained population) level, where fundamental questions must be rationally answered and policies guiding the emergent dynamics devised. Our approach will be illustrated through an agent-based model of civil violence. This spatiotemporally varying ABM incorporates interactions between a heterogeneous population of citizens [active (insurgent), inactive, or jailed] and a population of police officers. Detailed simulations exhibit an equilibrium punctuated by periods of social upheavals. We show how to effectively reduce the agent-based dynamics to a stochastic model with only two coarse-grained degrees of freedom: the number of jailed citizens and the number of active ones. The coarse-grained model captures the ABM dynamics while drastically reducing the computation time (by a factor of approximately 20).

A computational approach for coupled 1D and 2D/3D CFD modelling of pulse Tube cryocoolers

NASA Astrophysics Data System (ADS)

Fang, T.; Spoor, P. S.; Ghiaasiaan, S. M.

2017-12-01

The physics behind Stirling-type cryocoolers are complicated. One dimensional (1D) simulation tools offer limited details and accuracy, in particular for cryocoolers that have non-linear configurations. Multi-dimensional Computational Fluid Dynamic (CFD) methods are useful but are computationally expensive in simulating cyrocooler systems in their entirety. In view of the fact that some components of a cryocooler, e.g., inertance tubes and compliance tanks, can be modelled as 1D components with little loss of critical information, a 1D-2D/3D coupled model was developed. Accordingly, one-dimensional - like components are represented by specifically developed routines. These routines can be coupled to CFD codes and provide boundary conditions for 2D/3D CFD simulations. The developed coupled model, while preserving sufficient flow field details, is two orders of magnitude faster than equivalent 2D/3D CFD models. The predictions show good agreement with experimental data and 2D/3D CFD model.

Efficient Embedded Decoding of Neural Network Language Models in a Machine Translation System.

PubMed

Zamora-Martinez, Francisco; Castro-Bleda, Maria Jose

2018-02-22

Neural Network Language Models (NNLMs) are a successful approach to Natural Language Processing tasks, such as Machine Translation. We introduce in this work a Statistical Machine Translation (SMT) system which fully integrates NNLMs in the decoding stage, breaking the traditional approach based on [Formula: see text]-best list rescoring. The neural net models (both language models (LMs) and translation models) are fully coupled in the decoding stage, allowing to more strongly influence the translation quality. Computational issues were solved by using a novel idea based on memorization and smoothing of the softmax constants to avoid their computation, which introduces a trade-off between LM quality and computational cost. These ideas were studied in a machine translation task with different combinations of neural networks used both as translation models and as target LMs, comparing phrase-based and [Formula: see text]-gram-based systems, showing that the integrated approach seems more promising for [Formula: see text]-gram-based systems, even with nonfull-quality NNLMs.

Comparisons of Computed Mobile Phone Induced SAR in the SAM Phantom to That in Anatomically Correct Models of the Human Head.

PubMed

Beard, Brian B; Kainz, Wolfgang; Onishi, Teruo; Iyama, Takahiro; Watanabe, Soichi; Fujiwara, Osamu; Wang, Jianqing; Bit-Babik, Giorgi; Faraone, Antonio; Wiart, Joe; Christ, Andreas; Kuster, Niels; Lee, Ae-Kyoung; Kroeze, Hugo; Siegbahn, Martin; Keshvari, Jafar; Abrishamkar, Houman; Simon, Winfried; Manteuffel, Dirk; Nikoloski, Neviana

2006-06-05

The specific absorption rates (SAR) determined computationally in the specific anthropomorphic mannequin (SAM) and anatomically correct models of the human head when exposed to a mobile phone model are compared as part of a study organized by IEEE Standards Coordinating Committee 34, SubCommittee 2, and Working Group 2, and carried out by an international task force comprising 14 government, academic, and industrial research institutions. The detailed study protocol defined the computational head and mobile phone models. The participants used different finite-difference time-domain software and independently positioned the mobile phone and head models in accordance with the protocol. The results show that when the pinna SAR is calculated separately from the head SAR, SAM produced a higher SAR in the head than the anatomically correct head models. Also the larger (adult) head produced a statistically significant higher peak SAR for both the 1- and 10-g averages than did the smaller (child) head for all conditions of frequency and position.

Computational Modeling of Open-Irrigated Electrodes for Radiofrequency Cardiac Ablation Including Blood Motion-Saline Flow Interaction

PubMed Central

González-Suárez, Ana; Berjano, Enrique; Guerra, Jose M.; Gerardo-Giorda, Luca

2016-01-01

Radiofrequency catheter ablation (RFCA) is a routine treatment for cardiac arrhythmias. During RFCA, the electrode-tissue interface temperature should be kept below 80°C to avoid thrombus formation. Open-irrigated electrodes facilitate power delivery while keeping low temperatures around the catheter. No computational model of an open-irrigated electrode in endocardial RFCA accounting for both the saline irrigation flow and the blood motion in the cardiac chamber has been proposed yet. We present the first computational model including both effects at once. The model has been validated against existing experimental results. Computational results showed that the surface lesion width and blood temperature are affected by both the electrode design and the irrigation flow rate. Smaller surface lesion widths and blood temperatures are obtained with higher irrigation flow rate, while the lesion depth is not affected by changing the irrigation flow rate. Larger lesions are obtained with increasing power and the electrode-tissue contact. Also, larger lesions are obtained when electrode is placed horizontally. Overall, the computational findings are in close agreement with previous experimental results providing an excellent tool for future catheter research. PMID:26938638

Hamiltonian Monte Carlo acceleration using surrogate functions with random bases.

PubMed

Zhang, Cheng; Shahbaba, Babak; Zhao, Hongkai

2017-11-01

For big data analysis, high computational cost for Bayesian methods often limits their applications in practice. In recent years, there have been many attempts to improve computational efficiency of Bayesian inference. Here we propose an efficient and scalable computational technique for a state-of-the-art Markov chain Monte Carlo methods, namely, Hamiltonian Monte Carlo. The key idea is to explore and exploit the structure and regularity in parameter space for the underlying probabilistic model to construct an effective approximation of its geometric properties. To this end, we build a surrogate function to approximate the target distribution using properly chosen random bases and an efficient optimization process. The resulting method provides a flexible, scalable, and efficient sampling algorithm, which converges to the correct target distribution. We show that by choosing the basis functions and optimization process differently, our method can be related to other approaches for the construction of surrogate functions such as generalized additive models or Gaussian process models. Experiments based on simulated and real data show that our approach leads to substantially more efficient sampling algorithms compared to existing state-of-the-art methods.

COMSOL in the Academic Environment at USNA

DTIC Science & Technology

2009-10-01

figure shows the electric field calculated and the right shows the electron density at one point in time. 3.3 Acoustic Detection of Landmines – 3...industries heavy investment in computer graphics and modeling. Packages such as Maya , Zbrush, Mudbox and others excel at this type of modeling. A...like Sketch-Up, Maya or AutoCAD. An extensive library of pre-built models would include all of the Platonic solids, combinations of Platonic

A Taylor Expansion-Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling

DOE PAGES

Mo, Shaoxing; Lu, Dan; Shi, Xiaoqing; ...

2017-12-27

Global sensitivity analysis (GSA) and uncertainty quantification (UQ) for groundwater modeling are challenging because of the model complexity and significant computational requirements. To reduce the massive computational cost, a cheap-to-evaluate surrogate model is usually constructed to approximate and replace the expensive groundwater models in the GSA and UQ. Constructing an accurate surrogate requires actual model simulations on a number of parameter samples. Thus, a robust experimental design strategy is desired to locate informative samples so as to reduce the computational cost in surrogate construction and consequently to improve the efficiency in the GSA and UQ. In this study, we developmore » a Taylor expansion-based adaptive design (TEAD) that aims to build an accurate global surrogate model with a small training sample size. TEAD defines a novel hybrid score function to search informative samples, and a robust stopping criterion to terminate the sample search that guarantees the resulted approximation errors satisfy the desired accuracy. The good performance of TEAD in building global surrogate models is demonstrated in seven analytical functions with different dimensionality and complexity in comparison to two widely used experimental design methods. The application of the TEAD-based surrogate method in two groundwater models shows that the TEAD design can effectively improve the computational efficiency of GSA and UQ for groundwater modeling.« less

A Taylor Expansion-Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling

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

Mo, Shaoxing; Lu, Dan; Shi, Xiaoqing

Global sensitivity analysis (GSA) and uncertainty quantification (UQ) for groundwater modeling are challenging because of the model complexity and significant computational requirements. To reduce the massive computational cost, a cheap-to-evaluate surrogate model is usually constructed to approximate and replace the expensive groundwater models in the GSA and UQ. Constructing an accurate surrogate requires actual model simulations on a number of parameter samples. Thus, a robust experimental design strategy is desired to locate informative samples so as to reduce the computational cost in surrogate construction and consequently to improve the efficiency in the GSA and UQ. In this study, we developmore » a Taylor expansion-based adaptive design (TEAD) that aims to build an accurate global surrogate model with a small training sample size. TEAD defines a novel hybrid score function to search informative samples, and a robust stopping criterion to terminate the sample search that guarantees the resulted approximation errors satisfy the desired accuracy. The good performance of TEAD in building global surrogate models is demonstrated in seven analytical functions with different dimensionality and complexity in comparison to two widely used experimental design methods. The application of the TEAD-based surrogate method in two groundwater models shows that the TEAD design can effectively improve the computational efficiency of GSA and UQ for groundwater modeling.« less

P-HS-SFM: a parallel harmony search algorithm for the reproduction of experimental data in the continuous microscopic crowd dynamic models

NASA Astrophysics Data System (ADS)

Jaber, Khalid Mohammad; Alia, Osama Moh'd.; Shuaib, Mohammed Mahmod

2018-03-01

Finding the optimal parameters that can reproduce experimental data (such as the velocity-density relation and the specific flow rate) is a very important component of the validation and calibration of microscopic crowd dynamic models. Heavy computational demand during parameter search is a known limitation that exists in a previously developed model known as the Harmony Search-Based Social Force Model (HS-SFM). In this paper, a parallel-based mechanism is proposed to reduce the computational time and memory resource utilisation required to find these parameters. More specifically, two MATLAB-based multicore techniques (parfor and create independent jobs) using shared memory are developed by taking advantage of the multithreading capabilities of parallel computing, resulting in a new framework called the Parallel Harmony Search-Based Social Force Model (P-HS-SFM). The experimental results show that the parfor-based P-HS-SFM achieved a better computational time of about 26 h, an efficiency improvement of ? 54% and a speedup factor of 2.196 times in comparison with the HS-SFM sequential processor. The performance of the P-HS-SFM using the create independent jobs approach is also comparable to parfor with a computational time of 26.8 h, an efficiency improvement of about 30% and a speedup of 2.137 times.

Stochastic hybrid systems for studying biochemical processes.

PubMed

Singh, Abhyudai; Hespanha, João P

2010-11-13

Many protein and mRNA species occur at low molecular counts within cells, and hence are subject to large stochastic fluctuations in copy numbers over time. Development of computationally tractable frameworks for modelling stochastic fluctuations in population counts is essential to understand how noise at the cellular level affects biological function and phenotype. We show that stochastic hybrid systems (SHSs) provide a convenient framework for modelling the time evolution of population counts of different chemical species involved in a set of biochemical reactions. We illustrate recently developed techniques that allow fast computations of the statistical moments of the population count, without having to run computationally expensive Monte Carlo simulations of the biochemical reactions. Finally, we review different examples from the literature that illustrate the benefits of using SHSs for modelling biochemical processes.

Deep learning with coherent nanophotonic circuits

NASA Astrophysics Data System (ADS)

Shen, Yichen; Harris, Nicholas C.; Skirlo, Scott; Prabhu, Mihika; Baehr-Jones, Tom; Hochberg, Michael; Sun, Xin; Zhao, Shijie; Larochelle, Hugo; Englund, Dirk; Soljačić, Marin

2017-07-01

Artificial neural networks are computational network models inspired by signal processing in the brain. These models have dramatically improved performance for many machine-learning tasks, including speech and image recognition. However, today's computing hardware is inefficient at implementing neural networks, in large part because much of it was designed for von Neumann computing schemes. Significant effort has been made towards developing electronic architectures tuned to implement artificial neural networks that exhibit improved computational speed and accuracy. Here, we propose a new architecture for a fully optical neural network that, in principle, could offer an enhancement in computational speed and power efficiency over state-of-the-art electronics for conventional inference tasks. We experimentally demonstrate the essential part of the concept using a programmable nanophotonic processor featuring a cascaded array of 56 programmable Mach-Zehnder interferometers in a silicon photonic integrated circuit and show its utility for vowel recognition.

Experimental quantum computing without entanglement.

PubMed

Lanyon, B P; Barbieri, M; Almeida, M P; White, A G

2008-11-14

Deterministic quantum computation with one pure qubit (DQC1) is an efficient model of computation that uses highly mixed states. Unlike pure-state models, its power is not derived from the generation of a large amount of entanglement. Instead it has been proposed that other nonclassical correlations are responsible for the computational speedup, and that these can be captured by the quantum discord. In this Letter we implement DQC1 in an all-optical architecture, and experimentally observe the generated correlations. We find no entanglement, but large amounts of quantum discord-except in three cases where an efficient classical simulation is always possible. Our results show that even fully separable, highly mixed, states can contain intrinsically quantum mechanical correlations and that these could offer a valuable resource for quantum information technologies.

Remote sensing image ship target detection method based on visual attention model

NASA Astrophysics Data System (ADS)

Sun, Yuejiao; Lei, Wuhu; Ren, Xiaodong

2017-11-01

The traditional methods of detecting ship targets in remote sensing images mostly use sliding window to search the whole image comprehensively. However, the target usually occupies only a small fraction of the image. This method has high computational complexity for large format visible image data. The bottom-up selective attention mechanism can selectively allocate computing resources according to visual stimuli, thus improving the computational efficiency and reducing the difficulty of analysis. Considering of that, a method of ship target detection in remote sensing images based on visual attention model was proposed in this paper. The experimental results show that the proposed method can reduce the computational complexity while improving the detection accuracy, and improve the detection efficiency of ship targets in remote sensing images.

Modeling biological problems in computer science: a case study in genome assembly.

PubMed

Medvedev, Paul

2018-01-30

As computer scientists working in bioinformatics/computational biology, we often face the challenge of coming up with an algorithm to answer a biological question. This occurs in many areas, such as variant calling, alignment and assembly. In this tutorial, we use the example of the genome assembly problem to demonstrate how to go from a question in the biological realm to a solution in the computer science realm. We show the modeling process step-by-step, including all the intermediate failed attempts. Please note this is not an introduction to how genome assembly algorithms work and, if treated as such, would be incomplete and unnecessarily long-winded. © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Augmented virtuality for arthroscopic knee surgery.

PubMed

Li, John M; Bardana, Davide D; Stewart, A James

2011-01-01

This paper describes a computer system to visualize the location and alignment of an arthroscope using augmented virtuality. A 3D computer model of the patient's joint (from CT) is shown, along with a model of the tracked arthroscopic probe and the projection of the camera image onto the virtual joint. A user study, using plastic bones instead of live patients, was made to determine the effectiveness of this navigated display; the study showed that the navigated display improves target localization in novice residents.

Pinatubo global cooling on target

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

Kerr, R.A.

1993-01-29

When Pinatubo blasted millions of tons of debris into the stratosphere in June 1991, Hansen of NASA's Goddard Institute for Space Studies used his computer climate model to predict that the shade cost by the debris would cool the globe by about half a degree C. Year end temperature reports for 1992 are now showing that the prediction was on target-confirming the tentative belief that volcanos can temporarily cool the climate and validating at least one component of the computer models predicting a greenhouse warming.

Model studies of laser absorption computed tomography for remote air pollution measurement

NASA Technical Reports Server (NTRS)

Wolfe, D. C., Jr.; Byer, R. L.

1982-01-01

Model studies of the potential of laser absorption-computed tomography are presented which demonstrate the possibility of sensitive remote atmospheric pollutant measurements, over kilometer-sized areas, with two-dimensional resolution, at modest laser source powers. An analysis of this tomographic reconstruction process as a function of measurement SNR, laser power, range, and system geometry, shows that the system is able to yield two-dimensional maps of pollutant concentrations at ranges and resolutions superior to those attainable with existing, direct-detection laser radars.

Pattern recognition with "materials that compute".

PubMed

Fang, Yan; Yashin, Victor V; Levitan, Steven P; Balazs, Anna C

2016-09-01

Driven by advances in materials and computer science, researchers are attempting to design systems where the computer and material are one and the same entity. Using theoretical and computational modeling, we design a hybrid material system that can autonomously transduce chemical, mechanical, and electrical energy to perform a computational task in a self-organized manner, without the need for external electrical power sources. Each unit in this system integrates a self-oscillating gel, which undergoes the Belousov-Zhabotinsky (BZ) reaction, with an overlaying piezoelectric (PZ) cantilever. The chemomechanical oscillations of the BZ gels deflect the PZ layer, which consequently generates a voltage across the material. When these BZ-PZ units are connected in series by electrical wires, the oscillations of these units become synchronized across the network, where the mode of synchronization depends on the polarity of the PZ. We show that the network of coupled, synchronizing BZ-PZ oscillators can perform pattern recognition. The "stored" patterns are set of polarities of the individual BZ-PZ units, and the "input" patterns are coded through the initial phase of the oscillations imposed on these units. The results of the modeling show that the input pattern closest to the stored pattern exhibits the fastest convergence time to stable synchronization behavior. In this way, networks of coupled BZ-PZ oscillators achieve pattern recognition. Further, we show that the convergence time to stable synchronization provides a robust measure of the degree of match between the input and stored patterns. Through these studies, we establish experimentally realizable design rules for creating "materials that compute."

Radiation-driven winds of hot stars. V - Wind models for central stars of planetary nebulae

NASA Technical Reports Server (NTRS)

Pauldrach, A.; Puls, J.; Kudritzki, R. P.; Mendez, R. H.; Heap, S. R.

1988-01-01

Wind models using the recent improvements of radiation driven wind theory by Pauldrach et al. (1986) and Pauldrach (1987) are presented for central stars of planetary nebulae. The models are computed along evolutionary tracks evolving with different stellar mass from the Asymptotic Giant Branch. We show that the calculated terminal wind velocities are in agreement with the observations and allow in principle an independent determination of stellar masses and radii. The computed mass-loss rates are in qualitative agreement with the occurrence of spectroscopic stellar wind features as a function of stellar effective temperature and gravity.

Treatment of atomic and molecular line blanketing by opacity sampling. [atmospheric optics - stellar atmospheres

NASA Technical Reports Server (NTRS)

Johnson, H. R.; Krupp, B. M.

1975-01-01

An opacity sampling (OS) technique for treating the radiative opacity of large numbers of atomic and molecular lines in cool stellar atmospheres is presented. Tests were conducted and results show that the structure of atmospheric models is accurately fixed by the use of 1000 frequency points, and 500 frequency points is often adequate. The effects of atomic and molecular lines are separately studied. A test model computed by using the OS method agrees very well with a model having identical atmospheric parameters computed by the giant line (opacity distribution function) method.

Modelling non-hydrostatic processes in sill regions

NASA Astrophysics Data System (ADS)

Souza, A.; Xing, J.; Davies, A.; Berntsen, J.

2007-12-01

We use a non-hydrostatic model to compute tidally induced flow and mixing in the region of bottom topography representing the sill at the entrance to Loch Etive (Scotland). This site is chosen since detailed measurements were recently made there. With non-hydrostatic dynamics in the model our results showed that the model could reproduce the observed flow characteristics, e.g., hydraulic transition, flow separation and internal waves. However, when calculations were performed using the model in the hydrostatic form, significant artificial convective mixing occurred. This influenced the computed temperature and flow field. We will discuss in detail the effects of non-hydrostatic dynamics on flow over the sill, especially investigate non-linear and non-hydrostatic contributions to modelled internal waves and internal wave energy fluxes.

