Steady-state traffic flow on a ring road with up- and down- slopes

This paper studies steady-state traffic flow on a ring road with up- and down- slopes using a semi-discrete model. By exploiting the relations between the semi-discrete and the continuum models, a steady-state solution is uniquely determined for a given total number of vehicles on the ring road. The solution is exact and always stable with respect to the first-order continuum model, whereas it is a good approximation with respect to the semi-discrete model provided that the involved equilibrium constant states are linearly stable. In an otherwise case, the instability of one or more equilibria could trigger stop-and-go waves propagating in certain road sections or throughout the ring road. The indicated results are reasonable and thus physically significant for a better understanding of real traffic flow on an inhomogeneous road

A novel approach to modelling and simulating the contact behaviour between a human hand model and a deformable object

A deeper understanding of biomechanical behaviour of human hands becomes fundamental for any human hand-operated Q2 activities. The integration of biomechanical knowledge of human hands into product design process starts to play an increasingly important role in developing an ergonomic product-to-user interface for products and systems requiring high level of comfortable and responsive interactions. Generation of such precise and dynamic models can provide scientific evaluation tools to support product and system development through simulation. This type of support is urgently required in many applications such as hand skill training for surgical operations, ergonomic study of a product or system developed and so forth. The aim of this work is to study the contact behaviour between the operators’ hand and a hand-held tool or other similar contacts, by developing a novel and precise nonlinear 3D finite element model of the hand and by investigating the contact behaviour through simulation. The contact behaviour is externalised by solving the problem using the bi-potential method. The human body’s biomechanical characteristics, such as hand deformity and structural behaviour, have been fully modelled by implementing anisotropic hyperelastic laws. A case study is given to illustrate the effectiveness of the approac

Matrix Tensor Product Approach to the Equivalence of Multipartite States under Local Unitary Transformations

The equivalence of multipartite quantum mixed states under local unitary transformations is studied. A criterion for the equivalence of non-degenerate mixed multipartite quantum states under local unitary transformations is presented.Comment: 7 page

Effects of Polysaccharides from Gynostemma Pentaphyllum (Thunb.), Makino on Physical Fatigue

Background: Gynostemma pentaphyllum (Thunb.) Makino has been reported to have a wide range of health benefits in Chinese herbal medicines. Polysaccharides from Gynostemma pentaphyllum (PGP), has been identified as one of the active ingredients responsible for its biological activities. Although many pharmacological activities of PGP have received a great deal of attention, there is limited evidence for the anti-fatigue effects of PGP. The purpose of this study was to investigate the effects of polysaccharides from PGP on physical fatigue.Materials and method: The rats were divided into four groups, with 10 animals per group: control (C), group, low-treated (LT), group, mediumtreated (MT), group, and high-treated (HT), group. The C group received distilled water, while LT, MT and HT groups were given various doses of PGP (100, 200, 400 mg/kg· d). After 30 days, forced swimming test was carried out in an acrylic plastic pool, then the exhaustive swimming time of rats and some biochemical parameters related to fatigue were measured. The data obtained showed that PGP could extend the exhaustive swimming time of the rats, as well as decrease the blood lactic acid (BLA), and blood urea nitrogen (BUN), concentrations, and increase the hemoglobin, liver glycogen and muscle glycogen concentrations.Result: The data obtained showed that different doses of PGP could extend the exhaustive swimming time of the rats, as well as decrease the BLA and BUN concentrations, and increase the hemoglobin, liver glycogen and muscle glycogen concentrations, which suggests that PGP had significant anti-fatigue effects on rats.Conclusion: PGP may be of use as a potential anti-fatigue agent, but there is a need for further research on long-term use in order to show its positive effects on physical fatigue.Key words: polysaccharides from Gynostemma pentaphyllum (Thunb.) Makino; physical fatigue; forced swimming test; rat

Computing one-bit compressive sensing via double-sparsity constrained optimization

One-bit compressive sensing is popular in signal processing and communications due to the advantage of its low storage costs and hardware complexity. However, it has been a challenging task all along since only the one-bit (the sign) information is available to recover the signal. In this paper, we appropriately formulate the one-bit compressed sensing by a double-sparsity constrained optimization problem. The first-order optimality conditions via the newly introduced τ-stationarity for this nonconvex and discontinuous problem are established, based on which, a gradient projection subspace pursuit (GPSP) approach with global convergence and fast convergence rate is proposed. Numerical experiments against other leading solvers illustrate the high efficiency of our proposed algorithm in terms of the computation time and the quality of the signal recovery as well

High-Dimensional Stochastic Design Optimization by Adaptive-Sparse Polynomial Dimensional Decomposition

This paper presents a novel adaptive-sparse polynomial dimensional decomposition (PDD) method for stochastic design optimization of complex systems. The method entails an adaptive-sparse PDD approximation of a high-dimensional stochastic response for statistical moment and reliability analyses; a novel integration of the adaptive-sparse PDD approximation and score functions for estimating the first-order design sensitivities of the statistical moments and failure probability; and standard gradient-based optimization algorithms. New analytical formulae are presented for the design sensitivities that are simultaneously determined along with the moments or the failure probability. Numerical results stemming from mathematical functions indicate that the new method provides more computationally efficient design solutions than the existing methods. Finally, stochastic shape optimization of a jet engine bracket with 79 variables was performed, demonstrating the power of the new method to tackle practical engineering problems.Comment: 18 pages, 2 figures, to appear in Sparse Grids and Applications--Stuttgart 2014, Lecture Notes in Computational Science and Engineering 109, edited by J. Garcke and D. Pfl\"{u}ger, Springer International Publishing, 201

Complete Characterizations of Local Weak Sharp Minima With Applications to Semi-Infinite Optimization and Complementarity

In this paper we identify a favorable class of nonsmooth functions for which local weak sharp minima can be completely characterized in terms of normal cones and subdifferentials, or tangent cones and subderivatives, or their mixture in finite-dimensional spaces. The results obtained not only significantly extend previous ones in the literature, but also allow us to provide new types of criteria for local weak sharpness. Applications of the developed theory are given to semi-infinite programming and to semi-infinite complementarity problems

Global and quadratic convergence of Newton hard-thresholding pursuit

Algorithms based on the hard thresholding principle have been well studied with sounding theoretical guarantees in the compressed sensing and more general sparsity-constrained optimization. It is widely observed in existing empirical studies that when a restricted Newton step was used (as the debiasing step), the hard-thresholding algorithms tend to meet halting conditions in a significantly low number of iterations and are very efficient. Hence, the thus obtained Newton hard-thresholding algorithms call for stronger theoretical guarantees than for their simple hard-thresholding counterparts. This paper provides a theoretical justification for the use of the restricted Newton step. We build our theory and algorithm, Newton Hard-Thresholding Pursuit (NHTP), for the sparsity-constrained optimization. Our main result shows that NHTP is quadratically convergent under the standard assumption of restricted strong convexity and smoothness. We also establish its global convergence to a stationary point under a weaker assumption. In the special case of the compressive sensing, NHTP effectively reduces to some of the existing hard-thresholding algorithms with a Newton step. Consequently, our fast convergence result justifies why those algorithms perform better than without the Newton step. The efficiency of NHTP was demonstrated on both synthetic and real data in compressed sensing and sparse logistic regression