Strange attractors in periodically-kicked degenerate Hopf bifurcations

We prove that spiral sinks (stable foci of vector fields) can be transformed into strange attractors exhibiting sustained, observable chaos if subjected to periodic pulsatile forcing. We show that this phenomenon occurs in the context of periodically-kicked degenerate supercritical Hopf bifurcations. The results and their proofs make use of a new multi-parameter version of the theory of rank one maps developed by Wang and Young.Comment: 16 page

Symmetric duality for a class of nondifferentiable multi-objective fractional variational problems

AbstractWe introduce a symmetric dual pair for a class of nondifferentiable multi-objective fractional variational problems. Weak, strong, converse and self duality relations are established under certain invexity assumptions. The paper includes extensions of previous symmetric duality results for multi-objective fractional variational problems obtained by Kim, Lee and Schaible [D.S. Kim, W.J. Lee, S. Schaible, Symmetric duality for invex multiobjective fractional variational problems, J. Math. Anal. Appl. 289 (2004) 505–521] and symmetric duality results for the static case obtained by Yang, Wang and Deng [X.M. Yang, S.Y. Wang, X.T. Deng, Symmetric duality for a class of multiobjective fractional programming problems, J. Math. Anal. Appl. 274 (2002) 279–295] to the dynamic case

From limit cycles to strange attractors

We define a quantitative notion of shear for limit cycles of flows. We prove that strange attractors and SRB measures emerge when systems exhibiting limit cycles with sufficient shear are subjected to periodic pulsatile drives. The strange attractors possess a number of precisely-defined dynamical properties that together imply chaos that is both sustained in time and physically observable.Comment: 27 page

Characterisation and use of glass fibre reinforced plastic waste powder as filler in styrene-butadiene rubber

Glass fibre reinforced plastic (GRP) wastes are often disposed of in landfill, incinerated or processed into powders. GRP waste powders can be recycled as filler in virgin polymers and should be characterised before they are added to avoid processing problems. A GRP waste powder was characterised using advanced measuring and analytical techniques. These included, scanning electron microscopy, Fourier transform infrared spectrometry, particle size analyser, differential scanning calorimetry, X-ray photo-electron spectroscopy and energy dispersive X-ray microanalyser. The results showed that the waste powder consisted of irregular shaped particles and glass fibre fragments up to 700 m in size. Moreover, the waste powder was a thermoset polyester resin and its chemical constituents were calcium, oxygen, aluminium, silica, chlorine, bromine and carbon. When up to 25 parts per hundred rubber by weight of the GRP waste powder was mixed with a sulphur cure- based styrene-butadiene rubber, the viscosity, scorch and optimum cure times increased, and the rate of cure decreased. The tearing energy, elongation at break, tensile strength, stored energy density at break, and Young’s modulus of the vulcanisate improved as the loading of the waste powder was raised

Characterisation and use of glass fibre reinforced plastic waste powder as filler in styrene-butadiene rubber

Glass fibre reinforced plastic (GRP) wastes are often disposed of in landfill, incinerated or processed into powders. GRP waste powders can be recycled as filler in virgin polymers and should be characterised before they are added to avoid processing problems. A GRP waste powder was characterised using advanced measuring and analytical techniques. These included, scanning electron microscopy, Fourier transform infrared spectrometry, particle size analyser, differential scanning calorimetry, X-ray photo-electron spectroscopy and energy dispersive X-ray microanalyser. The results showed that the waste powder consisted of irregular shaped particles and glass fibre fragments up to 700 m in size. Moreover, the waste powder was a thermoset polyester resin and its chemical constituents were calcium, oxygen, aluminium, silica, chlorine, bromine and carbon. When up to 25 parts per hundred rubber by weight of the GRP waste powder was mixed with a sulphur cure- based styrene-butadiene rubber, the viscosity, scorch and optimum cure times increased, and the rate of cure decreased. The tearing energy, elongation at break, tensile strength, stored energy density at break, and Young’s modulus of the vulcanisate improved as the loading of the waste powder was raised

Defect and anisotropic gap induced quasi-one-dimensional modulation of local density of states in YBa2_2Cu3_3O7−δ_{7-\delta}

Motivated by recent angle-resolved photoemission spectroscopy (ARPES) measurement that superconducting YBa$_2$Cu$_3$O$_{7-\delta}$ (YBCO) exhibits a $d_{x^2-y^2} + s$-symmetry gap, we show possible quasi-one-dimensional modulations of local density of states in YBCO. These aniostropic gap and defect induced stripe structures are most conspicuous at higher biases and arise due to the nesting effect associated with a Fermi liquid. Observation of these spectra by scanning tunneling microscopy (STM) would unify the picture among STM, ARPES, and inelastic neutron scattering for YBCO.Comment: 4 pages, 4 figure

Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning

Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independently. In a previous study, we found that an iterative use of predicted secondary structure and backbone torsion angles can further improve secondary structure and torsion angle prediction. In this study, we expand the iterative features to include solvent accessible surface area and backbone angles and dihedrals based on Cα atoms. By using a deep learning neural network in three iterations, we achieved 82% accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone φ and ψ angles, respectively, and 8° and 32° for mean absolute errors of Cα-based θ and τ angles, respectively, for an independent test dataset of 1199 proteins. The accuracy of the method is slightly lower for 72 CASP 11 targets but much higher than those of model structures from current state-of-the-art techniques. This suggests the potentially beneficial use of these predicted properties for model assessment and ranking

Online change detection for energy-efficient mobilec crowdsensing

Mobile crowdsensing is power hungry since it requires continuously and simultaneously sensing, processing and uploading fused data from various sensor types including motion sensors and environment sensors. Realizing that being able to pinpoint change points of contexts enables energy-efficient mobile crowdsensing, we modify histogram-based techniques to efficiently detect changes, which has less computational complexity and performs better than the conventional techniques. To evaluate our proposed technique, we conducted experiments on real audio databases comprising 200 sound tracks. We also compare our change detection with multivariate normal distribution and one-class support vector machine. The results show that our proposed technique is more practical for mobile crowdsensing. For example, we show that it is possible to save 80% resource compared to standard continuous sensing while remaining detection sensitivity above 95%. This work enables energy-efficient mobile crowdsensing applications by adapting to contexts

Temperature dependence of current self-oscillations and electric field domains in sequential tunneling doped superlattices

We examine how the current--voltage characteristics of a doped weakly coupled superlattice depends on temperature. The drift velocity of a discrete drift model of sequential tunneling in a doped GaAs/AlAs superlattice is calculated as a function of temperature. Numerical simulations and theoretical arguments show that increasing temperature favors the appearance of current self-oscillations at the expense of static electric field domain formation. Our findings agree with available experimental evidence.Comment: 7 pages, 5 figure