Modern Computational Techniques for the HMMER Sequence Analysis

PubMed Central

2013-01-01

This paper focuses on the latest research and critical reviews on modern computing architectures, software and hardware accelerated algorithms for bioinformatics data analysis with an emphasis on one of the most important sequence analysis applications—hidden Markov models (HMM). We show the detailed performance comparison of sequence analysis tools on various computing platforms recently developed in the bioinformatics society. The characteristics of the sequence analysis, such as data and compute-intensive natures, make it very attractive to optimize and parallelize by using both traditional software approach and innovated hardware acceleration technologies. PMID:25937944

Computing quantum hashing in the model of quantum branching programs

NASA Astrophysics Data System (ADS)

Ablayev, Farid; Ablayev, Marat; Vasiliev, Alexander

2018-02-01

We investigate the branching program complexity of quantum hashing. We consider a quantum hash function that maps elements of a finite field into quantum states. We require that this function is preimage-resistant and collision-resistant. We consider two complexity measures for Quantum Branching Programs (QBP): a number of qubits and a number of compu-tational steps. We show that the quantum hash function can be computed efficiently. Moreover, we prove that such QBP construction is optimal. That is, we prove lower bounds that match the constructed quantum hash function computation.

Spectral stability of unitary network models

NASA Astrophysics Data System (ADS)

Asch, Joachim; Bourget, Olivier; Joye, Alain

2015-08-01

We review various unitary network models used in quantum computing, spectral analysis or condensed matter physics and establish relationships between them. We show that symmetric one-dimensional quantum walks are universal, as are CMV matrices. We prove spectral stability and propagation properties for general asymptotically uniform models by means of unitary Mourre theory.

Student Modeling and Ab Initio Language Learning.

ERIC Educational Resources Information Center

Heift, Trude; Schulze, Mathias

2003-01-01

Provides examples of student modeling techniques that have been employed in computer-assisted language learning over the past decade. Describes two systems for learning German: "German Tutor" and "Geroline." Shows how a student model can support computerized adaptive language testing for diagnostic purposes in a Web-based language learning…

Brian hears: online auditory processing using vectorization over channels.

PubMed

Fontaine, Bertrand; Goodman, Dan F M; Benichoux, Victor; Brette, Romain

2011-01-01

The human cochlea includes about 3000 inner hair cells which filter sounds at frequencies between 20 Hz and 20 kHz. This massively parallel frequency analysis is reflected in models of auditory processing, which are often based on banks of filters. However, existing implementations do not exploit this parallelism. Here we propose algorithms to simulate these models by vectorizing computation over frequency channels, which are implemented in "Brian Hears," a library for the spiking neural network simulator package "Brian." This approach allows us to use high-level programming languages such as Python, because with vectorized operations, the computational cost of interpretation represents a small fraction of the total cost. This makes it possible to define and simulate complex models in a simple way, while all previous implementations were model-specific. In addition, we show that these algorithms can be naturally parallelized using graphics processing units, yielding substantial speed improvements. We demonstrate these algorithms with several state-of-the-art cochlear models, and show that they compare favorably with existing, less flexible, implementations.

Deep Unsupervised Learning on a Desktop PC: A Primer for Cognitive Scientists.

PubMed

Testolin, Alberto; Stoianov, Ivilin; De Filippo De Grazia, Michele; Zorzi, Marco

2013-01-01

Deep belief networks hold great promise for the simulation of human cognition because they show how structured and abstract representations may emerge from probabilistic unsupervised learning. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. However, learning in deep networks typically requires big datasets and it can involve millions of connection weights, which implies that simulations on standard computers are unfeasible. Developing realistic, medium-to-large-scale learning models of cognition would therefore seem to require expertise in programing parallel-computing hardware, and this might explain why the use of this promising approach is still largely confined to the machine learning community. Here we show how simulations of deep unsupervised learning can be easily performed on a desktop PC by exploiting the processors of low cost graphic cards (graphic processor units) without any specific programing effort, thanks to the use of high-level programming routines (available in MATLAB or Python). We also show that even an entry-level graphic card can outperform a small high-performance computing cluster in terms of learning time and with no loss of learning quality. We therefore conclude that graphic card implementations pave the way for a widespread use of deep learning among cognitive scientists for modeling cognition and behavior.

Deep Unsupervised Learning on a Desktop PC: A Primer for Cognitive Scientists

PubMed Central

Testolin, Alberto; Stoianov, Ivilin; De Filippo De Grazia, Michele; Zorzi, Marco

2013-01-01

Deep belief networks hold great promise for the simulation of human cognition because they show how structured and abstract representations may emerge from probabilistic unsupervised learning. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. However, learning in deep networks typically requires big datasets and it can involve millions of connection weights, which implies that simulations on standard computers are unfeasible. Developing realistic, medium-to-large-scale learning models of cognition would therefore seem to require expertise in programing parallel-computing hardware, and this might explain why the use of this promising approach is still largely confined to the machine learning community. Here we show how simulations of deep unsupervised learning can be easily performed on a desktop PC by exploiting the processors of low cost graphic cards (graphic processor units) without any specific programing effort, thanks to the use of high-level programming routines (available in MATLAB or Python). We also show that even an entry-level graphic card can outperform a small high-performance computing cluster in terms of learning time and with no loss of learning quality. We therefore conclude that graphic card implementations pave the way for a widespread use of deep learning among cognitive scientists for modeling cognition and behavior. PMID:23653617

A Model-Based Approach to Trial-By-Trial P300 Amplitude Fluctuations

PubMed Central

Kolossa, Antonio; Fingscheidt, Tim; Wessel, Karl; Kopp, Bruno

2013-01-01

It has long been recognized that the amplitude of the P300 component of event-related brain potentials is sensitive to the degree to which eliciting stimuli are surprising to the observers (Donchin, 1981). While Squires et al. (1976) showed and modeled dependencies of P300 amplitudes from observed stimuli on various time scales, Mars et al. (2008) proposed a computational model keeping track of stimulus probabilities on a long-term time scale. We suggest here a computational model which integrates prior information with short-term, long-term, and alternation-based experiential influences on P300 amplitude fluctuations. To evaluate the new model, we measured trial-by-trial P300 amplitude fluctuations in a simple two-choice response time task, and tested the computational models of trial-by-trial P300 amplitudes using Bayesian model evaluation. The results reveal that the new digital filtering (DIF) model provides a superior account of the trial-by-trial P300 amplitudes when compared to both Squires et al.’s (1976) model, and Mars et al.’s (2008) model. We show that the P300-generating system can be described as two parallel first-order infinite impulse response (IIR) low-pass filters and an additional fourth-order finite impulse response (FIR) high-pass filter. Implications of the acquired data are discussed with regard to the neurobiological distinction between short-term, long-term, and working memory as well as from the point of view of predictive coding models and Bayesian learning theories of cortical function. PMID:23404628

Determination of the geophysical model function of NSCAT and its corresponding variance by the use of neural networks

NASA Astrophysics Data System (ADS)

Mejia, C.; Badran, F.; Bentamy, A.; Crepon, M.; Thiria, S.; Tran, N.

1999-05-01

We have computed two geophysical model functions (one for the vertical and one for the horizontal polarization) for the NASA scatterometer (NSCAT) by using neural networks. These neural network geophysical model functions (NNGMFs) were estimated with NSCAT scatterometer σO measurements collocated with European Centre for Medium-Range Weather Forecasts analyzed wind vectors during the period January 15 to April 15, 1997. We performed a student t test showing that the NNGMFs estimate the NSCAT σO with a confidence level of 95%. Analysis of the results shows that the mean NSCAT signal depends on the incidence angle and the wind speed and presents the classical biharmonic modulation with respect to the wind azimuth. NSCAT σO increases with respect to the wind speed and presents a well-marked change at around 7 m s-1. The upwind-downwind amplitude is higher for the horizontal polarization signal than for vertical polarization, indicating that the use of horizontal polarization can give additional information for wind retrieval. Comparison of the σO computed by the NNGMFs against the NSCAT-measured σO show a quite low rms, except at low wind speeds. We also computed two specific neural networks for estimating the variance associated to these GMFs. The variances are analyzed with respect to geophysical parameters. This led us to compute the geophysical signal-to-noise ratio, i.e., Kp. The Kp values are quite high at low wind speed and decrease at high wind speed. At constant wind speed the highest Kp are at crosswind directions, showing that the crosswind values are the most difficult to estimate. These neural networks can be expressed as analytical functions, and FORTRAN subroutines can be provided.

Finite volume model for two-dimensional shallow environmental flow

USGS Publications Warehouse

Simoes, F.J.M.

2011-01-01

This paper presents the development of a two-dimensional, depth integrated, unsteady, free-surface model based on the shallow water equations. The development was motivated by the desire of balancing computational efficiency and accuracy by selective and conjunctive use of different numerical techniques. The base framework of the discrete model uses Godunov methods on unstructured triangular grids, but the solution technique emphasizes the use of a high-resolution Riemann solver where needed, switching to a simpler and computationally more efficient upwind finite volume technique in the smooth regions of the flow. Explicit time marching is accomplished with strong stability preserving Runge-Kutta methods, with additional acceleration techniques for steady-state computations. A simplified mass-preserving algorithm is used to deal with wet/dry fronts. Application of the model is made to several benchmark cases that show the interplay of the diverse solution techniques.

The Layer-Oriented Approach to Declarative Languages for Biological Modeling

PubMed Central

Raikov, Ivan; De Schutter, Erik

2012-01-01

We present a new approach to modeling languages for computational biology, which we call the layer-oriented approach. The approach stems from the observation that many diverse biological phenomena are described using a small set of mathematical formalisms (e.g. differential equations), while at the same time different domains and subdomains of computational biology require that models are structured according to the accepted terminology and classification of that domain. Our approach uses distinct semantic layers to represent the domain-specific biological concepts and the underlying mathematical formalisms. Additional functionality can be transparently added to the language by adding more layers. This approach is specifically concerned with declarative languages, and throughout the paper we note some of the limitations inherent to declarative approaches. The layer-oriented approach is a way to specify explicitly how high-level biological modeling concepts are mapped to a computational representation, while abstracting away details of particular programming languages and simulation environments. To illustrate this process, we define an example language for describing models of ionic currents, and use a general mathematical notation for semantic transformations to show how to generate model simulation code for various simulation environments. We use the example language to describe a Purkinje neuron model and demonstrate how the layer-oriented approach can be used for solving several practical issues of computational neuroscience model development. We discuss the advantages and limitations of the approach in comparison with other modeling language efforts in the domain of computational biology and outline some principles for extensible, flexible modeling language design. We conclude by describing in detail the semantic transformations defined for our language. PMID:22615554

The layer-oriented approach to declarative languages for biological modeling.

PubMed

Raikov, Ivan; De Schutter, Erik

2012-01-01

We present a new approach to modeling languages for computational biology, which we call the layer-oriented approach. The approach stems from the observation that many diverse biological phenomena are described using a small set of mathematical formalisms (e.g. differential equations), while at the same time different domains and subdomains of computational biology require that models are structured according to the accepted terminology and classification of that domain. Our approach uses distinct semantic layers to represent the domain-specific biological concepts and the underlying mathematical formalisms. Additional functionality can be transparently added to the language by adding more layers. This approach is specifically concerned with declarative languages, and throughout the paper we note some of the limitations inherent to declarative approaches. The layer-oriented approach is a way to specify explicitly how high-level biological modeling concepts are mapped to a computational representation, while abstracting away details of particular programming languages and simulation environments. To illustrate this process, we define an example language for describing models of ionic currents, and use a general mathematical notation for semantic transformations to show how to generate model simulation code for various simulation environments. We use the example language to describe a Purkinje neuron model and demonstrate how the layer-oriented approach can be used for solving several practical issues of computational neuroscience model development. We discuss the advantages and limitations of the approach in comparison with other modeling language efforts in the domain of computational biology and outline some principles for extensible, flexible modeling language design. We conclude by describing in detail the semantic transformations defined for our language.

Nonlinear state-space modelling of the kinematics of an oscillating circular cylinder in a fluid flow

NASA Astrophysics Data System (ADS)

Decuyper, J.; De Troyer, T.; Runacres, M. C.; Tiels, K.; Schoukens, J.

2018-01-01

The flow-induced vibration of bluff bodies is an important problem of many marine, civil, or mechanical engineers. In the design phase of such structures, it is vital to obtain good predictions of the fluid forces acting on the structure. Current methods rely on computational fluid dynamic simulations (CFD), with a too high computational cost to be effectively used in the design phase or for control applications. Alternative methods use heuristic mathematical models of the fluid forces, but these lack the accuracy (they often assume the system to be linear) or flexibility to be useful over a wide operating range. In this work we show that it is possible to build an accurate, flexible and low-computational-cost mathematical model using nonlinear system identification techniques. This model is data driven: it is trained over a user-defined region of interest using data obtained from experiments or simulations, or both. Here we use a Van der Pol oscillator as well as CFD simulations of an oscillating circular cylinder to generate the training data. Then a discrete-time polynomial nonlinear state-space model is fit to the data. This model relates the oscillation of the cylinder to the force that the fluid exerts on the cylinder. The model is finally validated over a wide range of oscillation frequencies and amplitudes, both inside and outside the so-called lock-in region. We show that forces simulated by the model are in good agreement with the data obtained from CFD.

Nonlinear stability of traffic models and the use of Lyapunov vectors for estimating the traffic state

NASA Astrophysics Data System (ADS)

Palatella, Luigi; Trevisan, Anna; Rambaldi, Sandro

2013-08-01

Valuable information for estimating the traffic flow is obtained with current GPS technology by monitoring position and velocity of vehicles. In this paper, we present a proof of concept study that shows how the traffic state can be estimated using only partial and noisy data by assimilating them in a dynamical model. Our approach is based on a data assimilation algorithm, developed by the authors for chaotic geophysical models, designed to be equivalent but computationally much less demanding than the traditional extended Kalman filter. Here we show that the algorithm is even more efficient if the system is not chaotic and demonstrate by numerical experiments that an accurate reconstruction of the complete traffic state can be obtained at a very low computational cost by monitoring only a small percentage of vehicles.

Acetylcholine-modulated plasticity in reward-driven navigation: a computational study.

PubMed

Zannone, Sara; Brzosko, Zuzanna; Paulsen, Ole; Clopath, Claudia

2018-06-21

Neuromodulation plays a fundamental role in the acquisition of new behaviours. In previous experimental work, we showed that acetylcholine biases hippocampal synaptic plasticity towards depression, and the subsequent application of dopamine can retroactively convert depression into potentiation. We also demonstrated that incorporating this sequentially neuromodulated Spike-Timing-Dependent Plasticity (STDP) rule in a network model of navigation yields effective learning of changing reward locations. Here, we employ computational modelling to further characterize the effects of cholinergic depression on behaviour. We find that acetylcholine, by allowing learning from negative outcomes, enhances exploration over the action space. We show that this results in a variety of effects, depending on the structure of the model, the environment and the task. Interestingly, sequentially neuromodulated STDP also yields flexible learning, surpassing the performance of other reward-modulated plasticity rules.

A jellium model of a catalyst particle in carbon nanotube growth

NASA Astrophysics Data System (ADS)

Artyukhov, Vasilii I.; Liu, Mingjie; Penev, Evgeni S.; Yakobson, Boris I.

2017-06-01

We show how a jellium model can represent a catalyst particle within the density-functional theory based approaches to the growth mechanism of carbon nanotubes (CNTs). The advantage of jellium is an abridged, less computationally taxing description of the multi-atom metal particle, while at the same time in avoiding the uncertainty of selecting a particular atomic geometry of either a solid or ever-changing liquid catalyst particle. A careful choice of jellium sphere size and its electron density as a descriptive parameter allows one to calculate the CNT-metal interface energies close to explicit full atomistic models. Further, we show that using jellium permits computing and comparing the formation of topological defects (sole pentagons or heptagons, the culprits of growth termination) as well as pentagon-heptagon pairs 5|7 (known as chirality-switching dislocation).

A Computational Approach to Qualitative Analysis in Large Textual Datasets

PubMed Central

Evans, Michael S.

2014-01-01

In this paper I introduce computational techniques to extend qualitative analysis into the study of large textual datasets. I demonstrate these techniques by using probabilistic topic modeling to analyze a broad sample of 14,952 documents published in major American newspapers from 1980 through 2012. I show how computational data mining techniques can identify and evaluate the significance of qualitatively distinct subjects of discussion across a wide range of public discourse. I also show how examining large textual datasets with computational methods can overcome methodological limitations of conventional qualitative methods, such as how to measure the impact of particular cases on broader discourse, how to validate substantive inferences from small samples of textual data, and how to determine if identified cases are part of a consistent temporal pattern. PMID:24498398

Lattice Boltzmann model capable of mesoscopic vorticity computation

NASA Astrophysics Data System (ADS)

Peng, Cheng; Guo, Zhaoli; Wang, Lian-Ping

2017-11-01

It is well known that standard lattice Boltzmann (LB) models allow the strain-rate components to be computed mesoscopically (i.e., through the local particle distributions) and as such possess a second-order accuracy in strain rate. This is one of the appealing features of the lattice Boltzmann method (LBM) which is of only second-order accuracy in hydrodynamic velocity itself. However, no known LB model can provide the same quality for vorticity and pressure gradients. In this paper, we design a multiple-relaxation time LB model on a three-dimensional 27-discrete-velocity (D3Q27) lattice. A detailed Chapman-Enskog analysis is presented to illustrate all the necessary constraints in reproducing the isothermal Navier-Stokes equations. The remaining degrees of freedom are carefully analyzed to derive a model that accommodates mesoscopic computation of all the velocity and pressure gradients from the nonequilibrium moments. This way of vorticity calculation naturally ensures a second-order accuracy, which is also proven through an asymptotic analysis. We thus show, with enough degrees of freedom and appropriate modifications, the mesoscopic vorticity computation can be achieved in LBM. The resulting model is then validated in simulations of a three-dimensional decaying Taylor-Green flow, a lid-driven cavity flow, and a uniform flow passing a fixed sphere. Furthermore, it is shown that the mesoscopic vorticity computation can be realized even with single relaxation parameter.

Accelerating Climate and Weather Simulations through Hybrid Computing

NASA Technical Reports Server (NTRS)

Zhou, Shujia; Cruz, Carlos; Duffy, Daniel; Tucker, Robert; Purcell, Mark

2011-01-01

Unconventional multi- and many-core processors (e.g. IBM (R) Cell B.E.(TM) and NVIDIA (R) GPU) have emerged as effective accelerators in trial climate and weather simulations. Yet these climate and weather models typically run on parallel computers with conventional processors (e.g. Intel, AMD, and IBM) using Message Passing Interface. To address challenges involved in efficiently and easily connecting accelerators to parallel computers, we investigated using IBM's Dynamic Application Virtualization (TM) (IBM DAV) software in a prototype hybrid computing system with representative climate and weather model components. The hybrid system comprises two Intel blades and two IBM QS22 Cell B.E. blades, connected with both InfiniBand(R) (IB) and 1-Gigabit Ethernet. The system significantly accelerates a solar radiation model component by offloading compute-intensive calculations to the Cell blades. Systematic tests show that IBM DAV can seamlessly offload compute-intensive calculations from Intel blades to Cell B.E. blades in a scalable, load-balanced manner. However, noticeable communication overhead was observed, mainly due to IP over the IB protocol. Full utilization of IB Sockets Direct Protocol and the lower latency production version of IBM DAV will reduce this overhead.

Experimental Identification of Non-Abelian Topological Orders on a Quantum Simulator.

PubMed

Li, Keren; Wan, Yidun; Hung, Ling-Yan; Lan, Tian; Long, Guilu; Lu, Dawei; Zeng, Bei; Laflamme, Raymond

2017-02-24

Topological orders can be used as media for topological quantum computing-a promising quantum computation model due to its invulnerability against local errors. Conversely, a quantum simulator, often regarded as a quantum computing device for special purposes, also offers a way of characterizing topological orders. Here, we show how to identify distinct topological orders via measuring their modular S and T matrices. In particular, we employ a nuclear magnetic resonance quantum simulator to study the properties of three topologically ordered matter phases described by the string-net model with two string types, including the Z_{2} toric code, doubled semion, and doubled Fibonacci. The third one, non-Abelian Fibonacci order is notably expected to be the simplest candidate for universal topological quantum computing. Our experiment serves as the basic module, built on which one can simulate braiding of non-Abelian anyons and ultimately, topological quantum computation via the braiding, and thus provides a new approach of investigating topological orders using quantum computers.

