684,659 research outputs found
Virtual integration platform for computational fluid dynamics
Computational Fluid Dynamics (CFD) tools used in shipbuilding industry involve multiple disciplines, such as resistance, manoeuvring, and cavitation. Traditionally, the analysis was performed separately and sequentially in each discipline, which often resulted in conflict and inconsistency of hydrodynamic prediction. In an effort to solve such problems for future CFD computations, a Virtual Integration Platform (VIP) has been developed in the University of Strathclyde within two EU FP6 projects - VIRTUE and SAFEDOR1. The VIP provides a holistic collaborative environment for designers with features such as Project/Process Management, Distributed Tools Integration, Global Optimisation, Version Management, and Knowledge Management. These features enhance collaboration among customers, ship design companies, shipyards, and consultancies not least because they bring together the best expertise and resources around the world. The platform has been tested in seven European ship design companies including consultancies. Its main functionalities along with advances are presented in this paper with two industrial applications
Dendritic inhibition enhances neural coding properties.
The presence of a large number of inhibitory contacts at the soma and axon
initial segment of cortical pyramidal cells has inspired a large and influential
class of neural network model which use post-integration lateral inhibition as a
mechanism for competition between nodes. However, inhibitory synapses also
target the dendrites of pyramidal cells. The role of this dendritic inhibition
in competition between neurons has not previously been addressed. We
demonstrate, using a simple computational model, that such pre-integration
lateral inhibition provides networks of neurons with useful representational and
computational properties which are not provided by post-integration
inhibition
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Neurons and symbols: a manifesto
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of
neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty
Individual differences in human path integration abilities correlate with gray matter volume in retrosplenial cortex, hippocampus, and medial prefrontal cortex
Humans differ in their individual navigational abilities. These individual differences may exist in part because successful navigation relies on several disparate abilities, which rely on different brain structures. One such navigational capability is path integration, the updating of position and orientation, in which navigators track distances, directions, and locations in space during movement. Although structural differences related to landmark-based navigation have been examined, gray matter volume related to path integration ability has not yet been tested. Here, we examined individual differences in two path integration paradigms: (1) a location tracking task and (2) a task tracking translational and rotational self-motion. Using voxel-based morphometry, we related differences in performance in these path integration tasks to variation in brain morphology in 26 healthy young adults. Performance in the location tracking task positively correlated with individual differences in gray matter volume in three areas critical for path integration: the hippocampus, the retrosplenial cortex, and the medial prefrontal cortex. These regions are consistent with the path integration system known from computational and animal models and provide novel evidence that morphological variability in retrosplenial and medial prefrontal cortices underlies individual differences in human path integration ability. The results for tracking rotational self-motion-but not translation or location-demonstrated that cerebellum gray matter volume correlated with individual performance. Our findings also suggest that these three aspects of path integration are largely independent. Together, the results of this study provide a link between individual abilities and the functional correlates, computational models, and animal models of path integration
On the robust determination of eigenmodes in 2D stratified wave guiding systems with Berenger-type PML's.
A contour integration method is presented to determine the eigenmodes in a layered structure closed with PML layers at the boundaries of the computational window. Improvements are provided to ensure the accuracy of contour integration and to assure that all the encircled eigenvalues are determined. Numerical examples are presented to test the accuracy of the method
High-Order Numerical Solution of Second-Order One-Dimensional Hyperbolic Telegraph Equation Using a Shifted Gegenbauer Pseudospectral Method
We present a high-order shifted Gegenbauer pseudospectral method (SGPM) to
solve numerically the second-order one-dimensional hyperbolic telegraph
equation provided with some initial and Dirichlet boundary conditions. The
framework of the numerical scheme involves the recast of the problem into its
integral formulation followed by its discretization into a system of
well-conditioned linear algebraic equations. The integral operators are
numerically approximated using some novel shifted Gegenbauer operational
matrices of integration. We derive the error formula of the associated
numerical quadratures. We also present a method to optimize the constructed
operational matrix of integration by minimizing the associated quadrature error
in some optimality sense. We study the error bounds and convergence of the
optimal shifted Gegenbauer operational matrix of integration. Moreover, we
construct the relation between the operational matrices of integration of the
shifted Gegenbauer polynomials and standard Gegenbauer polynomials. We derive
the global collocation matrix of the SGPM, and construct an efficient
computational algorithm for the solution of the collocation equations. We
present a study on the computational cost of the developed computational
algorithm, and a rigorous convergence and error analysis of the introduced
method. Four numerical test examples have been carried out in order to verify
the effectiveness, the accuracy, and the exponential convergence of the method.
The SGPM is a robust technique, which can be extended to solve a wide range of
problems arising in numerous applications.Comment: 36 pages, articl
MetaboTools: A comprehensive toolbox for analysis of genome-scale metabolic models
Metabolomic data sets provide a direct read-out of cellular phenotypes and
are increasingly generated to study biological questions. Our previous work
revealed the potential of analyzing extracellular metabolomic data in the
context of the metabolic model using constraint-based modeling. Through this
work, which consists of a protocol, a toolbox, and tutorials of two use cases,
we make our methods available to the broader scientific community. The protocol
describes, in a step-wise manner, the workflow of data integration and
computational analysis. The MetaboTools comprise the Matlab code required to
complete the workflow described in the protocol. Tutorials explain the
computational steps for integration of two different data sets and demonstrate
a comprehensive set of methods for the computational analysis of metabolic
models and stratification thereof into different phenotypes. The presented
workflow supports integrative analysis of multiple omics data sets.
Importantly, all analysis tools can be applied to metabolic models without
performing the entire workflow. Taken together, this protocol constitutes a
comprehensive guide to the intra-model analysis of extracellular metabolomic
data and a resource offering a broad set of computational analysis tools for a
wide biomedical and non-biomedical research community
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