Imprecise results: Utilizing partial computations in real-time systems

NASA Technical Reports Server (NTRS)

Lin, Kwei-Jay; Natarajan, Swaminathan; Liu, Jane W.-S.

1987-01-01

In real-time systems, a computation may not have time to complete its execution because of deadline requirements. In such cases, no result except the approximate results produced by the computations up to that point will be available. It is desirable to utilize these imprecise results if possible. Two approaches are proposed to enable computations to return imprecise results when executions cannot be completed normally. The milestone approach records results periodically, and if a deadline is reached, returns the last recorded result. The sieve approach demarcates sections of code which can be skipped if the time available is insufficient. By using these approaches, the system is able to produce imprecise results when deadlines are reached. The design of the Concord project is described which supports imprecise computations using these techniques. Also presented is a general model of imprecise computations using these techniques, as well as one which takes into account the influence of the environment, showing where the latter approach fits into this model.

Internet messenger based smart virtual class learning using ubiquitous computing

NASA Astrophysics Data System (ADS)

Umam, K.; Mardi, S. N. S.; Hariadi, M.

2017-06-01

Internet messenger (IM) has become an important educational technology component in college education, IM makes it possible for students to engage in learning and collaborating at smart virtual class learning (SVCL) using ubiquitous computing. However, the model of IM-based smart virtual class learning using ubiquitous computing and empirical evidence that would favor a broad application to improve engagement and behavior are still limited. In addition, the expectation that IM based SVCL using ubiquitous computing could improve engagement and behavior on smart class cannot be confirmed because the majority of the reviewed studies followed instructions paradigms. This article aims to present the model of IM-based SVCL using ubiquitous computing and showing learners’ experiences in improved engagement and behavior for learner-learner and learner-lecturer interactions. The method applied in this paper includes design process and quantitative analysis techniques, with the purpose of identifying scenarios of ubiquitous computing and realize the impressions of learners and lecturers about engagement and behavior aspect and its contribution to learning

Fuel Burn Estimation Using Real Track Data

NASA Technical Reports Server (NTRS)

Chatterji, Gano B.

2011-01-01

A procedure for estimating fuel burned based on actual flight track data, and drag and fuel-flow models is described. The procedure consists of estimating aircraft and wind states, lift, drag and thrust. Fuel-flow for jet aircraft is determined in terms of thrust, true airspeed and altitude as prescribed by the Base of Aircraft Data fuel-flow model. This paper provides a theoretical foundation for computing fuel-flow with most of the information derived from actual flight data. The procedure does not require an explicit model of thrust and calibrated airspeed/Mach profile which are typically needed for trajectory synthesis. To validate the fuel computation method, flight test data provided by the Federal Aviation Administration were processed. Results from this method show that fuel consumed can be estimated within 1% of the actual fuel consumed in the flight test. Next, fuel consumption was estimated with simplified lift and thrust models. Results show negligible difference with respect to the full model without simplifications. An iterative takeoff weight estimation procedure is described for estimating fuel consumption, when takeoff weight is unavailable, and for establishing fuel consumption uncertainty bounds. Finally, the suitability of using radar-based position information for fuel estimation is examined. It is shown that fuel usage could be estimated within 5.4% of the actual value using positions reported in the Airline Situation Display to Industry data with simplified models and iterative takeoff weight computation.

Kaiser Permanente/Sandia National health care model. Phase I prototype final report. Part 1 - model overview

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

Edwards, D.; Yoshimura, A.; Butler, D.

1996-11-01

This report describes the results of a Cooperative Research and Development Agreement between Sandia National Laboratories and Kaiser Permanente Southern California to develop a prototype computer model of Kaiser Permanente`s health care delivery system. As a discrete event simulation, SimHCO models for each of 100,000 patients the progression of disease, individual resource usage, and patient choices in a competitive environment. SimHCO is implemented in the object-oriented programming language C++, stressing reusable knowledge and reusable software components. The versioned implementation of SimHCO showed that the object-oriented framework allows the program to grow in complexity in an incremental way. Furthermore, timing calculationsmore » showed that SimHCO runs in a reasonable time on typical workstations, and that a second phase model will scale proportionally and run within the system constraints of contemporary computer technology. This report is published as two documents: Model Overview and Domain Analysis. A separate Kaiser-proprietary report contains the Disease and Health Care Organization Selection Models.« less

Multifidelity, Multidisciplinary Design Under Uncertainty with Non-Intrusive Polynomial Chaos

NASA Technical Reports Server (NTRS)

West, Thomas K., IV; Gumbert, Clyde

2017-01-01

The primary objective of this work is to develop an approach for multifidelity uncertainty quantification and to lay the framework for future design under uncertainty efforts. In this study, multifidelity is used to describe both the fidelity of the modeling of the physical systems, as well as the difference in the uncertainty in each of the models. For computational efficiency, a multifidelity surrogate modeling approach based on non-intrusive polynomial chaos using the point-collocation technique is developed for the treatment of both multifidelity modeling and multifidelity uncertainty modeling. Two stochastic model problems are used to demonstrate the developed methodologies: a transonic airfoil model and multidisciplinary aircraft analysis model. The results of both showed the multifidelity modeling approach was able to predict the output uncertainty predicted by the high-fidelity model as a significant reduction in computational cost.

Computational Study of Axisymmetric Off-Design Nozzle Flows

NASA Technical Reports Server (NTRS)

DalBello, Teryn; Georgiadis, Nicholas; Yoder, Dennis; Keith, Theo

2003-01-01

Computational Fluid Dynamics (CFD) analyses of axisymmetric circular-arc boattail nozzles operating off-design at transonic Mach numbers have been completed. These computations span the very difficult transonic flight regime with shock-induced separations and strong adverse pressure gradients. External afterbody and internal nozzle pressure distributions computed with the Wind code are compared with experimental data. A range of turbulence models were examined, including the Explicit Algebraic Stress model. Computations have been completed at freestream Mach numbers of 0.9 and 1.2, and nozzle pressure ratios (NPR) of 4 and 6. Calculations completed with variable time-stepping (steady-state) did not converge to a true steady-state solution. Calculations obtained using constant timestepping (timeaccurate) indicate less variations in flow properties compared with steady-state solutions. This failure to converge to a steady-state solution was the result of using variable time-stepping with large-scale separations present in the flow. Nevertheless, time-averaged boattail surface pressure coefficient and internal nozzle pressures show reasonable agreement with experimental data. The SST turbulence model demonstrates the best overall agreement with experimental data.

Computations of turbulent lean premixed combustion using conditional moment closure

NASA Astrophysics Data System (ADS)

Amzin, Shokri; Swaminathan, Nedunchezhian

2013-12-01

Conditional Moment Closure (CMC) is a suitable method for predicting scalars such as carbon monoxide with slow chemical time scales in turbulent combustion. Although this method has been successfully applied to non-premixed combustion, its application to lean premixed combustion is rare. In this study the CMC method is used to compute piloted lean premixed combustion in a distributed combustion regime. The conditional scalar dissipation rate of the conditioning scalar, the progress variable, is closed using an algebraic model and turbulence is modelled using the standard k-ɛ model. The conditional mean reaction rate is closed using a first order CMC closure with the GRI-3.0 chemical mechanism to represent the chemical kinetics of methane oxidation. The PDF of the progress variable is obtained using a presumed shape with the Beta function. The computed results are compared with the experimental measurements and earlier computations using the transported PDF approach. The results show reasonable agreement with the experimental measurements and are consistent with the transported PDF computations. When the compounded effects of shear-turbulence and flame are strong, second order closures may be required for the CMC.

Multiscale Modeling of Ultra High Temperature Ceramics (UHTC) ZrB2 and HfB2: Application to Lattice Thermal Conductivity

NASA Technical Reports Server (NTRS)

Lawson, John W.; Daw, Murray S.; Squire, Thomas H.; Bauschlicher, Charles W.

2012-01-01

We are developing a multiscale framework in computational modeling for the ultra high temperature ceramics (UHTC) ZrB2 and HfB2. These materials are characterized by high melting point, good strength, and reasonable oxidation resistance. They are candidate materials for a number of applications in extreme environments including sharp leading edges of hypersonic aircraft. In particular, we used a combination of ab initio methods, atomistic simulations and continuum computations to obtain insights into fundamental properties of these materials. Ab initio methods were used to compute basic structural, mechanical and thermal properties. From these results, a database was constructed to fit a Tersoff style interatomic potential suitable for atomistic simulations. These potentials were used to evaluate the lattice thermal conductivity of single crystals and the thermal resistance of simple grain boundaries. Finite element method (FEM) computations using atomistic results as inputs were performed with meshes constructed on SEM images thereby modeling the realistic microstructure. These continuum computations showed the reduction in thermal conductivity due to the grain boundary network.

Computational Grounded Cognition: a new alliance between grounded cognition and computational modeling

PubMed Central

Pezzulo, Giovanni; Barsalou, Lawrence W.; Cangelosi, Angelo; Fischer, Martin H.; McRae, Ken; Spivey, Michael J.

2013-01-01

Grounded theories assume that there is no central module for cognition. According to this view, all cognitive phenomena, including those considered the province of amodal cognition such as reasoning, numeric, and language processing, are ultimately grounded in (and emerge from) a variety of bodily, affective, perceptual, and motor processes. The development and expression of cognition is constrained by the embodiment of cognitive agents and various contextual factors (physical and social) in which they are immersed. The grounded framework has received numerous empirical confirmations. Still, there are very few explicit computational models that implement grounding in sensory, motor and affective processes as intrinsic to cognition, and demonstrate that grounded theories can mechanistically implement higher cognitive abilities. We propose a new alliance between grounded cognition and computational modeling toward a novel multidisciplinary enterprise: Computational Grounded Cognition. We clarify the defining features of this novel approach and emphasize the importance of using the methodology of Cognitive Robotics, which permits simultaneous consideration of multiple aspects of grounding, embodiment, and situatedness, showing how they constrain the development and expression of cognition. PMID:23346065

Constructing Precisely Computing Networks with Biophysical Spiking Neurons.

PubMed

Schwemmer, Michael A; Fairhall, Adrienne L; Denéve, Sophie; Shea-Brown, Eric T

2015-07-15

While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates. In equivalent spiking implementations, firing is assumed to be random such that averaging across populations of neurons recovers the rate-based approach. Recently, however, Denéve and colleagues have suggested that the spiking behavior of neurons may be fundamental to how neuronal networks compute, with precise spike timing determined by each neuron's contribution to producing the desired output (Boerlin and Denéve, 2011; Boerlin et al., 2013). By postulating that each neuron fires to reduce the error in the network's output, it was demonstrated that linear computations can be performed by networks of integrate-and-fire neurons that communicate through instantaneous synapses. This left open, however, the possibility that realistic networks, with conductance-based neurons with subthreshold nonlinearity and the slower timescales of biophysical synapses, may not fit into this framework. Here, we show how the spike-based approach can be extended to biophysically plausible networks. We then show that our network reproduces a number of key features of cortical networks including irregular and Poisson-like spike times and a tight balance between excitation and inhibition. Lastly, we discuss how the behavior of our model scales with network size or with the number of neurons "recorded" from a larger computing network. These results significantly increase the biological plausibility of the spike-based approach to network computation. We derive a network of neurons with standard spike-generating currents and synapses with realistic timescales that computes based upon the principle that the precise timing of each spike is important for the computation. We then show that our network reproduces a number of key features of cortical networks including irregular, Poisson-like spike times, and a tight balance between excitation and inhibition. These results significantly increase the biological plausibility of the spike-based approach to network computation, and uncover how several components of biological networks may work together to efficiently carry out computation. Copyright © 2015 the authors 0270-6474/15/3510112-23$15.00/0.

The advanced role of computational mechanics and visualization in science and technology: analysis of the Germanwings Flight 9525 crash

NASA Astrophysics Data System (ADS)

Chen, Goong; Wang, Yi-Ching; Perronnet, Alain; Gu, Cong; Yao, Pengfei; Bin-Mohsin, Bandar; Hajaiej, Hichem; Scully, Marlan O.

2017-03-01

Computational mathematics, physics and engineering form a major constituent of modern computational science, which now stands on an equal footing with the established branches of theoretical and experimental sciences. Computational mechanics solves problems in science and engineering based upon mathematical modeling and computing, bypassing the need for expensive and time-consuming laboratory setups and experimental measurements. Furthermore, it allows the numerical simulations of large scale systems, such as the formation of galaxies that could not be done in any earth bound laboratories. This article is written as part of the 21st Century Frontiers Series to illustrate some state-of-the-art computational science. We emphasize how to do numerical modeling and visualization in the study of a contemporary event, the pulverizing crash of the Germanwings Flight 9525 on March 24, 2015, as a showcase. Such numerical modeling and the ensuing simulation of aircraft crashes into land or mountain are complex tasks as they involve both theoretical study and supercomputing of a complex physical system. The most tragic type of crash involves ‘pulverization’ such as the one suffered by this Germanwings flight. Here, we show pulverizing airliner crashes by visualization through video animations from supercomputer applications of the numerical modeling tool LS-DYNA. A sound validation process is challenging but essential for any sophisticated calculations. We achieve this by validation against the experimental data from a crash test done in 1993 of an F4 Phantom II fighter jet into a wall. We have developed a method by hybridizing two primary methods: finite element analysis and smoothed particle hydrodynamics. This hybrid method also enhances visualization by showing a ‘debris cloud’. Based on our supercomputer simulations and the visualization, we point out that prior works on this topic based on ‘hollow interior’ modeling can be quite problematic and, thus, not likely to be correct. We discuss the effects of terrain on pulverization using the information from the recovered flight-data-recorder and show our forensics and assessments of what may have happened during the final moments of the crash. Finally, we point out that our study has potential for being made into real-time flight crash simulators to help the study of crashworthiness and survivability for future aviation safety. Some forward-looking statements are also made.

Analytical gradients for MP2, double hybrid functionals, and TD‐DFT with polarizable embedding described by fluctuating charges

PubMed Central

Carnimeo, Ivan; Cappelli, Chiara

2015-01-01

A polarizable quantum mechanics (QM)/ molecular mechanics (MM) approach recently developed for Hartree–Fock (HF) and Kohn–Sham (KS) methods has been extended to energies and analytical gradients for MP2, double hybrid functionals, and TD‐DFT models, thus allowing the computation of equilibrium structures for excited electronic states together with more accurate results for ground electronic states. After a detailed presentation of the theoretical background and of some implementation details, a number of test cases are analyzed to show that the polarizable embedding model based on fluctuating charges (FQ) is remarkably more accurate than the corresponding electronic embedding based on a fixed charge (FX) description. In particular, a set of electronegativities and hardnesses has been optimized for interactions between QM and FQ regions together with new repulsion–dispersion parameters. After validation of both the numerical implementation and of the new parameters, absorption electronic spectra have been computed for representative model systems including vibronic effects. The results show remarkable agreement with full QM computations and significant improvement with respect to the corresponding FX results. The last part of the article provides some hints about computation of solvatochromic effects on absorption spectra in aqueous solution as a function of the number of FQ water molecules and on the use of FX external shells to improve the convergence of the results. © 2015 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc. PMID:26399473

Analysis of the DFP/AFCS Systems for Compensating Gravity Distortions on the 70-Meter Antenna

NASA Technical Reports Server (NTRS)

Imbriale, William A.; Hoppe, Daniel J.; Rochblatt, David

2000-01-01

This paper presents the theoretical computations showing the expected performances for both systems. The basic analysis tool is a Physical Optics reflector analysis code that was ported to a parallel computer for faster execution times. There are several steps involved in computing the RF performance of the various systems. 1 . A model of the RF distortions of the main reflector is required. This model is based upon measured holography maps of the 70-meter antenna obtained at 3 elevation angles. The holography maps are then processed (using an appropriate gravity mechanical model of the dish) to provide surface distortion maps at all elevation angles. 2. From the surface distortion maps, ray optics is used to determine the theoretical shape of the DFP that will exactly phase compensate the distortions. 3. From the theoretical shape and a NASTRAN mechanical model of the plate, the actuator positions that generate a surface that provides the best RMS fit to the theoretical model are selected. Using the actuator positions and the NASTRAN model provides an accurate description of the actual mirror shape. 4. Starting from the mechanical drawings of the feed, a computed RF feed pattern is generated. This pattern is expanded into a set of spherical wave modes so that a complete near field analysis of the reflector system can be obtained. 5. For the array feed, the excitation coefficients that provide the maximum gain are computed using a phase conjugate technique. The basic experimental geometry consisted of a dual shaped 70-meter antenna system; a refocusing ellipse, a DFP and an array feed system. To provide physical insight to the systems performance, focal plane field plots are presented at several elevations. Curves of predicted performance are shown for the DFP system, monopulse tracking system, AFCS and combined DFP/AFCS system. The calculated results show that the combined DFP/AFCS system is capable of recovering the majority of the gain lost due to gravity distortion.

Architectures for Quantum Simulation Showing a Quantum Speedup

NASA Astrophysics Data System (ADS)

Bermejo-Vega, Juan; Hangleiter, Dominik; Schwarz, Martin; Raussendorf, Robert; Eisert, Jens

2018-04-01

One of the main aims in the field of quantum simulation is to achieve a quantum speedup, often referred to as "quantum computational supremacy," referring to the experimental realization of a quantum device that computationally outperforms classical computers. In this work, we show that one can devise versatile and feasible schemes of two-dimensional, dynamical, quantum simulators showing such a quantum speedup, building on intermediate problems involving nonadaptive, measurement-based, quantum computation. In each of the schemes, an initial product state is prepared, potentially involving an element of randomness as in disordered models, followed by a short-time evolution under a basic translationally invariant Hamiltonian with simple nearest-neighbor interactions and a mere sampling measurement in a fixed basis. The correctness of the final-state preparation in each scheme is fully efficiently certifiable. We discuss experimental necessities and possible physical architectures, inspired by platforms of cold atoms in optical lattices and a number of others, as well as specific assumptions that enter the complexity-theoretic arguments. This work shows that benchmark settings exhibiting a quantum speedup may require little control, in contrast to universal quantum computing. Thus, our proposal puts a convincing experimental demonstration of a quantum speedup within reach in the near term.

Optimization of computations for adjoint field and Jacobian needed in 3D CSEM inversion

NASA Astrophysics Data System (ADS)

Dehiya, Rahul; Singh, Arun; Gupta, Pravin K.; Israil, M.

2017-01-01

We present the features and results of a newly developed code, based on Gauss-Newton optimization technique, for solving three-dimensional Controlled-Source Electromagnetic inverse problem. In this code a special emphasis has been put on representing the operations by block matrices for conjugate gradient iteration. We show how in the computation of Jacobian, the matrix formed by differentiation of system matrix can be made independent of frequency to optimize the operations at conjugate gradient step. The coarse level parallel computing, using OpenMP framework, is used primarily due to its simplicity in implementation and accessibility of shared memory multi-core computing machine to almost anyone. We demonstrate how the coarseness of modeling grid in comparison to source (comp`utational receivers) spacing can be exploited for efficient computing, without compromising the quality of the inverted model, by reducing the number of adjoint calls. It is also demonstrated that the adjoint field can even be computed on a grid coarser than the modeling grid without affecting the inversion outcome. These observations were reconfirmed using an experiment design where the deviation of source from straight tow line is considered. Finally, a real field data inversion experiment is presented to demonstrate robustness of the code.

Diagnosing hypoxia in murine models of rheumatoid arthritis from reflectance multispectral images

NASA Astrophysics Data System (ADS)

Glinton, Sophie; Naylor, Amy J.; Claridge, Ela

2017-07-01

Spectra computed from multispectral images of murine models of Rheumatoid Arthritis show a characteristic decrease in reflectance within the 600-800nm region which is indicative of the reduction in blood oxygenation and is consistent with hypoxia.

First-Principles Molecular Dynamics Studies of Organometallic Complexes and Homogeneous Catalytic Processes.

PubMed

Vidossich, Pietro; Lledós, Agustí; Ujaque, Gregori

2016-06-21

Computational chemistry is a valuable aid to complement experimental studies of organometallic systems and their reactivity. It allows probing mechanistic hypotheses and investigating molecular structures, shedding light on the behavior and properties of molecular assemblies at the atomic scale. When approaching a chemical problem, the computational chemist has to decide on the theoretical approach needed to describe electron/nuclear interactions and the composition of the model used to approximate the actual system. Both factors determine the reliability of the modeling study. The community dedicated much effort to developing and improving the performance and accuracy of theoretical approaches for electronic structure calculations, on which the description of (inter)atomic interactions rely. Here, the importance of the model system used in computational studies is highlighted through examples from our recent research focused on organometallic systems and homogeneous catalytic processes. We show how the inclusion of explicit solvent allows the characterization of molecular events that would otherwise not be accessible in reduced model systems (clusters). These include the stabilization of nascent charged fragments via microscopic solvation (notably, hydrogen bonding), transfer of charge (protons) between distant fragments mediated by solvent molecules, and solvent coordination to unsaturated metal centers. Furthermore, when weak interactions are involved, we show how conformational and solvation properties of organometallic complexes are also affected by the explicit inclusion of solvent molecules. Such extended model systems may be treated under periodic boundary conditions, thus removing the cluster/continuum (or vacuum) boundary, and require a statistical mechanics simulation technique to sample the accessible configurational space. First-principles molecular dynamics, in which atomic forces are computed from electronic structure calculations (namely, density functional theory), is certainly the technique of choice to investigate chemical events in solution. This methodology is well established and thanks to advances in both algorithms and computational resources simulation times required for the modeling of chemical events are nowadays accessible, though the computational requirements use to be high. Specific applications reviewed here include mechanistic studies of the Shilov and Wacker processes, speciation in Pd chemistry, hydrogen bonding to metal centers, and the dynamics of agostic interactions.

Spring assisted cranioplasty: A patient specific computational model.

PubMed

Borghi, Alessandro; Rodriguez-Florez, Naiara; Rodgers, Will; James, Gregory; Hayward, Richard; Dunaway, David; Jeelani, Owase; Schievano, Silvia

2018-03-01

Implantation of spring-like distractors in the treatment of sagittal craniosynostosis is a novel technique that has proven functionally and aesthetically effective in correcting skull deformities; however, final shape outcomes remain moderately unpredictable due to an incomplete understanding of the skull-distractor interaction. The aim of this study was to create a patient specific computational model of spring assisted cranioplasty (SAC) that can help predict the individual overall final head shape. Pre-operative computed tomography images of a SAC patient were processed to extract a 3D model of the infant skull anatomy and simulate spring implantation. The distractors were modeled based on mechanical experimental data. Viscoelastic bone properties from the literature were tuned using the specific patient procedural information recorded during surgery and from x-ray measurements at follow-up. The model accurately captured spring expansion on-table (within 9% of the measured values), as well as at first and second follow-ups (within 8% of the measured values). Comparison between immediate post-operative 3D head scanning and numerical results for this patient proved that the model could successfully predict the final overall head shape. This preliminary work showed the potential application of computational modeling to study SAC, to support pre-operative planning and guide novel distractor design. Copyright © 2018 IPEM. Published by Elsevier Ltd. All rights reserved.

PconsD: ultra rapid, accurate model quality assessment for protein structure prediction.

PubMed

Skwark, Marcin J; Elofsson, Arne

2013-07-15

Clustering methods are often needed for accurately assessing the quality of modeled protein structures. Recent blind evaluation of quality assessment methods in CASP10 showed that there is little difference between many different methods as far as ranking models and selecting best model are concerned. When comparing many models, the computational cost of the model comparison can become significant. Here, we present PconsD, a fast, stream-computing method for distance-driven model quality assessment that runs on consumer hardware. PconsD is at least one order of magnitude faster than other methods of comparable accuracy. The source code for PconsD is freely available at http://d.pcons.net/. Supplementary benchmarking data are also available there. arne@bioinfo.se Supplementary data are available at Bioinformatics online.

LEMS: a language for expressing complex biological models in concise and hierarchical form and its use in underpinning NeuroML 2.

PubMed

Cannon, Robert C; Gleeson, Padraig; Crook, Sharon; Ganapathy, Gautham; Marin, Boris; Piasini, Eugenio; Silver, R Angus

2014-01-01

Computational models are increasingly important for studying complex neurophysiological systems. As scientific tools, it is essential that such models can be reproduced and critically evaluated by a range of scientists. However, published models are currently implemented using a diverse set of modeling approaches, simulation tools, and computer languages making them inaccessible and difficult to reproduce. Models also typically contain concepts that are tightly linked to domain-specific simulators, or depend on knowledge that is described exclusively in text-based documentation. To address these issues we have developed a compact, hierarchical, XML-based language called LEMS (Low Entropy Model Specification), that can define the structure and dynamics of a wide range of biological models in a fully machine readable format. We describe how LEMS underpins the latest version of NeuroML and show that this framework can define models of ion channels, synapses, neurons and networks. Unit handling, often a source of error when reusing models, is built into the core of the language by specifying physical quantities in models in terms of the base dimensions. We show how LEMS, together with the open source Java and Python based libraries we have developed, facilitates the generation of scripts for multiple neuronal simulators and provides a route for simulator free code generation. We establish that LEMS can be used to define models from systems biology and map them to neuroscience-domain specific simulators, enabling models to be shared between these traditionally separate disciplines. LEMS and NeuroML 2 provide a new, comprehensive framework for defining computational models of neuronal and other biological systems in a machine readable format, making them more reproducible and increasing the transparency and accessibility of their underlying structure and properties.

LEMS: a language for expressing complex biological models in concise and hierarchical form and its use in underpinning NeuroML 2

PubMed Central

Cannon, Robert C.; Gleeson, Padraig; Crook, Sharon; Ganapathy, Gautham; Marin, Boris; Piasini, Eugenio; Silver, R. Angus

2014-01-01

Computational models are increasingly important for studying complex neurophysiological systems. As scientific tools, it is essential that such models can be reproduced and critically evaluated by a range of scientists. However, published models are currently implemented using a diverse set of modeling approaches, simulation tools, and computer languages making them inaccessible and difficult to reproduce. Models also typically contain concepts that are tightly linked to domain-specific simulators, or depend on knowledge that is described exclusively in text-based documentation. To address these issues we have developed a compact, hierarchical, XML-based language called LEMS (Low Entropy Model Specification), that can define the structure and dynamics of a wide range of biological models in a fully machine readable format. We describe how LEMS underpins the latest version of NeuroML and show that this framework can define models of ion channels, synapses, neurons and networks. Unit handling, often a source of error when reusing models, is built into the core of the language by specifying physical quantities in models in terms of the base dimensions. We show how LEMS, together with the open source Java and Python based libraries we have developed, facilitates the generation of scripts for multiple neuronal simulators and provides a route for simulator free code generation. We establish that LEMS can be used to define models from systems biology and map them to neuroscience-domain specific simulators, enabling models to be shared between these traditionally separate disciplines. LEMS and NeuroML 2 provide a new, comprehensive framework for defining computational models of neuronal and other biological systems in a machine readable format, making them more reproducible and increasing the transparency and accessibility of their underlying structure and properties. PMID:25309419

A primer for biomedical scientists on how to execute model II linear regression analysis.

PubMed

Ludbrook, John

2012-04-01

1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.

Feedback Inhibition Shapes Emergent Computational Properties of Cortical Microcircuit Motifs.

PubMed

Jonke, Zeno; Legenstein, Robert; Habenschuss, Stefan; Maass, Wolfgang

2017-08-30

Cortical microcircuits are very complex networks, but they are composed of a relatively small number of stereotypical motifs. Hence, one strategy for throwing light on the computational function of cortical microcircuits is to analyze emergent computational properties of these stereotypical microcircuit motifs. We are addressing here the question how spike timing-dependent plasticity shapes the computational properties of one motif that has frequently been studied experimentally: interconnected populations of pyramidal cells and parvalbumin-positive inhibitory cells in layer 2/3. Experimental studies suggest that these inhibitory neurons exert some form of divisive inhibition on the pyramidal cells. We show that this data-based form of feedback inhibition, which is softer than that of winner-take-all models that are commonly considered in theoretical analyses, contributes to the emergence of an important computational function through spike timing-dependent plasticity: The capability to disentangle superimposed firing patterns in upstream networks, and to represent their information content through a sparse assembly code. SIGNIFICANCE STATEMENT We analyze emergent computational properties of a ubiquitous cortical microcircuit motif: populations of pyramidal cells that are densely interconnected with inhibitory neurons. Simulations of this model predict that sparse assembly codes emerge in this microcircuit motif under spike timing-dependent plasticity. Furthermore, we show that different assemblies will represent different hidden sources of upstream firing activity. Hence, we propose that spike timing-dependent plasticity enables this microcircuit motif to perform a fundamental computational operation on neural activity patterns. Copyright © 2017 the authors 0270-6474/17/378511-13$15.00/0.

A robust computational technique for model order reduction of two-time-scale discrete systems via genetic algorithms.

PubMed

Alsmadi, Othman M K; Abo-Hammour, Zaer S

2015-01-01

A robust computational technique for model order reduction (MOR) of multi-time-scale discrete systems (single input single output (SISO) and multi-input multioutput (MIMO)) is presented in this paper. This work is motivated by the singular perturbation of multi-time-scale systems where some specific dynamics may not have significant influence on the overall system behavior. The new approach is proposed using genetic algorithms (GA) with the advantage of obtaining a reduced order model, maintaining the exact dominant dynamics in the reduced order, and minimizing the steady state error. The reduction process is performed by obtaining an upper triangular transformed matrix of the system state matrix defined in state space representation along with the elements of B, C, and D matrices. The GA computational procedure is based on maximizing the fitness function corresponding to the response deviation between the full and reduced order models. The proposed computational intelligence MOR method is compared to recently published work on MOR techniques where simulation results show the potential and advantages of the new approach.

Data-driven train set crash dynamics simulation

NASA Astrophysics Data System (ADS)

Tang, Zhao; Zhu, Yunrui; Nie, Yinyu; Guo, Shihui; Liu, Fengjia; Chang, Jian; Zhang, Jianjun

2017-02-01

Traditional finite element (FE) methods are arguably expensive in computation/simulation of the train crash. High computational cost limits their direct applications in investigating dynamic behaviours of an entire train set for crashworthiness design and structural optimisation. On the contrary, multi-body modelling is widely used because of its low computational cost with the trade-off in accuracy. In this study, a data-driven train crash modelling method is proposed to improve the performance of a multi-body dynamics simulation of train set crash without increasing the computational burden. This is achieved by the parallel random forest algorithm, which is a machine learning approach that extracts useful patterns of force-displacement curves and predicts a force-displacement relation in a given collision condition from a collection of offline FE simulation data on various collision conditions, namely different crash velocities in our analysis. Using the FE simulation results as a benchmark, we compared our method with traditional multi-body modelling methods and the result shows that our data-driven method improves the accuracy over traditional multi-body models in train crash simulation and runs at the same level of efficiency.

Rapid prototyping raw models on the basis of high resolution computed tomography lung data for respiratory flow dynamics.

PubMed

Giesel, Frederik L; Mehndiratta, Amit; von Tengg-Kobligk, Hendrik; Schaeffer, A; Teh, Kevin; Hoffman, E A; Kauczor, Hans-Ulrich; van Beek, E J R; Wild, Jim M

2009-04-01

Three-dimensional image reconstruction by volume rendering and rapid prototyping has made it possible to visualize anatomic structures in three dimensions for interventional planning and academic research. Volumetric chest computed tomography was performed on a healthy volunteer. Computed tomographic images of the larger bronchial branches were segmented by an extended three-dimensional region-growing algorithm, converted into a stereolithography file, and used for computer-aided design on a laser sintering machine. The injection of gases for respiratory flow modeling and measurements using magnetic resonance imaging were done on a hollow cast. Manufacturing the rapid prototype took about 40 minutes and included the airway tree from trackea to segmental bronchi (fifth generation). The branching of the airways are clearly visible in the (3)He images, and the radial imaging has the potential to elucidate the airway dimensions. The results for flow patterns in the human bronchial tree using the rapid-prototype model with hyperpolarized helium-3 magnetic resonance imaging show the value of this model for flow phantom studies.

Modification of Hazen's equation in coarse grained soils by soft computing techniques

NASA Astrophysics Data System (ADS)

Kaynar, Oguz; Yilmaz, Isik; Marschalko, Marian; Bednarik, Martin; Fojtova, Lucie

2013-04-01

Hazen first proposed a Relationship between coefficient of permeability (k) and effective grain size (d10) was first proposed by Hazen, and it was then extended by some other researchers. However many attempts were done for estimation of k, correlation coefficients (R2) of the models were generally lower than ~0.80 and whole grain size distribution curves were not included in the assessments. Soft computing techniques such as; artificial neural networks, fuzzy inference systems, genetic algorithms, etc. and their hybrids are now being successfully used as an alternative tool. In this study, use of some soft computing techniques such as Artificial Neural Networks (ANNs) (MLP, RBF, etc.) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for prediction of permeability of coarse grained soils was described, and Hazen's equation was then modificated. It was found that the soft computing models exhibited high performance in prediction of permeability coefficient. However four different kinds of ANN algorithms showed similar prediction performance, results of MLP was found to be relatively more accurate than RBF models. The most reliable prediction was obtained from ANFIS model.

Risk in the Clouds?: Security Issues Facing Government Use of Cloud Computing

NASA Astrophysics Data System (ADS)

Wyld, David C.

Cloud computing is poised to become one of the most important and fundamental shifts in how computing is consumed and used. Forecasts show that government will play a lead role in adopting cloud computing - for data storage, applications, and processing power, as IT executives seek to maximize their returns on limited procurement budgets in these challenging economic times. After an overview of the cloud computing concept, this article explores the security issues facing public sector use of cloud computing and looks to the risk and benefits of shifting to cloud-based models. It concludes with an analysis of the challenges that lie ahead for government use of cloud resources.

On some methods for improving time of reachability sets computation for the dynamic system control problem

NASA Astrophysics Data System (ADS)

Zimovets, Artem; Matviychuk, Alexander; Ushakov, Vladimir

2016-12-01

The paper presents two different approaches to reduce the time of computer calculation of reachability sets. First of these two approaches use different data structures for storing the reachability sets in the computer memory for calculation in single-threaded mode. Second approach is based on using parallel algorithms with reference to the data structures from the first approach. Within the framework of this paper parallel algorithm of approximate reachability set calculation on computer with SMP-architecture is proposed. The results of numerical modelling are presented in the form of tables which demonstrate high efficiency of parallel computing technology and also show how computing time depends on the used data structure.

Dynamical analysis of Parkinsonian state emulated by hybrid Izhikevich neuron models

NASA Astrophysics Data System (ADS)

Liu, Chen; Wang, Jiang; Yu, Haitao; Deng, Bin; Wei, Xile; Li, Huiyan; Loparo, Kenneth A.; Fietkiewicz, Chris

2015-11-01

Computational models play a significant role in exploring novel theories to complement the findings of physiological experiments. Various computational models have been developed to reveal the mechanisms underlying brain functions. Particularly, in the development of therapies to modulate behavioral and pathological abnormalities, computational models provide the basic foundations to exhibit transitions between physiological and pathological conditions. Considering the significant roles of the intrinsic properties of the globus pallidus and the coupling connections between neurons in determining the firing patterns and the dynamical activities of the basal ganglia neuronal network, we propose a hypothesis that pathological behaviors under the Parkinsonian state may originate from combined effects of intrinsic properties of globus pallidus neurons and synaptic conductances in the whole neuronal network. In order to establish a computational efficient network model, hybrid Izhikevich neuron model is used due to its capacity of capturing the dynamical characteristics of the biological neuronal activities. Detailed analysis of the individual Izhikevich neuron model can assist in understanding the roles of model parameters, which then facilitates the establishment of the basal ganglia-thalamic network model, and contributes to a further exploration of the underlying mechanisms of the Parkinsonian state. Simulation results show that the hybrid Izhikevich neuron model is capable of capturing many of the dynamical properties of the basal ganglia-thalamic neuronal network, such as variations of the firing rates and emergence of synchronous oscillations under the Parkinsonian condition, despite the simplicity of the two-dimensional neuronal model. It may suggest that the computational efficient hybrid Izhikevich neuron model can be used to explore basal ganglia normal and abnormal functions. Especially it provides an efficient way of emulating the large-scale neuron network and potentially contributes to development of improved therapy for neurological disorders such as Parkinson's disease.

Numerical simulation of steady supersonic flow over spinning bodies of revolution

NASA Technical Reports Server (NTRS)

Sturek, W. B.; Schiff, L. B.

1982-01-01

A recently reported parabolized Navier-Stokes code has been employed to compute the supersonic flowfield about a spinning cone and spinning and nonspinning ogive cylinder and boattailed bodies of revolution at moderate incidence. The computations were performed for flow conditions where extensive measurements for wall pressure, boundary-layer velocity profiles, and Magnus force had been obtained. Comparisons between the computational results and experiment indicate excellent agreement for angles of attack up to 6 deg. At angles greater than 6 deg discrepancies are noted which are tentatively attributed to turbulence modeling errors. The comparisons for Magnus effects show that the code accurately predicts the effects of body shape for the selected models.

Navier-Stokes and Comprehensive Analysis Performance Predictions of the NREL Phase VI Experiment

NASA Technical Reports Server (NTRS)

Duque, Earl P. N.; Burklund, Michael D.; Johnson, Wayne

2003-01-01

A vortex lattice code, CAMRAD II, and a Reynolds-Averaged Navier-Stoke code, OVERFLOW-D2, were used to predict the aerodynamic performance of a two-bladed horizontal axis wind turbine. All computations were compared with experimental data that was collected at the NASA Ames Research Center 80- by 120-Foot Wind Tunnel. Computations were performed for both axial as well as yawed operating conditions. Various stall delay models and dynamics stall models were used by the CAMRAD II code. Comparisons between the experimental data and computed aerodynamic loads show that the OVERFLOW-D2 code can accurately predict the power and spanwise loading of a wind turbine rotor.

Model selection for the North American Breeding Bird Survey: A comparison of methods

USGS Publications Warehouse

Link, William; Sauer, John; Niven, Daniel

2017-01-01

The North American Breeding Bird Survey (BBS) provides data for >420 bird species at multiple geographic scales over 5 decades. Modern computational methods have facilitated the fitting of complex hierarchical models to these data. It is easy to propose and fit new models, but little attention has been given to model selection. Here, we discuss and illustrate model selection using leave-one-out cross validation, and the Bayesian Predictive Information Criterion (BPIC). Cross-validation is enormously computationally intensive; we thus evaluate the performance of the Watanabe-Akaike Information Criterion (WAIC) as a computationally efficient approximation to the BPIC. Our evaluation is based on analyses of 4 models as applied to 20 species covered by the BBS. Model selection based on BPIC provided no strong evidence of one model being consistently superior to the others; for 14/20 species, none of the models emerged as superior. For the remaining 6 species, a first-difference model of population trajectory was always among the best fitting. Our results show that WAIC is not reliable as a surrogate for BPIC. Development of appropriate model sets and their evaluation using BPIC is an important innovation for the analysis of BBS data.

Simple Statistical Model to Quantify Maximum Expected EMC in Spacecraft and Avionics Boxes

NASA Technical Reports Server (NTRS)

Trout, Dawn H.; Bremner, Paul

2014-01-01

This study shows cumulative distribution function (CDF) comparisons of composite a fairing electromagnetic field data obtained by computational electromagnetic 3D full wave modeling and laboratory testing. Test and model data correlation is shown. In addition, this presentation shows application of the power balance and extention of this method to predict the variance and maximum exptected mean of the E-field data. This is valuable for large scale evaluations of transmission inside cavities.

Developing an Educational Computer Game for Migratory Bird Identification Based on a Two-Tier Test Approach

ERIC Educational Resources Information Center

Chu, Hui-Chun; Chang, Shao-Chen

2014-01-01

Although educational computer games have been recognized as being a promising approach, previous studies have indicated that, without supportive models, students might only show temporary interest during the game-based learning process, and their learning performance is often not as good as expected. Therefore, in this paper, a two-tier test…

Optimal design of a combustion chamber of gas turbine engine by a Combustion chamber 1D-2D computer program

NASA Astrophysics Data System (ADS)

Aleksandrov, Y. B.; Mingazov, B. G.

2017-09-01

The paper shows a method of modeling and optimization of processes in combustion chambers of gas turbine engines using a computer program developed by a team at the Department of Jet Engines and Power Plants (DJEPP) of Technical University named after A N Tupolev KNRTU-KAI.

A Fast Synthetic Aperture Radar Raw Data Simulation Using Cloud Computing.

PubMed

Li, Zhixin; Su, Dandan; Zhu, Haijiang; Li, Wei; Zhang, Fan; Li, Ruirui

2017-01-08

Synthetic Aperture Radar (SAR) raw data simulation is a fundamental problem in radar system design and imaging algorithm research. The growth of surveying swath and resolution results in a significant increase in data volume and simulation period, which can be considered to be a comprehensive data intensive and computing intensive issue. Although several high performance computing (HPC) methods have demonstrated their potential for accelerating simulation, the input/output (I/O) bottleneck of huge raw data has not been eased. In this paper, we propose a cloud computing based SAR raw data simulation algorithm, which employs the MapReduce model to accelerate the raw data computing and the Hadoop distributed file system (HDFS) for fast I/O access. The MapReduce model is designed for the irregular parallel accumulation of raw data simulation, which greatly reduces the parallel efficiency of graphics processing unit (GPU) based simulation methods. In addition, three kinds of optimization strategies are put forward from the aspects of programming model, HDFS configuration and scheduling. The experimental results show that the cloud computing based algorithm achieves 4_ speedup over the baseline serial approach in an 8-node cloud environment, and each optimization strategy can improve about 20%. This work proves that the proposed cloud algorithm is capable of solving the computing intensive and data intensive issues in SAR raw data simulation, and is easily extended to large scale computing to achieve higher acceleration.

Boolean and brain-inspired computing using spin-transfer torque devices

NASA Astrophysics Data System (ADS)

Fan, Deliang

Several completely new approaches (such as spintronic, carbon nanotube, graphene, TFETs, etc.) to information processing and data storage technologies are emerging to address the time frame beyond current Complementary Metal-Oxide-Semiconductor (CMOS) roadmap. The high speed magnetization switching of a nano-magnet due to current induced spin-transfer torque (STT) have been demonstrated in recent experiments. Such STT devices can be explored in compact, low power memory and logic design. In order to truly leverage STT devices based computing, researchers require a re-think of circuit, architecture, and computing model, since the STT devices are unlikely to be drop-in replacements for CMOS. The potential of STT devices based computing will be best realized by considering new computing models that are inherently suited to the characteristics of STT devices, and new applications that are enabled by their unique capabilities, thereby attaining performance that CMOS cannot achieve. The goal of this research is to conduct synergistic exploration in architecture, circuit and device levels for Boolean and brain-inspired computing using nanoscale STT devices. Specifically, we first show that the non-volatile STT devices can be used in designing configurable Boolean logic blocks. We propose a spin-memristor threshold logic (SMTL) gate design, where memristive cross-bar array is used to perform current mode summation of binary inputs and the low power current mode spintronic threshold device carries out the energy efficient threshold operation. Next, for brain-inspired computing, we have exploited different spin-transfer torque device structures that can implement the hard-limiting and soft-limiting artificial neuron transfer functions respectively. We apply such STT based neuron (or 'spin-neuron') in various neural network architectures, such as hierarchical temporal memory and feed-forward neural network, for performing "human-like" cognitive computing, which show more than two orders of lower energy consumption compared to state of the art CMOS implementation. Finally, we show the dynamics of injection locked Spin Hall Effect Spin-Torque Oscillator (SHE-STO) cluster can be exploited as a robust multi-dimensional distance metric for associative computing, image/ video analysis, etc. Our simulation results show that the proposed system architecture with injection locked SHE-STOs and the associated CMOS interface circuits can be suitable for robust and energy efficient associative computing and pattern matching.

Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT.

PubMed

Lavassani, Mehrzad; Forsström, Stefan; Jennehag, Ulf; Zhang, Tingting

2018-05-12

Digitalization is a global trend becoming ever more important to our connected and sustainable society. This trend also affects industry where the Industrial Internet of Things is an important part, and there is a need to conserve spectrum as well as energy when communicating data to a fog or cloud back-end system. In this paper we investigate the benefits of fog computing by proposing a novel distributed learning model on the sensor device and simulating the data stream in the fog, instead of transmitting all raw sensor values to the cloud back-end. To save energy and to communicate as few packets as possible, the updated parameters of the learned model at the sensor device are communicated in longer time intervals to a fog computing system. The proposed framework is implemented and tested in a real world testbed in order to make quantitative measurements and evaluate the system. Our results show that the proposed model can achieve a 98% decrease in the number of packets sent over the wireless link, and the fog node can still simulate the data stream with an acceptable accuracy of 97%. We also observe an end-to-end delay of 180 ms in our proposed three-layer framework. Hence, the framework shows that a combination of fog and cloud computing with a distributed data modeling at the sensor device for wireless sensor networks can be beneficial for Industrial Internet of Things applications.

Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT

PubMed Central

Lavassani, Mehrzad; Jennehag, Ulf; Zhang, Tingting

2018-01-01

Digitalization is a global trend becoming ever more important to our connected and sustainable society. This trend also affects industry where the Industrial Internet of Things is an important part, and there is a need to conserve spectrum as well as energy when communicating data to a fog or cloud back-end system. In this paper we investigate the benefits of fog computing by proposing a novel distributed learning model on the sensor device and simulating the data stream in the fog, instead of transmitting all raw sensor values to the cloud back-end. To save energy and to communicate as few packets as possible, the updated parameters of the learned model at the sensor device are communicated in longer time intervals to a fog computing system. The proposed framework is implemented and tested in a real world testbed in order to make quantitative measurements and evaluate the system. Our results show that the proposed model can achieve a 98% decrease in the number of packets sent over the wireless link, and the fog node can still simulate the data stream with an acceptable accuracy of 97%. We also observe an end-to-end delay of 180 ms in our proposed three-layer framework. Hence, the framework shows that a combination of fog and cloud computing with a distributed data modeling at the sensor device for wireless sensor networks can be beneficial for Industrial Internet of Things applications. PMID:29757227

Computational mechanisms underlying cortical responses to the affordance properties of visual scenes

PubMed Central

Epstein, Russell A.

2018-01-01

Biologically inspired deep convolutional neural networks (CNNs), trained for computer vision tasks, have been found to predict cortical responses with remarkable accuracy. However, the internal operations of these models remain poorly understood, and the factors that account for their success are unknown. Here we develop a set of techniques for using CNNs to gain insights into the computational mechanisms underlying cortical responses. We focused on responses in the occipital place area (OPA), a scene-selective region of dorsal occipitoparietal cortex. In a previous study, we showed that fMRI activation patterns in the OPA contain information about the navigational affordances of scenes; that is, information about where one can and cannot move within the immediate environment. We hypothesized that this affordance information could be extracted using a set of purely feedforward computations. To test this idea, we examined a deep CNN with a feedforward architecture that had been previously trained for scene classification. We found that responses in the CNN to scene images were highly predictive of fMRI responses in the OPA. Moreover the CNN accounted for the portion of OPA variance relating to the navigational affordances of scenes. The CNN could thus serve as an image-computable candidate model of affordance-related responses in the OPA. We then ran a series of in silico experiments on this model to gain insights into its internal operations. These analyses showed that the computation of affordance-related features relied heavily on visual information at high-spatial frequencies and cardinal orientations, both of which have previously been identified as low-level stimulus preferences of scene-selective visual cortex. These computations also exhibited a strong preference for information in the lower visual field, which is consistent with known retinotopic biases in the OPA. Visualizations of feature selectivity within the CNN suggested that affordance-based responses encoded features that define the layout of the spatial environment, such as boundary-defining junctions and large extended surfaces. Together, these results map the sensory functions of the OPA onto a fully quantitative model that provides insights into its visual computations. More broadly, they advance integrative techniques for understanding visual cortex across multiple level of analysis: from the identification of cortical sensory functions to the modeling of their underlying algorithms. PMID:29684011

Manufacturing Magic and Computational Creativity

PubMed Central

Williams, Howard; McOwan, Peter W.

2016-01-01

This paper describes techniques in computational creativity, blending mathematical modeling and psychological insight, to generate new magic tricks. The details of an explicit computational framework capable of creating new magic tricks are summarized, and evaluated against a range of contemporary theories about what constitutes a creative system. To allow further development of the proposed system we situate this approach to the generation of magic in the wider context of other areas of application in computational creativity in performance arts. We show how approaches in these domains could be incorporated to enhance future magic generation systems, and critically review possible future applications of such magic generating computers. PMID:27375533

Optimization of tomographic reconstruction workflows on geographically distributed resources

DOE PAGES

Bicer, Tekin; Gursoy, Doga; Kettimuthu, Rajkumar; ...

2016-01-01

New technological advancements in synchrotron light sources enable data acquisitions at unprecedented levels. This emergent trend affects not only the size of the generated data but also the need for larger computational resources. Although beamline scientists and users have access to local computational resources, these are typically limited and can result in extended execution times. Applications that are based on iterative processing as in tomographic reconstruction methods require high-performance compute clusters for timely analysis of data. Here, time-sensitive analysis and processing of Advanced Photon Source data on geographically distributed resources are focused on. Two main challenges are considered: (i) modelingmore » of the performance of tomographic reconstruction workflows and (ii) transparent execution of these workflows on distributed resources. For the former, three main stages are considered: (i) data transfer between storage and computational resources, (i) wait/queue time of reconstruction jobs at compute resources, and (iii) computation of reconstruction tasks. These performance models allow evaluation and estimation of the execution time of any given iterative tomographic reconstruction workflow that runs on geographically distributed resources. For the latter challenge, a workflow management system is built, which can automate the execution of workflows and minimize the user interaction with the underlying infrastructure. The system utilizes Globus to perform secure and efficient data transfer operations. The proposed models and the workflow management system are evaluated by using three high-performance computing and two storage resources, all of which are geographically distributed. Workflows were created with different computational requirements using two compute-intensive tomographic reconstruction algorithms. Experimental evaluation shows that the proposed models and system can be used for selecting the optimum resources, which in turn can provide up to 3.13× speedup (on experimented resources). Furthermore, the error rates of the models range between 2.1 and 23.3% (considering workflow execution times), where the accuracy of the model estimations increases with higher computational demands in reconstruction tasks.« less

Optimization of tomographic reconstruction workflows on geographically distributed resources

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

Bicer, Tekin; Gursoy, Doga; Kettimuthu, Rajkumar

New technological advancements in synchrotron light sources enable data acquisitions at unprecedented levels. This emergent trend affects not only the size of the generated data but also the need for larger computational resources. Although beamline scientists and users have access to local computational resources, these are typically limited and can result in extended execution times. Applications that are based on iterative processing as in tomographic reconstruction methods require high-performance compute clusters for timely analysis of data. Here, time-sensitive analysis and processing of Advanced Photon Source data on geographically distributed resources are focused on. Two main challenges are considered: (i) modelingmore » of the performance of tomographic reconstruction workflows and (ii) transparent execution of these workflows on distributed resources. For the former, three main stages are considered: (i) data transfer between storage and computational resources, (i) wait/queue time of reconstruction jobs at compute resources, and (iii) computation of reconstruction tasks. These performance models allow evaluation and estimation of the execution time of any given iterative tomographic reconstruction workflow that runs on geographically distributed resources. For the latter challenge, a workflow management system is built, which can automate the execution of workflows and minimize the user interaction with the underlying infrastructure. The system utilizes Globus to perform secure and efficient data transfer operations. The proposed models and the workflow management system are evaluated by using three high-performance computing and two storage resources, all of which are geographically distributed. Workflows were created with different computational requirements using two compute-intensive tomographic reconstruction algorithms. Experimental evaluation shows that the proposed models and system can be used for selecting the optimum resources, which in turn can provide up to 3.13× speedup (on experimented resources). Furthermore, the error rates of the models range between 2.1 and 23.3% (considering workflow execution times), where the accuracy of the model estimations increases with higher computational demands in reconstruction tasks.« less

A Computational Model of Linguistic Humor in Puns.

PubMed

Kao, Justine T; Levy, Roger; Goodman, Noah D

2016-07-01

Humor plays an essential role in human interactions. Precisely what makes something funny, however, remains elusive. While research on natural language understanding has made significant advancements in recent years, there has been little direct integration of humor research with computational models of language understanding. In this paper, we propose two information-theoretic measures-ambiguity and distinctiveness-derived from a simple model of sentence processing. We test these measures on a set of puns and regular sentences and show that they correlate significantly with human judgments of funniness. Moreover, within a set of puns, the distinctiveness measure distinguishes exceptionally funny puns from mediocre ones. Our work is the first, to our knowledge, to integrate a computational model of general language understanding and humor theory to quantitatively predict humor at a fine-grained level. We present it as an example of a framework for applying models of language processing to understand higher level linguistic and cognitive phenomena. © 2015 The Authors. Cognitive Science published by Wiley Periodicals, Inc. on behalf of Cognitive Science Society.

Inexact hardware for modelling weather & climate

NASA Astrophysics Data System (ADS)

Düben, Peter D.; McNamara, Hugh; Palmer, Tim

2014-05-01

The use of stochastic processing hardware and low precision arithmetic in atmospheric models is investigated. Stochastic processors allow hardware-induced faults in calculations, sacrificing exact calculations in exchange for improvements in performance and potentially accuracy and a reduction in power consumption. A similar trade-off is achieved using low precision arithmetic, with improvements in computation and communication speed and savings in storage and memory requirements. As high-performance computing becomes more massively parallel and power intensive, these two approaches may be important stepping stones in the pursuit of global cloud resolving atmospheric modelling. The impact of both, hardware induced faults and low precision arithmetic is tested in the dynamical core of a global atmosphere model. Our simulations show that both approaches to inexact calculations do not substantially affect the quality of the model simulations, provided they are restricted to act only on smaller scales. This suggests that inexact calculations at the small scale could reduce computation and power costs without adversely affecting the quality of the simulations.

Impact of surface coupling grids on tropical cyclone extremes in high-resolution atmospheric simulations

DOE PAGES

Zarzycki, Colin M.; Reed, Kevin A.; Bacmeister, Julio T.; ...

2016-02-25

This article discusses the sensitivity of tropical cyclone climatology to surface coupling strategy in high-resolution configurations of the Community Earth System Model. Using two supported model setups, we demonstrate that the choice of grid on which the lowest model level wind stress and surface fluxes are computed may lead to differences in cyclone strength in multi-decadal climate simulations, particularly for the most intense cyclones. Using a deterministic framework, we show that when these surface quantities are calculated on an ocean grid that is coarser than the atmosphere, the computed frictional stress is misaligned with wind vectors in individual atmospheric gridmore » cells. This reduces the effective surface drag, and results in more intense cyclones when compared to a model configuration where the ocean and atmosphere are of equivalent resolution. Our results demonstrate that the choice of computation grid for atmosphere–ocean interactions is non-negligible when considering climate extremes at high horizontal resolution, especially when model components are on highly disparate grids.« less

Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

PubMed

Said, Nadia; Engelhart, Michael; Kirches, Christian; Körkel, Stefan; Holt, Daniel V

2016-01-01

Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

Micro-structurally detailed model of a therapeutic hydrogel injectate in a rat biventricular cardiac geometry for computational simulations

PubMed Central

Sirry, Mazin S.; Davies, Neil H.; Kadner, Karen; Dubuis, Laura; Saleh, Muhammad G.; Meintjes, Ernesta M.; Spottiswoode, Bruce S.; Zilla, Peter; Franz, Thomas

2013-01-01

Biomaterial injection based therapies have showed cautious success in restoration of cardiac function and prevention of adverse remodelling into heart failure after myocardial infarction (MI). However, the underlying mechanisms are not well understood. Computational studies utilised simplified representations of the therapeutic myocardial injectates. Wistar rats underwent experimental infarction followed by immediate injection of polyethylene glycol hydrogel in the infarct region. Hearts were explanted, cryo-sectioned and the region with the injectate histologically analysed. Histological micrographs were used to reconstruct the dispersed hydrogel injectate. Cardiac magnetic resonance imaging (CMRI) data from a healthy rat were used to obtain an end-diastolic biventricular geometry which was subsequently adjusted and combined with the injectate model. The computational geometry of the injectate exhibited microscopic structural details found the in situ. The combination of injectate and cardiac geometry provides realistic geometries for multiscale computational studies of intra-myocardial injectate therapies for the rat model that has been widely used for MI research. PMID:23682845

[Computer aided diagnosis model for lung tumor based on ensemble convolutional neural network].

PubMed

Wang, Yuanyuan; Zhou, Tao; Lu, Huiling; Wu, Cuiying; Yang, Pengfei

2017-08-01

The convolutional neural network (CNN) could be used on computer-aided diagnosis of lung tumor with positron emission tomography (PET)/computed tomography (CT), which can provide accurate quantitative analysis to compensate for visual inertia and defects in gray-scale sensitivity, and help doctors diagnose accurately. Firstly, parameter migration method is used to build three CNNs (CT-CNN, PET-CNN, and PET/CT-CNN) for lung tumor recognition in CT, PET, and PET/CT image, respectively. Then, we aimed at CT-CNN to obtain the appropriate model parameters for CNN training through analysis the influence of model parameters such as epochs, batchsize and image scale on recognition rate and training time. Finally, three single CNNs are used to construct ensemble CNN, and then lung tumor PET/CT recognition was completed through relative majority vote method and the performance between ensemble CNN and single CNN was compared. The experiment results show that the ensemble CNN is better than single CNN on computer-aided diagnosis of lung tumor.

Automatic domain updating technique for improving computational efficiency of 2-D flood-inundation simulation

NASA Astrophysics Data System (ADS)

Tanaka, T.; Tachikawa, Y.; Ichikawa, Y.; Yorozu, K.

2017-12-01

Flood is one of the most hazardous disasters and causes serious damage to people and property around the world. To prevent/mitigate flood damage through early warning system and/or river management planning, numerical modelling of flood-inundation processes is essential. In a literature, flood-inundation models have been extensively developed and improved to achieve flood flow simulation with complex topography at high resolution. With increasing demands on flood-inundation modelling, its computational burden is now one of the key issues. Improvements of computational efficiency of full shallow water equations are made from various perspectives such as approximations of the momentum equations, parallelization technique, and coarsening approaches. To support these techniques and more improve the computational efficiency of flood-inundation simulations, this study proposes an Automatic Domain Updating (ADU) method of 2-D flood-inundation simulation. The ADU method traces the wet and dry interface and automatically updates the simulation domain in response to the progress and recession of flood propagation. The updating algorithm is as follow: first, to register the simulation cells potentially flooded at initial stage (such as floodplains nearby river channels), and then if a registered cell is flooded, to register its surrounding cells. The time for this additional process is saved by checking only cells at wet and dry interface. The computation time is reduced by skipping the processing time of non-flooded area. This algorithm is easily applied to any types of 2-D flood inundation models. The proposed ADU method is implemented to 2-D local inertial equations for the Yodo River basin, Japan. Case studies for two flood events show that the simulation is finished within two to 10 times smaller time showing the same result as that without the ADU method.

Closed-form solution of decomposable stochastic models

NASA Technical Reports Server (NTRS)

Sjogren, Jon A.

1990-01-01

Markov and semi-Markov processes are increasingly being used in the modeling of complex reconfigurable systems (fault tolerant computers). The estimation of the reliability (or some measure of performance) of the system reduces to solving the process for its state probabilities. Such a model may exhibit numerous states and complicated transition distributions, contributing to an expensive and numerically delicate solution procedure. Thus, when a system exhibits a decomposition property, either structurally (autonomous subsystems), or behaviorally (component failure versus reconfiguration), it is desirable to exploit this decomposition in the reliability calculation. In interesting cases there can be failure states which arise from non-failure states of the subsystems. Equations are presented which allow the computation of failure probabilities of the total (combined) model without requiring a complete solution of the combined model. This material is presented within the context of closed-form functional representation of probabilities as utilized in the Symbolic Hierarchical Automated Reliability and Performance Evaluator (SHARPE) tool. The techniques adopted enable one to compute such probability functions for a much wider class of systems at a reduced computational cost. Several examples show how the method is used, especially in enhancing the versatility of the SHARPE tool.

Computational model of collagen turnover in carotid arteries during hypertension.

PubMed

Sáez, P; Peña, E; Tarbell, J M; Martínez, M A

2015-02-01

It is well known that biological tissues adapt their properties because of different mechanical and chemical stimuli. The goal of this work is to study the collagen turnover in the arterial tissue of hypertensive patients through a coupled computational mechano-chemical model. Although it has been widely studied experimentally, computational models dealing with the mechano-chemical approach are not. The present approach can be extended easily to study other aspects of bone remodeling or collagen degradation in heart diseases. The model can be divided into three different stages. First, we study the smooth muscle cell synthesis of different biological substances due to over-stretching during hypertension. Next, we study the mass-transport of these substances along the arterial wall. The last step is to compute the turnover of collagen based on the amount of these substances in the arterial wall which interact with each other to modify the turnover rate of collagen. We simulate this process in a finite element model of a real human carotid artery. The final results show the well-known stiffening of the arterial wall due to the increase in the collagen content. Copyright © 2015 John Wiley & Sons, Ltd.

Algorithms for Efficient Computation of Transfer Functions for Large Order Flexible Systems

NASA Technical Reports Server (NTRS)

Maghami, Peiman G.; Giesy, Daniel P.

1998-01-01

An efficient and robust computational scheme is given for the calculation of the frequency response function of a large order, flexible system implemented with a linear, time invariant control system. Advantage is taken of the highly structured sparsity of the system matrix of the plant based on a model of the structure using normal mode coordinates. The computational time per frequency point of the new computational scheme is a linear function of system size, a significant improvement over traditional, still-matrix techniques whose computational times per frequency point range from quadratic to cubic functions of system size. This permits the practical frequency domain analysis of systems of much larger order than by traditional, full-matrix techniques. Formulations are given for both open- and closed-loop systems. Numerical examples are presented showing the advantages of the present formulation over traditional approaches, both in speed and in accuracy. Using a model with 703 structural modes, the present method was up to two orders of magnitude faster than a traditional method. The present method generally showed good to excellent accuracy throughout the range of test frequencies, while traditional methods gave adequate accuracy for lower frequencies, but generally deteriorated in performance at higher frequencies with worst case errors being many orders of magnitude times the correct values.

Parallelization of interpolation, solar radiation and water flow simulation modules in GRASS GIS using OpenMP

NASA Astrophysics Data System (ADS)

Hofierka, Jaroslav; Lacko, Michal; Zubal, Stanislav

2017-10-01

In this paper, we describe the parallelization of three complex and computationally intensive modules of GRASS GIS using the OpenMP application programming interface for multi-core computers. These include the v.surf.rst module for spatial interpolation, the r.sun module for solar radiation modeling and the r.sim.water module for water flow simulation. We briefly describe the functionality of the modules and parallelization approaches used in the modules. Our approach includes the analysis of the module's functionality, identification of source code segments suitable for parallelization and proper application of OpenMP parallelization code to create efficient threads processing the subtasks. We document the efficiency of the solutions using the airborne laser scanning data representing land surface in the test area and derived high-resolution digital terrain model grids. We discuss the performance speed-up and parallelization efficiency depending on the number of processor threads. The study showed a substantial increase in computation speeds on a standard multi-core computer while maintaining the accuracy of results in comparison to the output from original modules. The presented parallelization approach showed the simplicity and efficiency of the parallelization of open-source GRASS GIS modules using OpenMP, leading to an increased performance of this geospatial software on standard multi-core computers.

Visual Attention Modeling for Stereoscopic Video: A Benchmark and Computational Model.

PubMed

Fang, Yuming; Zhang, Chi; Li, Jing; Lei, Jianjun; Perreira Da Silva, Matthieu; Le Callet, Patrick

2017-10-01

In this paper, we investigate the visual attention modeling for stereoscopic video from the following two aspects. First, we build one large-scale eye tracking database as the benchmark of visual attention modeling for stereoscopic video. The database includes 47 video sequences and their corresponding eye fixation data. Second, we propose a novel computational model of visual attention for stereoscopic video based on Gestalt theory. In the proposed model, we extract the low-level features, including luminance, color, texture, and depth, from discrete cosine transform coefficients, which are used to calculate feature contrast for the spatial saliency computation. The temporal saliency is calculated by the motion contrast from the planar and depth motion features in the stereoscopic video sequences. The final saliency is estimated by fusing the spatial and temporal saliency with uncertainty weighting, which is estimated by the laws of proximity, continuity, and common fate in Gestalt theory. Experimental results show that the proposed method outperforms the state-of-the-art stereoscopic video saliency detection models on our built large-scale eye tracking database and one other database (DML-ITRACK-3D).

Accuracy assessment of the linear Poisson-Boltzmann equation and reparametrization of the OBC generalized Born model for nucleic acids and nucleic acid-protein complexes.

PubMed

Fogolari, Federico; Corazza, Alessandra; Esposito, Gennaro

2015-04-05

The generalized Born model in the Onufriev, Bashford, and Case (Onufriev et al., Proteins: Struct Funct Genet 2004, 55, 383) implementation has emerged as one of the best compromises between accuracy and speed of computation. For simulations of nucleic acids, however, a number of issues should be addressed: (1) the generalized Born model is based on a linear model and the linearization of the reference Poisson-Boltmann equation may be questioned for highly charged systems as nucleic acids; (2) although much attention has been given to potentials, solvation forces could be much less sensitive to linearization than the potentials; and (3) the accuracy of the Onufriev-Bashford-Case (OBC) model for nucleic acids depends on fine tuning of parameters. Here, we show that the linearization of the Poisson Boltzmann equation has mild effects on computed forces, and that with optimal choice of the OBC model parameters, solvation forces, essential for molecular dynamics simulations, agree well with those computed using the reference Poisson-Boltzmann model. © 2015 Wiley Periodicals, Inc.

Free energy and phase transition of the matrix model on a plane wave

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

Hadizadeh, Shirin; Ramadanovic, Bojan; Semenoff, Gordon W.

2005-03-15

It has recently been observed that the weakly coupled plane-wave matrix model has a density of states which grows exponentially at high energy. This implies that the model has a phase transition. The transition appears to be of first order. However, its exact nature is sensitive to interactions. In this paper, we analyze the effect of interactions by computing the relevant parts of the effective potential for the Polyakov loop operator in the finite temperature plane-wave matrix model to three-loop order. We show that the phase transition is indeed of first order. We also compute the correction to the Hagedornmore » temperature to order two loops.« less

Agent-based model to rural urban migration analysis

NASA Astrophysics Data System (ADS)

Silveira, Jaylson J.; Espíndola, Aquino L.; Penna, T. J. P.

2006-05-01

In this paper, we analyze the rural-urban migration phenomenon as it is usually observed in economies which are in the early stages of industrialization. The analysis is conducted by means of a statistical mechanics approach which builds a computational agent-based model. Agents are placed on a lattice and the connections among them are described via an Ising-like model. Simulations on this computational model show some emergent properties that are common in developing economies, such as a transitional dynamics characterized by continuous growth of urban population, followed by the equalization of expected wages between rural and urban sectors (Harris-Todaro equilibrium condition), urban concentration and increasing of per capita income.

Minimal Traffic Model with Safe Driving Conditions

NASA Astrophysics Data System (ADS)

Terborg, Heinrich; Pérez, Luis A.

We have developed a new computational traffic model in which security aspects are fundamental. In this paper we show that this model reproduces many known empirical aspects of vehicular traffic such as the three states of traffic flow and the backward speed of the downstream front of a traffic jam (C), without the aid of adjustable parameters. The model is studied for both open and closed single lane traffic systems. Also, we were able to analytically compute the value of C as 15.37 km/h from a relation that only includes the human reaction time, the mean vehicle length and the effective friction coefficient during the braking process of a vehicle as its main components.

Modeling the Impact of Motivation, Personality, and Emotion on Social Behavior

NASA Astrophysics Data System (ADS)

Miller, Lynn C.; Read, Stephen J.; Zachary, Wayne; Rosoff, Andrew

Models seeking to predict human social behavior must contend with multiple sources of individual and group variability that underlie social behavior. One set of interrelated factors that strongly contribute to that variability - motivations, personality, and emotions - has been only minimally incorporated in previous computational models of social behavior. The Personality, Affect, Culture (PAC) framework is a theory-based computational model that addresses this gap. PAC is used to simulate social agents whose social behavior varies according to their personalities and emotions, which, in turn, vary according to their motivations and underlying motive control parameters. Examples involving disease spread and counter-insurgency operations show how PAC can be used to study behavioral variability in different social contexts.

Comparisons of regional Hydrological Angular Momentum (HAM) of the different models

NASA Astrophysics Data System (ADS)

Nastula, J.; Kolaczek, B.; Popinski, W.

2006-10-01

In the paper hydrological excitations of the polar motion (HAM) were computed from various hydrological data series (NCEP, ECMWF, CPC water storage and LaD World Simulations of global continental water). HAM series obtained from these four models and the geodetic excitation function GEOD computed from the polar motion COMB03 data were compared in the seasonal spectral band. The results show big differences of these hydrological excitation functions as well as of their spectra in the seasonal spectra band. Seasonal oscillations of the global geophysical excitation functions (AAM + OAM + HAM) in all cases besides the NCEP/NCAR model are smaller than the geodetic excitation function. It means that these models need further improvement and perhaps not only hydrological models need improvements.

Spiking Neural Network With Distributed Plasticity Reproduces Cerebellar Learning in Eye Blink Conditioning Paradigms.

PubMed

Antonietti, Alberto; Casellato, Claudia; Garrido, Jesús A; Luque, Niceto R; Naveros, Francisco; Ros, Eduardo; D' Angelo, Egidio; Pedrocchi, Alessandra

2016-01-01

In this study, we defined a realistic cerebellar model through the use of artificial spiking neural networks, testing it in computational simulations that reproduce associative motor tasks in multiple sessions of acquisition and extinction. By evolutionary algorithms, we tuned the cerebellar microcircuit to find out the near-optimal plasticity mechanism parameters that better reproduced human-like behavior in eye blink classical conditioning, one of the most extensively studied paradigms related to the cerebellum. We used two models: one with only the cortical plasticity and another including two additional plasticity sites at nuclear level. First, both spiking cerebellar models were able to well reproduce the real human behaviors, in terms of both "timing" and "amplitude", expressing rapid acquisition, stable late acquisition, rapid extinction, and faster reacquisition of an associative motor task. Even though the model with only the cortical plasticity site showed good learning capabilities, the model with distributed plasticity produced faster and more stable acquisition of conditioned responses in the reacquisition phase. This behavior is explained by the effect of the nuclear plasticities, which have slow dynamics and can express memory consolidation and saving. We showed how the spiking dynamics of multiple interactive neural mechanisms implicitly drive multiple essential components of complex learning processes.  This study presents a very advanced computational model, developed together by biomedical engineers, computer scientists, and neuroscientists. Since its realistic features, the proposed model can provide confirmations and suggestions about neurophysiological and pathological hypotheses and can be used in challenging clinical applications.

Quantum Computation

NASA Astrophysics Data System (ADS)

Aharonov, Dorit

In the last few years, theoretical study of quantum systems serving as computational devices has achieved tremendous progress. We now have strong theoretical evidence that quantum computers, if built, might be used as a dramatically powerful computational tool, capable of performing tasks which seem intractable for classical computers. This review is about to tell the story of theoretical quantum computation. I l out the developing topic of experimental realizations of the model, and neglected other closely related topics which are quantum information and quantum communication. As a result of narrowing the scope of this paper, I hope it has gained the benefit of being an almost self contained introduction to the exciting field of quantum computation. The review begins with background on theoretical computer science, Turing machines and Boolean circuits. In light of these models, I define quantum computers, and discuss the issue of universal quantum gates. Quantum algorithms, including Shor's factorization algorithm and Grover's algorithm for searching databases, are explained. I will devote much attention to understanding what the origins of the quantum computational power are, and what the limits of this power are. Finally, I describe the recent theoretical results which show that quantum computers maintain their complexity power even in the presence of noise, inaccuracies and finite precision. This question cannot be separated from that of quantum complexity because any realistic model will inevitably be subjected to such inaccuracies. I tried to put all results in their context, asking what the implications to other issues in computer science and physics are. In the end of this review, I make these connections explicit by discussing the possible implications of quantum computation on fundamental physical questions such as the transition from quantum to classical physics.

Ancilla-driven quantum computation for qudits and continuous variables

NASA Astrophysics Data System (ADS)

Proctor, Timothy; Giulian, Melissa; Korolkova, Natalia; Andersson, Erika; Kendon, Viv

2017-05-01

Although qubits are the leading candidate for the basic elements in a quantum computer, there are also a range of reasons to consider using higher-dimensional qudits or quantum continuous variables (QCVs). In this paper, we use a general "quantum variable" formalism to propose a method of quantum computation in which ancillas are used to mediate gates on a well-isolated "quantum memory" register and which may be applied to the setting of qubits, qudits (for d >2 ), or QCVs. More specifically, we present a model in which universal quantum computation may be implemented on a register using only repeated applications of a single fixed two-body ancilla-register interaction gate, ancillas prepared in a single state, and local measurements of these ancillas. In order to maintain determinism in the computation, adaptive measurements via a classical feed forward of measurement outcomes are used, with the method similar to that in measurement-based quantum computation (MBQC). We show that our model has the same hybrid quantum-classical processing advantages as MBQC, including the power to implement any Clifford circuit in essentially one layer of quantum computation. In some physical settings, high-quality measurements of the ancillas may be highly challenging or not possible, and hence we also present a globally unitary model which replaces the need for measurements of the ancillas with the requirement for ancillas to be prepared in states from a fixed orthonormal basis. Finally, we discuss settings in which these models may be of practical interest.

Combining Feature Selection and Integration—A Neural Model for MT Motion Selectivity

PubMed Central

Beck, Cornelia; Neumann, Heiko

2011-01-01

Background The computation of pattern motion in visual area MT based on motion input from area V1 has been investigated in many experiments and models attempting to replicate the main mechanisms. Two different core conceptual approaches were developed to explain the findings. In integrationist models the key mechanism to achieve pattern selectivity is the nonlinear integration of V1 motion activity. In contrast, selectionist models focus on the motion computation at positions with 2D features. Methodology/Principal Findings Recent experiments revealed that neither of the two concepts alone is sufficient to explain all experimental data and that most of the existing models cannot account for the complex behaviour found. MT pattern selectivity changes over time for stimuli like type II plaids from vector average to the direction computed with an intersection of constraint rule or by feature tracking. Also, the spatial arrangement of the stimulus within the receptive field of a MT cell plays a crucial role. We propose a recurrent neural model showing how feature integration and selection can be combined into one common architecture to explain these findings. The key features of the model are the computation of 1D and 2D motion in model area V1 subpopulations that are integrated in model MT cells using feedforward and feedback processing. Our results are also in line with findings concerning the solution of the aperture problem. Conclusions/Significance We propose a new neural model for MT pattern computation and motion disambiguation that is based on a combination of feature selection and integration. The model can explain a range of recent neurophysiological findings including temporally dynamic behaviour. PMID:21814543

Energy and criticality in random Boolean networks

NASA Astrophysics Data System (ADS)

Andrecut, M.; Kauffman, S. A.

2008-06-01

The central issue of the research on the Random Boolean Networks (RBNs) model is the characterization of the critical transition between ordered and chaotic phases. Here, we discuss an approach based on the ‘energy’ associated with the unsatisfiability of the Boolean functions in the RBNs model, which provides an upper bound estimation for the energy used in computation. We show that in the ordered phase the RBNs are in a ‘dissipative’ regime, performing mostly ‘downhill’ moves on the ‘energy’ landscape. Also, we show that in the disordered phase the RBNs have to ‘hillclimb’ on the ‘energy’ landscape in order to perform computation. The analytical results, obtained using Derrida's approximation method, are in complete agreement with numerical simulations.

Magnon Hall effect on the Lieb lattice.

PubMed

Cao, Xiaodong; Chen, Kai; He, Dahai

2015-04-29

Ferromagnetic insulators without inversion symmetry may show magnon Hall effect (MHE) in the presence of a temperature gradient due to the existence of Dzyaloshinskii-Moriya interaction (DMI). In this theoretical study, we investigate MHE on a lattice with inversion symmetry, namely the Lieb lattice, where the DMI is introduced by adding an external electric field. We show the nontrivial topology of this model by examining the existence of edge states and computing the topological phase diagram characterized by the Chern numbers of different bands. Together with the topological phase diagram, we can further determine the sign and magnitude of the transverse thermal conductivity. The impact of the flat band possessed by this model on the thermal conductivity is discussed by computing the Berry curvature analytically.

Spatio-Temporal Data Analysis at Scale Using Models Based on Gaussian Processes

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

Stein, Michael

Gaussian processes are the most commonly used statistical model for spatial and spatio-temporal processes that vary continuously. They are broadly applicable in the physical sciences and engineering and are also frequently used to approximate the output of complex computer models, deterministic or stochastic. We undertook research related to theory, computation, and applications of Gaussian processes as well as some work on estimating extremes of distributions for which a Gaussian process assumption might be inappropriate. Our theoretical contributions include the development of new classes of spatial-temporal covariance functions with desirable properties and new results showing that certain covariance models lead tomore » predictions with undesirable properties. To understand how Gaussian process models behave when applied to deterministic computer models, we derived what we believe to be the first significant results on the large sample properties of estimators of parameters of Gaussian processes when the actual process is a simple deterministic function. Finally, we investigated some theoretical issues related to maxima of observations with varying upper bounds and found that, depending on the circumstances, standard large sample results for maxima may or may not hold. Our computational innovations include methods for analyzing large spatial datasets when observations fall on a partially observed grid and methods for estimating parameters of a Gaussian process model from observations taken by a polar-orbiting satellite. In our application of Gaussian process models to deterministic computer experiments, we carried out some matrix computations that would have been infeasible using even extended precision arithmetic by focusing on special cases in which all elements of the matrices under study are rational and using exact arithmetic. The applications we studied include total column ozone as measured from a polar-orbiting satellite, sea surface temperatures over the Pacific Ocean, and annual temperature extremes at a site in New York City. In each of these applications, our theoretical and computational innovations were directly motivated by the challenges posed by analyzing these and similar types of data.« less

Critical assessment of Reynolds stress turbulence models using homogeneous flows

NASA Technical Reports Server (NTRS)

Shabbir, Aamir; Shih, Tsan-Hsing

1992-01-01

In modeling the rapid part of the pressure correlation term in the Reynolds stress transport equations, extensive use has been made of its exact properties which were first suggested by Rotta. These, for example, have been employed in obtaining the widely used Launder, Reece and Rodi (LRR) model. Some recent proposals have dropped one of these properties to obtain new models. We demonstrate, by computing some simple homogeneous flows, that doing so does not lead to any significant improvements over the LRR model and it is not the right direction in improving the performance of existing models. The reason for this, in our opinion, is that violation of one of the exact properties can not bring in any new physics into the model. We compute thirteen homogeneous flows using LRR (with a recalibrated rapid term constant), IP and SSG models. The flows computed include the flow through axisymmetric contraction; axisymmetric expansion; distortion by plane strain; and homogeneous shear flows with and without rotation. Results show that for most general representation for a model linear in the anisotropic tensor, performs either better or as good as the other two models of the same level.

The relative effectiveness of computer-based and traditional resources for education in anatomy.

PubMed

Khot, Zaid; Quinlan, Kaitlyn; Norman, Geoffrey R; Wainman, Bruce

2013-01-01

There is increasing use of computer-based resources to teach anatomy, although no study has compared computer-based learning to traditional. In this study, we examine the effectiveness of three formats of anatomy learning: (1) a virtual reality (VR) computer-based module, (2) a static computer-based module providing Key Views (KV), (3) a plastic model. We conducted a controlled trial in which 60 undergraduate students had ten minutes to study the names of 20 different pelvic structures. The outcome measure was a 25 item short answer test consisting of 15 nominal and 10 functional questions, based on a cadaveric pelvis. All subjects also took a brief mental rotations test (MRT) as a measure of spatial ability, used as a covariate in the analysis. Data were analyzed with repeated measures ANOVA. The group learning from the model performed significantly better than the other two groups on the nominal questions (Model 67%; KV 40%; VR 41%, Effect size 1.19 and 1.29, respectively). There was no difference between the KV and VR groups. There was no difference between the groups on the functional questions (Model 28%; KV, 23%, VR 25%). Computer-based learning resources appear to have significant disadvantages compared to traditional specimens in learning nominal anatomy. Consistent with previous research, virtual reality shows no advantage over static presentation of key views. © 2013 American Association of Anatomists.

Algebraic model checking for Boolean gene regulatory networks.

PubMed

Tran, Quoc-Nam

2011-01-01

We present a computational method in which modular and Groebner bases (GB) computation in Boolean rings are used for solving problems in Boolean gene regulatory networks (BN). In contrast to other known algebraic approaches, the degree of intermediate polynomials during the calculation of Groebner bases using our method will never grow resulting in a significant improvement in running time and memory space consumption. We also show how calculation in temporal logic for model checking can be done by means of our direct and efficient Groebner basis computation in Boolean rings. We present our experimental results in finding attractors and control strategies of Boolean networks to illustrate our theoretical arguments. The results are promising. Our algebraic approach is more efficient than the state-of-the-art model checker NuSMV on BNs. More importantly, our approach finds all solutions for the BN problems.

A Computational Approach for Probabilistic Analysis of Water Impact Simulations

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Mason, Brian H.; Lyle, Karen H.

2009-01-01

NASA's development of new concepts for the Crew Exploration Vehicle Orion presents many similar challenges to those worked in the sixties during the Apollo program. However, with improved modeling capabilities, new challenges arise. For example, the use of the commercial code LS-DYNA, although widely used and accepted in the technical community, often involves high-dimensional, time consuming, and computationally intensive simulations. The challenge is to capture what is learned from a limited number of LS-DYNA simulations to develop models that allow users to conduct interpolation of solutions at a fraction of the computational time. This paper presents a description of the LS-DYNA model, a brief summary of the response surface techniques, the analysis of variance approach used in the sensitivity studies, equations used to estimate impact parameters, results showing conditions that might cause injuries, and concluding remarks.

Predicting Forearm Physical Exposures During Computer Work Using Self-Reports, Software-Recorded Computer Usage Patterns, and Anthropometric and Workstation Measurements.

PubMed

Huysmans, Maaike A; Eijckelhof, Belinda H W; Garza, Jennifer L Bruno; Coenen, Pieter; Blatter, Birgitte M; Johnson, Peter W; van Dieën, Jaap H; van der Beek, Allard J; Dennerlein, Jack T

2017-12-15

Alternative techniques to assess physical exposures, such as prediction models, could facilitate more efficient epidemiological assessments in future large cohort studies examining physical exposures in relation to work-related musculoskeletal symptoms. The aim of this study was to evaluate two types of models that predict arm-wrist-hand physical exposures (i.e. muscle activity, wrist postures and kinematics, and keyboard and mouse forces) during computer use, which only differed with respect to the candidate predicting variables; (i) a full set of predicting variables, including self-reported factors, software-recorded computer usage patterns, and worksite measurements of anthropometrics and workstation set-up (full models); and (ii) a practical set of predicting variables, only including the self-reported factors and software-recorded computer usage patterns, that are relatively easy to assess (practical models). Prediction models were build using data from a field study among 117 office workers who were symptom-free at the time of measurement. Arm-wrist-hand physical exposures were measured for approximately two hours while workers performed their own computer work. Each worker's anthropometry and workstation set-up were measured by an experimenter, computer usage patterns were recorded using software and self-reported factors (including individual factors, job characteristics, computer work behaviours, psychosocial factors, workstation set-up characteristics, and leisure-time activities) were collected by an online questionnaire. We determined the predictive quality of the models in terms of R2 and root mean squared (RMS) values and exposure classification agreement to low-, medium-, and high-exposure categories (in the practical model only). The full models had R2 values that ranged from 0.16 to 0.80, whereas for the practical models values ranged from 0.05 to 0.43. Interquartile ranges were not that different for the two models, indicating that only for some physical exposures the full models performed better. Relative RMS errors ranged between 5% and 19% for the full models, and between 10% and 19% for the practical model. When the predicted physical exposures were classified into low, medium, and high, classification agreement ranged from 26% to 71%. The full prediction models, based on self-reported factors, software-recorded computer usage patterns, and additional measurements of anthropometrics and workstation set-up, show a better predictive quality as compared to the practical models based on self-reported factors and recorded computer usage patterns only. However, predictive quality varied largely across different arm-wrist-hand exposure parameters. Future exploration of the relation between predicted physical exposure and symptoms is therefore only recommended for physical exposures that can be reasonably well predicted. © The Author 2017. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

Saliency image of feature building for image quality assessment

NASA Astrophysics Data System (ADS)

Ju, Xinuo; Sun, Jiyin; Wang, Peng

2011-11-01

The purpose and method of image quality assessment are quite different for automatic target recognition (ATR) and traditional application. Local invariant feature detectors, mainly including corner detectors, blob detectors and region detectors etc., are widely applied for ATR. A saliency model of feature was proposed to evaluate feasibility of ATR in this paper. The first step consisted of computing the first-order derivatives on horizontal orientation and vertical orientation, and computing DoG maps in different scales respectively. Next, saliency images of feature were built based auto-correlation matrix in different scale. Then, saliency images of feature of different scales amalgamated. Experiment were performed on a large test set, including infrared images and optical images, and the result showed that the salient regions computed by this model were consistent with real feature regions computed by mostly local invariant feature extraction algorithms.

A cost-utility analysis of the use of preoperative computed tomographic angiography in abdomen-based perforator flap breast reconstruction.

PubMed

Offodile, Anaeze C; Chatterjee, Abhishek; Vallejo, Sergio; Fisher, Carla S; Tchou, Julia C; Guo, Lifei

2015-04-01

Computed tomographic angiography is a diagnostic tool increasingly used for preoperative vascular mapping in abdomen-based perforator flap breast reconstruction. This study compared the use of computed tomographic angiography and the conventional practice of Doppler ultrasonography only in postmastectomy reconstruction using a cost-utility model. Following a comprehensive literature review, a decision analytic model was created using the three most clinically relevant health outcomes in free autologous breast reconstruction with computed tomographic angiography versus Doppler ultrasonography only. Cost and utility estimates for each health outcome were used to derive the quality-adjusted life-years and incremental cost-utility ratio. One-way sensitivity analysis was performed to scrutinize the robustness of the authors' results. Six studies and 782 patients were identified. Cost-utility analysis revealed a baseline cost savings of $3179, a gain in quality-adjusted life-years of 0.25. This yielded an incremental cost-utility ratio of -$12,716, implying a dominant choice favoring preoperative computed tomographic angiography. Sensitivity analysis revealed that computed tomographic angiography was costlier when the operative time difference between the two techniques was less than 21.3 minutes. However, the clinical advantage of computed tomographic angiography over Doppler ultrasonography only showed that computed tomographic angiography would still remain the cost-effective option even if it offered no additional operating time advantage. The authors' results show that computed tomographic angiography is a cost-effective technology for identifying lower abdominal perforators for autologous breast reconstruction. Although the perfect study would be a randomized controlled trial of the two approaches with true cost accrual, the authors' results represent the best available evidence.

Multi-agent systems in epidemiology: a first step for computational biology in the study of vector-borne disease transmission.

PubMed

Roche, Benjamin; Guégan, Jean-François; Bousquet, François

2008-10-15

Computational biology is often associated with genetic or genomic studies only. However, thanks to the increase of computational resources, computational models are appreciated as useful tools in many other scientific fields. Such modeling systems are particularly relevant for the study of complex systems, like the epidemiology of emerging infectious diseases. So far, mathematical models remain the main tool for the epidemiological and ecological analysis of infectious diseases, with SIR models could be seen as an implicit standard in epidemiology. Unfortunately, these models are based on differential equations and, therefore, can become very rapidly unmanageable due to the too many parameters which need to be taken into consideration. For instance, in the case of zoonotic and vector-borne diseases in wildlife many different potential host species could be involved in the life-cycle of disease transmission, and SIR models might not be the most suitable tool to truly capture the overall disease circulation within that environment. This limitation underlines the necessity to develop a standard spatial model that can cope with the transmission of disease in realistic ecosystems. Computational biology may prove to be flexible enough to take into account the natural complexity observed in both natural and man-made ecosystems. In this paper, we propose a new computational model to study the transmission of infectious diseases in a spatially explicit context. We developed a multi-agent system model for vector-borne disease transmission in a realistic spatial environment. Here we describe in detail the general behavior of this model that we hope will become a standard reference for the study of vector-borne disease transmission in wildlife. To conclude, we show how this simple model could be easily adapted and modified to be used as a common framework for further research developments in this field.

A Causal Model of Teacher Acceptance of Technology

ERIC Educational Resources Information Center

Chang, Jui-Ling; Lieu, Pang-Tien; Liang, Jung-Hui; Liu, Hsiang-Te; Wong, Seng-lee

2012-01-01

This study proposes a causal model for investigating teacher acceptance of technology. We received 258 effective replies from teachers at public and private universities in Taiwan. A questionnaire survey was utilized to test the proposed model. The Lisrel was applied to test the proposed hypotheses. The result shows that computer self-efficacy has…

Hysteresis in the trade cycle

NASA Astrophysics Data System (ADS)

Mc Namara, Hugh A.; Pokrovskii, Alexei V.

2006-02-01

The Kaldor model-one of the first nonlinear models of macroeconomics-is modified to incorporate a Preisach nonlinearity. The new dynamical system thus created shows highly complicated behaviour. This paper presents a rigorous (computer aided) proof of chaos in this new model, and of the existence of unstable periodic orbits of all minimal periods p>57.

Towards a more efficient and robust representation of subsurface hydrological processes in Earth System Models

NASA Astrophysics Data System (ADS)

Rosolem, R.; Rahman, M.; Kollet, S. J.; Wagener, T.

2017-12-01

Understanding the impacts of land cover and climate changes on terrestrial hydrometeorology is important across a range of spatial and temporal scales. Earth System Models (ESMs) provide a robust platform for evaluating these impacts. However, current ESMs lack the representation of key hydrological processes (e.g., preferential water flow, and direct interactions with aquifers) in general. The typical "free drainage" conceptualization of land models can misrepresent the magnitude of those interactions, consequently affecting the exchange of energy and water at the surface as well as estimates of groundwater recharge. Recent studies show the benefits of explicitly simulating the interactions between subsurface and surface processes in similar models. However, such parameterizations are often computationally demanding resulting in limited application for large/global-scale studies. Here, we take a different approach in developing a novel parameterization for groundwater dynamics. Instead of directly adding another complex process to an established land model, we examine a set of comprehensive experimental scenarios using a very robust and establish three-dimensional hydrological model to develop a simpler parameterization that represents the aquifer to land surface interactions. The main goal of our developed parameterization is to simultaneously maximize the computational gain (i.e., "efficiency") while minimizing simulation errors in comparison to the full 3D model (i.e., "robustness") to allow for easy implementation in ESMs globally. Our study focuses primarily on understanding both the dynamics for groundwater recharge and discharge, respectively. Preliminary results show that our proposed approach significantly reduced the computational demand while model deviations from the full 3D model are considered to be small for these processes.

The Lagrangian Ensemble metamodel for simulating plankton ecosystems

NASA Astrophysics Data System (ADS)

Woods, J. D.

2005-10-01

This paper presents a detailed account of the Lagrangian Ensemble (LE) metamodel for simulating plankton ecosystems. It uses agent-based modelling to describe the life histories of many thousands of individual plankters. The demography of each plankton population is computed from those life histories. So too is bio-optical and biochemical feedback to the environment. The resulting “virtual ecosystem” is a comprehensive simulation of the plankton ecosystem. It is based on phenotypic equations for individual micro-organisms. LE modelling differs significantly from population-based modelling. The latter uses prognostic equations to compute demography and biofeedback directly. LE modelling diagnoses them from the properties of individual micro-organisms, whose behaviour is computed from prognostic equations. That indirect approach permits the ecosystem to adjust gracefully to changes in exogenous forcing. The paper starts with theory: it defines the Lagrangian Ensemble metamodel and explains how LE code performs a number of computations “behind the curtain”. They include budgeting chemicals, and deriving biofeedback and demography from individuals. The next section describes the practice of LE modelling. It starts with designing a model that complies with the LE metamodel. Then it describes the scenario for exogenous properties that provide the computation with initial and boundary conditions. These procedures differ significantly from those used in population-based modelling. The next section shows how LE modelling is used in research, teaching and planning. The practice depends largely on hindcasting to overcome the limits to predictability of weather forecasting. The scientific method explains observable ecosystem phenomena in terms of finer-grained processes that cannot be observed, but which are controlled by the basic laws of physics, chemistry and biology. What-If? Prediction ( WIP), used for planning, extends hindcasting by adding events that describe natural or man-made hazards and remedial actions. Verification is based on the Ecological Turing Test, which takes account of uncertainties in the observed and simulated versions of a target ecological phenomenon. The rest of the paper is devoted to a case study designed to show what LE modelling offers the biological oceanographer. The case study is presented in two parts. The first documents the WB model (Woods & Barkmann, 1994) and scenario used to simulate the ecosystem in a mesocosm moored in deep water off the Azores. The second part illustrates the emergent properties of that virtual ecosystem. The behaviour and development of an individual plankton lineage are revealed by an audit trail of the agent used in the computation. The fields of environmental properties reveal the impact of biofeedback. The fields of demographic properties show how changes in individuals cumulatively affect the birth and death rates of their population. This case study documents the virtual ecosystem used by Woods, Perilli and Barkmann (2005; hereafter WPB); to investigate the stability of simulations created by the Lagrangian Ensemble metamodel. The Azores virtual ecosystem was created and analysed on the Virtual Ecology Workbench (VEW) which is described briefly in the Appendix.

Image-based computational fluid dynamics in blood vessel models: toward developing a prognostic tool to assess cardiovascular function changes in prolonged space flights

NASA Astrophysics Data System (ADS)

Chatzimavroudis, George P.; Spirka, Thomas A.; Setser, Randolph M.; Myers, Jerry G.

2005-04-01

One of NASA"s objectives is to be able to perform a complete pre-flight evaluation of possible cardiovascular changes in astronauts scheduled for prolonged space missions. Blood flow is an important component of cardiovascular function. Lately, attention has focused on using computational fluid dynamics (CFD) to analyze flow with realistic vessel geometries. MRI can provide detailed geometrical information and is the only clinical technique to measure all three spatial velocity components. The objective of this study was to investigate the reliability of MRI-based model reconstruction for CFD simulations. An aortic arch model and a carotid bifurcation model were scanned in a 1.5T MRI scanner. Axial MRI acquisitions provided images for geometry reconstruction using different resolution settings. The vessel walls were identified and the geometry was reconstructed using existing software. The geometry was then imported into a commercial CFD package for meshing and numerical solution. MRI velocity acquisitions provided true inlet boundary conditions for steady flow, as well as three-directional velocity data at several locations. In addition, an idealized version of each geometry was created from the model drawings. Contour and vector plots of the velocity showed identical features between the MRI velocity data, the MRI-based CFD data, and the idealized-geometry CFD data, with mean differences <10%. CFD results from different MRI resolution settings did not show significant differences (<5%). This study showed quantitatively that reliable CFD simulations can be performed in models reconstructed from MRI acquisitions and gives evidence that a future, subject-specific, computational evaluation of the cardiovascular system is possible.

Accelerating Computation of DCM for ERP in MATLAB by External Function Calls to the GPU.

PubMed

Wang, Wei-Jen; Hsieh, I-Fan; Chen, Chun-Chuan

2013-01-01

This study aims to improve the performance of Dynamic Causal Modelling for Event Related Potentials (DCM for ERP) in MATLAB by using external function calls to a graphics processing unit (GPU). DCM for ERP is an advanced method for studying neuronal effective connectivity. DCM utilizes an iterative procedure, the expectation maximization (EM) algorithm, to find the optimal parameters given a set of observations and the underlying probability model. As the EM algorithm is computationally demanding and the analysis faces possible combinatorial explosion of models to be tested, we propose a parallel computing scheme using the GPU to achieve a fast estimation of DCM for ERP. The computation of DCM for ERP is dynamically partitioned and distributed to threads for parallel processing, according to the DCM model complexity and the hardware constraints. The performance efficiency of this hardware-dependent thread arrangement strategy was evaluated using the synthetic data. The experimental data were used to validate the accuracy of the proposed computing scheme and quantify the time saving in practice. The simulation results show that the proposed scheme can accelerate the computation by a factor of 155 for the parallel part. For experimental data, the speedup factor is about 7 per model on average, depending on the model complexity and the data. This GPU-based implementation of DCM for ERP gives qualitatively the same results as the original MATLAB implementation does at the group level analysis. In conclusion, we believe that the proposed GPU-based implementation is very useful for users as a fast screen tool to select the most likely model and may provide implementation guidance for possible future clinical applications such as online diagnosis.

Accelerating Computation of DCM for ERP in MATLAB by External Function Calls to the GPU

PubMed Central

Wang, Wei-Jen; Hsieh, I-Fan; Chen, Chun-Chuan

2013-01-01

This study aims to improve the performance of Dynamic Causal Modelling for Event Related Potentials (DCM for ERP) in MATLAB by using external function calls to a graphics processing unit (GPU). DCM for ERP is an advanced method for studying neuronal effective connectivity. DCM utilizes an iterative procedure, the expectation maximization (EM) algorithm, to find the optimal parameters given a set of observations and the underlying probability model. As the EM algorithm is computationally demanding and the analysis faces possible combinatorial explosion of models to be tested, we propose a parallel computing scheme using the GPU to achieve a fast estimation of DCM for ERP. The computation of DCM for ERP is dynamically partitioned and distributed to threads for parallel processing, according to the DCM model complexity and the hardware constraints. The performance efficiency of this hardware-dependent thread arrangement strategy was evaluated using the synthetic data. The experimental data were used to validate the accuracy of the proposed computing scheme and quantify the time saving in practice. The simulation results show that the proposed scheme can accelerate the computation by a factor of 155 for the parallel part. For experimental data, the speedup factor is about 7 per model on average, depending on the model complexity and the data. This GPU-based implementation of DCM for ERP gives qualitatively the same results as the original MATLAB implementation does at the group level analysis. In conclusion, we believe that the proposed GPU-based implementation is very useful for users as a fast screen tool to select the most likely model and may provide implementation guidance for possible future clinical applications such as online diagnosis. PMID:23840507

Integration of Gravitational Torques in Cerebellar Pathways Allows for the Dynamic Inverse Computation of Vertical Pointing Movements of a Robot Arm

PubMed Central

Gentili, Rodolphe J.; Papaxanthis, Charalambos; Ebadzadeh, Mehdi; Eskiizmirliler, Selim; Ouanezar, Sofiane; Darlot, Christian

2009-01-01

Background Several authors suggested that gravitational forces are centrally represented in the brain for planning, control and sensorimotor predictions of movements. Furthermore, some studies proposed that the cerebellum computes the inverse dynamics (internal inverse model) whereas others suggested that it computes sensorimotor predictions (internal forward model). Methodology/Principal Findings This study proposes a model of cerebellar pathways deduced from both biological and physical constraints. The model learns the dynamic inverse computation of the effect of gravitational torques from its sensorimotor predictions without calculating an explicit inverse computation. By using supervised learning, this model learns to control an anthropomorphic robot arm actuated by two antagonists McKibben artificial muscles. This was achieved by using internal parallel feedback loops containing neural networks which anticipate the sensorimotor consequences of the neural commands. The artificial neural networks architecture was similar to the large-scale connectivity of the cerebellar cortex. Movements in the sagittal plane were performed during three sessions combining different initial positions, amplitudes and directions of movements to vary the effects of the gravitational torques applied to the robotic arm. The results show that this model acquired an internal representation of the gravitational effects during vertical arm pointing movements. Conclusions/Significance This is consistent with the proposal that the cerebellar cortex contains an internal representation of gravitational torques which is encoded through a learning process. Furthermore, this model suggests that the cerebellum performs the inverse dynamics computation based on sensorimotor predictions. This highlights the importance of sensorimotor predictions of gravitational torques acting on upper limb movements performed in the gravitational field. PMID:19384420

Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity

NASA Astrophysics Data System (ADS)

Li, Xiumin; Wang, Wei; Xue, Fangzheng; Song, Yongduan

2018-02-01

Recently there has been continuously increasing interest in building up computational models of spiking neural networks (SNN), such as the Liquid State Machine (LSM). The biologically inspired self-organized neural networks with neural plasticity can enhance the capability of computational performance, with the characteristic features of dynamical memory and recurrent connection cycles which distinguish them from the more widely used feedforward neural networks. Despite a variety of computational models for brain-like learning and information processing have been proposed, the modeling of self-organized neural networks with multi-neural plasticity is still an important open challenge. The main difficulties lie in the interplay among different forms of neural plasticity rules and understanding how structures and dynamics of neural networks shape the computational performance. In this paper, we propose a novel approach to develop the models of LSM with a biologically inspired self-organizing network based on two neural plasticity learning rules. The connectivity among excitatory neurons is adapted by spike-timing-dependent plasticity (STDP) learning; meanwhile, the degrees of neuronal excitability are regulated to maintain a moderate average activity level by another learning rule: intrinsic plasticity (IP). Our study shows that LSM with STDP+IP performs better than LSM with a random SNN or SNN obtained by STDP alone. The noticeable improvement with the proposed method is due to the better reflected competition among different neurons in the developed SNN model, as well as the more effectively encoded and processed relevant dynamic information with its learning and self-organizing mechanism. This result gives insights to the optimization of computational models of spiking neural networks with neural plasticity.

Finite element strategies to satisfy clinical and engineering requirements in the field of percutaneous valves.

PubMed

Capelli, Claudio; Biglino, Giovanni; Petrini, Lorenza; Migliavacca, Francesco; Cosentino, Daria; Bonhoeffer, Philipp; Taylor, Andrew M; Schievano, Silvia

2012-12-01

Finite element (FE) modelling can be a very resourceful tool in the field of cardiovascular devices. To ensure result reliability, FE models must be validated experimentally against physical data. Their clinical application (e.g., patients' suitability, morphological evaluation) also requires fast simulation process and access to results, while engineering applications need highly accurate results. This study shows how FE models with different mesh discretisations can suit clinical and engineering requirements for studying a novel device designed for percutaneous valve implantation. Following sensitivity analysis and experimental characterisation of the materials, the stent-graft was first studied in a simplified geometry (i.e., compliant cylinder) and validated against in vitro data, and then in a patient-specific implantation site (i.e., distensible right ventricular outflow tract). Different meshing strategies using solid, beam and shell elements were tested. Results showed excellent agreement between computational and experimental data in the simplified implantation site. Beam elements were found to be convenient for clinical applications, providing reliable results in less than one hour in a patient-specific anatomical model. Solid elements remain the FE choice for engineering applications, albeit more computationally expensive (>100 times). This work also showed how information on device mechanical behaviour differs when acquired in a simplified model as opposed to a patient-specific model.

Application of reduced order modeling techniques to problems in heat conduction, isoelectric focusing and differential algebraic equations

NASA Astrophysics Data System (ADS)

Mathai, Pramod P.

This thesis focuses on applying and augmenting 'Reduced Order Modeling' (ROM) techniques to large scale problems. ROM refers to the set of mathematical techniques that are used to reduce the computational expense of conventional modeling techniques, like finite element and finite difference methods, while minimizing the loss of accuracy that typically accompanies such a reduction. The first problem that we address pertains to the prediction of the level of heat dissipation in electronic and MEMS devices. With the ever decreasing feature sizes in electronic devices, and the accompanied rise in Joule heating, the electronics industry has, since the 1990s, identified a clear need for computationally cheap heat transfer modeling techniques that can be incorporated along with the electronic design process. We demonstrate how one can create reduced order models for simulating heat conduction in individual components that constitute an idealized electronic device. The reduced order models are created using Krylov Subspace Techniques (KST). We introduce a novel 'plug and play' approach, based on the small gain theorem in control theory, to interconnect these component reduced order models (according to the device architecture) to reliably and cheaply replicate whole device behavior. The final aim is to have this technique available commercially as a computationally cheap and reliable option that enables a designer to optimize for heat dissipation among competing VLSI architectures. Another place where model reduction is crucial to better design is Isoelectric Focusing (IEF) - the second problem in this thesis - which is a popular technique that is used to separate minute amounts of proteins from the other constituents that are present in a typical biological tissue sample. Fundamental questions about how to design IEF experiments still remain because of the high dimensional and highly nonlinear nature of the differential equations that describe the IEF process as well as the uncertainty in the parameters of the differential equations. There is a clear need to design better experiments for IEF without the current overhead of expensive chemicals and labor. We show how with a simpler modeling of the underlying chemistry, we can still achieve the accuracy that has been achieved in existing literature for modeling small ranges of pH (hydrogen ion concentration) in IEF, but with far less computational time. We investigate a further reduction of time by modeling the IEF problem using the Proper Orthogonal Decomposition (POD) technique and show why POD may not be sufficient due to the underlying constraints. The final problem that we address in this thesis addresses a certain class of dynamics with high stiffness - in particular, differential algebraic equations. With the help of simple examples, we show how the traditional POD procedure will fail to model certain high stiffness problems due to a particular behavior of the vector field which we will denote as twist. We further show how a novel augmentation to the traditional POD algorithm can model-reduce problems with twist in a computationally cheap manner without any additional data requirements.

Predicting Flow Reversals in a Computational Fluid Dynamics Simulated Thermosyphon Using Data Assimilation.

PubMed

Reagan, Andrew J; Dubief, Yves; Dodds, Peter Sheridan; Danforth, Christopher M

2016-01-01

A thermal convection loop is a annular chamber filled with water, heated on the bottom half and cooled on the top half. With sufficiently large forcing of heat, the direction of fluid flow in the loop oscillates chaotically, dynamics analogous to the Earth's weather. As is the case for state-of-the-art weather models, we only observe the statistics over a small region of state space, making prediction difficult. To overcome this challenge, data assimilation (DA) methods, and specifically ensemble methods, use the computational model itself to estimate the uncertainty of the model to optimally combine these observations into an initial condition for predicting the future state. Here, we build and verify four distinct DA methods, and then, we perform a twin model experiment with the computational fluid dynamics simulation of the loop using the Ensemble Transform Kalman Filter (ETKF) to assimilate observations and predict flow reversals. We show that using adaptively shaped localized covariance outperforms static localized covariance with the ETKF, and allows for the use of less observations in predicting flow reversals. We also show that a Dynamic Mode Decomposition (DMD) of the temperature and velocity fields recovers the low dimensional system underlying reversals, finding specific modes which together are predictive of reversal direction.

Predicting Flow Reversals in a Computational Fluid Dynamics Simulated Thermosyphon Using Data Assimilation

PubMed Central

Reagan, Andrew J.; Dubief, Yves; Dodds, Peter Sheridan; Danforth, Christopher M.

2016-01-01

A thermal convection loop is a annular chamber filled with water, heated on the bottom half and cooled on the top half. With sufficiently large forcing of heat, the direction of fluid flow in the loop oscillates chaotically, dynamics analogous to the Earth’s weather. As is the case for state-of-the-art weather models, we only observe the statistics over a small region of state space, making prediction difficult. To overcome this challenge, data assimilation (DA) methods, and specifically ensemble methods, use the computational model itself to estimate the uncertainty of the model to optimally combine these observations into an initial condition for predicting the future state. Here, we build and verify four distinct DA methods, and then, we perform a twin model experiment with the computational fluid dynamics simulation of the loop using the Ensemble Transform Kalman Filter (ETKF) to assimilate observations and predict flow reversals. We show that using adaptively shaped localized covariance outperforms static localized covariance with the ETKF, and allows for the use of less observations in predicting flow reversals. We also show that a Dynamic Mode Decomposition (DMD) of the temperature and velocity fields recovers the low dimensional system underlying reversals, finding specific modes which together are predictive of reversal direction. PMID:26849061

Digital computer simulation of inductor-energy-storage dc-to-dc converters with closed-loop regulators

NASA Technical Reports Server (NTRS)

Ohri, A. K.; Owen, H. A.; Wilson, T. G.; Rodriguez, G. E.

1974-01-01

The simulation of converter-controller combinations by means of a flexible digital computer program which produces output to a graphic display is discussed. The procedure is an alternative to mathematical analysis of converter systems. The types of computer programming involved in the simulation are described. Schematic diagrams, state equations, and output equations are displayed for four basic forms of inductor-energy-storage dc to dc converters. Mathematical models are developed to show the relationship of the parameters.

Effect of correlated decay on fault-tolerant quantum computation

NASA Astrophysics Data System (ADS)

Lemberger, B.; Yavuz, D. D.

2017-12-01

We analyze noise in the circuit model of quantum computers when the qubits are coupled to a common bosonic bath and discuss the possible failure of scalability of quantum computation. Specifically, we investigate correlated (super-radiant) decay between the qubit energy levels from a two- or three-dimensional array of qubits without imposing any restrictions on the size of the sample. We first show that regardless of how the spacing between the qubits compares with the emission wavelength, correlated decay produces errors outside the applicability of the threshold theorem. This is because the sum of the norms of the two-body interaction Hamiltonians (which can be viewed as the upper bound on the single-qubit error) that decoheres each qubit scales with the total number of qubits and is unbounded. We then discuss two related results: (1) We show that the actual error (instead of the upper bound) on each qubit scales with the number of qubits. As a result, in the limit of large number of qubits in the computer, N →∞ , correlated decay causes each qubit in the computer to decohere in ever shorter time scales. (2) We find the complete eigenvalue spectrum of the exchange Hamiltonian that causes correlated decay in the same limit. We show that the spread of the eigenvalue distribution grows faster with N compared to the spectrum of the unperturbed system Hamiltonian. As a result, as N →∞ , quantum evolution becomes completely dominated by the noise due to correlated decay. These results argue that scalable quantum computing may not be possible in the circuit model in a two- or three- dimensional geometry when the qubits are coupled to a common bosonic bath.

A high-order multiscale finite-element method for time-domain acoustic-wave modeling

NASA Astrophysics Data System (ADS)

Gao, Kai; Fu, Shubin; Chung, Eric T.

2018-05-01

Accurate and efficient wave equation modeling is vital for many applications in such as acoustics, electromagnetics, and seismology. However, solving the wave equation in large-scale and highly heterogeneous models is usually computationally expensive because the computational cost is directly proportional to the number of grids in the model. We develop a novel high-order multiscale finite-element method to reduce the computational cost of time-domain acoustic-wave equation numerical modeling by solving the wave equation on a coarse mesh based on the multiscale finite-element theory. In contrast to existing multiscale finite-element methods that use only first-order multiscale basis functions, our new method constructs high-order multiscale basis functions from local elliptic problems which are closely related to the Gauss-Lobatto-Legendre quadrature points in a coarse element. Essentially, these basis functions are not only determined by the order of Legendre polynomials, but also by local medium properties, and therefore can effectively convey the fine-scale information to the coarse-scale solution with high-order accuracy. Numerical tests show that our method can significantly reduce the computation time while maintain high accuracy for wave equation modeling in highly heterogeneous media by solving the corresponding discrete system only on the coarse mesh with the new high-order multiscale basis functions.

A high-order multiscale finite-element method for time-domain acoustic-wave modeling

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

Gao, Kai; Fu, Shubin; Chung, Eric T.

Accurate and efficient wave equation modeling is vital for many applications in such as acoustics, electromagnetics, and seismology. However, solving the wave equation in large-scale and highly heterogeneous models is usually computationally expensive because the computational cost is directly proportional to the number of grids in the model. We develop a novel high-order multiscale finite-element method to reduce the computational cost of time-domain acoustic-wave equation numerical modeling by solving the wave equation on a coarse mesh based on the multiscale finite-element theory. In contrast to existing multiscale finite-element methods that use only first-order multiscale basis functions, our new method constructsmore » high-order multiscale basis functions from local elliptic problems which are closely related to the Gauss–Lobatto–Legendre quadrature points in a coarse element. Essentially, these basis functions are not only determined by the order of Legendre polynomials, but also by local medium properties, and therefore can effectively convey the fine-scale information to the coarse-scale solution with high-order accuracy. Numerical tests show that our method can significantly reduce the computation time while maintain high accuracy for wave equation modeling in highly heterogeneous media by solving the corresponding discrete system only on the coarse mesh with the new high-order multiscale basis functions.« less

A high-order multiscale finite-element method for time-domain acoustic-wave modeling

DOE PAGES

Gao, Kai; Fu, Shubin; Chung, Eric T.

2018-02-04

Accurate and efficient wave equation modeling is vital for many applications in such as acoustics, electromagnetics, and seismology. However, solving the wave equation in large-scale and highly heterogeneous models is usually computationally expensive because the computational cost is directly proportional to the number of grids in the model. We develop a novel high-order multiscale finite-element method to reduce the computational cost of time-domain acoustic-wave equation numerical modeling by solving the wave equation on a coarse mesh based on the multiscale finite-element theory. In contrast to existing multiscale finite-element methods that use only first-order multiscale basis functions, our new method constructsmore » high-order multiscale basis functions from local elliptic problems which are closely related to the Gauss–Lobatto–Legendre quadrature points in a coarse element. Essentially, these basis functions are not only determined by the order of Legendre polynomials, but also by local medium properties, and therefore can effectively convey the fine-scale information to the coarse-scale solution with high-order accuracy. Numerical tests show that our method can significantly reduce the computation time while maintain high accuracy for wave equation modeling in highly heterogeneous media by solving the corresponding discrete system only on the coarse mesh with the new high-order multiscale basis functions.« less

Sound transmission in the chest under surface excitation - An experimental and computational study with diagnostic applications

PubMed Central

Peng, Ying; Dai, Zoujun; Mansy, Hansen A.; Sandler, Richard H.; Balk, Robert A; Royston, Thomas. J

2014-01-01

Chest physical examination often includes performing chest percussion, which involves introducing sound stimulus to the chest wall and detecting an audible change. This approach relies on observations that underlying acoustic transmission, coupling, and resonance patterns can be altered by chest structure changes due to pathologies. More accurate detection and quantification of these acoustic alterations may provide further useful diagnostic information. To elucidate the physical processes involved, a realistic computer model of sound transmission in the chest is helpful. In the present study, a computational model was developed and validated by comparing its predictions with results from animal and human experiments which involved applying acoustic excitation to the anterior chest while detecting skin vibrations at the posterior chest. To investigate the effect of pathology on sound transmission, the computational model was used to simulate the effects of pneumothorax on sounds introduced at the anterior chest and detected at the posterior. Model predictions and experimental results showed similar trends. The model also predicted wave patterns inside the chest, which may be used to assess results of elastography measurements. Future animal and human tests may expand the predictive power of the model to include acoustic behavior for a wider range of pulmonary conditions. PMID:25001497

Intrinsic two-dimensional features as textons

NASA Technical Reports Server (NTRS)

Barth, E.; Zetzsche, C.; Rentschler, I.

1998-01-01

We suggest that intrinsic two-dimensional (i2D) features, computationally defined as the outputs of nonlinear operators that model the activity of end-stopped neurons, play a role in preattentive texture discrimination. We first show that for discriminable textures with identical power spectra the predictions of traditional models depend on the type of nonlinearity and fail for energy measures. We then argue that the concept of intrinsic dimensionality, and the existence of end-stopped neurons, can help us to understand the role of the nonlinearities. Furthermore, we show examples in which models without strong i2D selectivity fail to predict the correct ranking order of perceptual segregation. Our arguments regarding the importance of i2D features resemble the arguments of Julesz and co-workers regarding textons such as terminators and crossings. However, we provide a computational framework that identifies textons with the outputs of nonlinear operators that are selective to i2D features.

Multilayer shallow water models with locally variable number of layers and semi-implicit time discretization

NASA Astrophysics Data System (ADS)

Bonaventura, Luca; Fernández-Nieto, Enrique D.; Garres-Díaz, José; Narbona-Reina, Gladys

2018-07-01

We propose an extension of the discretization approaches for multilayer shallow water models, aimed at making them more flexible and efficient for realistic applications to coastal flows. A novel discretization approach is proposed, in which the number of vertical layers and their distribution are allowed to change in different regions of the computational domain. Furthermore, semi-implicit schemes are employed for the time discretization, leading to a significant efficiency improvement for subcritical regimes. We show that, in the typical regimes in which the application of multilayer shallow water models is justified, the resulting discretization does not introduce any major spurious feature and allows again to reduce substantially the computational cost in areas with complex bathymetry. As an example of the potential of the proposed technique, an application to a sediment transport problem is presented, showing a remarkable improvement with respect to standard discretization approaches.

Impact of airway morphological changes on pulmonary flows in scoliosis

NASA Astrophysics Data System (ADS)

Farrell, James; Garrido, Enrique; Valluri, Prashant

2016-11-01

The relationship between thoracic deformity in scoliosis and lung function is poorly understood. In a pilot study, we reviewed computed tomography (CT) routine scans of patients undergoing scoliosis surgery. The CT scans were processed to segment the anatomy of the airways, lung and spine. A three-dimensional model was created to study the anatomical relationship. Preliminary analysis showed significant airway morphological differences depending on the anterior position of the spine. A computational fluid dynamics (CFD) study was also conducted on the airway geometry using the inspiratory scans. The CFD model assuming non-compliant airway walls was capable of showing pressure drops in areas of high airway resistance, but was unable to predict regional ventilation differences. Our results indicate a dependence between the dynamic deformation of the airway during breathing and lung function. Dynamic structural deformation must therefore be incorporated within any modelling approaches to guide clinicians on the decision to perform surgical correction of the scoliosis.

Effects of scale and Froude number on the hydraulics of waste stabilization ponds.

PubMed

Vieira, Isabela De Luna; Da Silva, Jhonatan Barbosa; Ide, Carlos Nobuyoshi; Janzen, Johannes Gérson

2018-01-01

This paper presents the findings from a series of computational fluid dynamics simulations to estimate the effect of scale and Froude number on hydraulic performance and effluent pollutant fraction of scaled waste stabilization ponds designed using Froude similarity. Prior to its application, the model was verified by comparing the computational and experimental results of a model scaled pond, showing good agreement and confirming that the model accurately reproduces the hydrodynamics and tracer transport processes. Our results showed that the scale and the interaction between scale and Froude number has an effect on the hydraulics of ponds. At 1:5 scale, the increase of scale increased short-circuiting and decreased mixing. Furthermore, at 1:10 scale, the increase of scale decreased the effluent pollutant fraction. Since the Reynolds effect cannot be ignored, a ratio of Reynolds and Froude numbers was suggested to predict the effluent pollutant fraction for flows with different Reynolds numbers.

Simulation Based Exploration of Critical Zone Dynamics in Intensively Managed Landscapes

NASA Astrophysics Data System (ADS)

Kumar, P.

2017-12-01

The advent of high-resolution measurements of topographic and (vertical) vegetation features using areal LiDAR are enabling us to resolve micro-scale ( 1m) landscape structural characteristics over large areas. Availability of hyperspectral measurements is further augmenting these LiDAR data by enabling the biogeochemical characterization of vegetation and soils at unprecedented spatial resolutions ( 1-10m). Such data have opened up novel opportunities for modeling Critical Zone processes and exploring questions that were not possible before. We show how an integrated 3-D model at 1m grid resolution can enable us to resolve micro-topographic and ecological dynamics and their control on hydrologic and biogeochemical processes over large areas. We address the computational challenge of such detailed modeling by exploiting hybrid CPU and GPU computing technologies. We show results of moisture, biogeochemical, and vegetation dynamics from studies in the Critical Zone Observatory for Intensively managed Landscapes (IMLCZO) in the Midwestern United States.