Mining learning preferences in web-based instruction: Holists vs. Serialists

Web-based instruction programs are used by learners with diverse knowledge, skills and needs. These differences determine their preferences for the design of Web-based instruction programs and ultimately influence learners' success in using them. Cognitive style has been found to significantly affect learners' preferences of web-based instruction programs. However, the majority of previous studies focus on Field Dependence/Independence. Pask's Holist/Serialist dimension has conceptual links with Field Dependence/Independence but it is left mostly unstudied. Therefore, this study focuses on identifying how this dimension of cognitive style affects learner preferences of Web-based instruction programs. A data mining approach is used to illustrate the difference in preferences between Holists and Serialists. The findings show that there are clear differences in regard to content presentation and navigation support. A set of design features were then produced to help designers incorporate cognitive styles into the development of Web-based instruction programs to ensure that they can accommodate learners' different preferences.This work is partially funded by National Science Council, Taiwan, ROC (NSC 98-2511-S-008-012- MY3; NSC 99- 2511-S-008 -003 -MY2; NSC 99-2631-S-008-001)

Zero Modes of Matter Fields on Scalar Flat Thick Branes

Zero modes of various matters with spin 0, 1 and 1/2 on a class of scalar flat thick branes are discussed in this paper. We show that scalar field with spin 0 is localized on all thick branes without additional condition, while spin 1 vector field is not localized. In addition, for spin 1/2 fermionic field, the zero mode is localized on the branes under certain conditions.Comment: 11 pages,no figure

Identification of nonlinear lateral flow immunoassay state-space models via particle filter approach

This is the post-print of the Article. The official published version can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, the particle filtering approach is used, together with the kernel smoothing method, to identify the state-space model for the lateral flow immunoassay through available but short time-series measurement. The lateral flow immunoassay model is viewed as a nonlinear dynamic stochastic model consisting of the equations for the biochemical reaction system as well as the measurement output. The renowned extended Kalman filter is chosen as the importance density of the particle filter for the purpose of modeling the nonlinear lateral flow immunoassay. By using the developed particle filter, both the states and parameters of the nonlinear state-space model can be identified simultaneously. The identified model is of fundamental significance for the development of lateral flow immunoassay quantification. It is shown that the proposed particle filtering approach works well for modeling the lateral flow immunoassay.This work was supported in part by the International Science and Technology Cooperation Project of China under Grant 2009DFA32050, Natural Science Foundation of China under Grants 61104041, International Science and Technology Cooperation Project of Fujian Province of China under Grant 2009I0016

A hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay models

This is the post-print version of the Article. The official published can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm optimization (SPSO) algorithm is proposed for jointly estimating both the parameters and states of the lateral flow immunoassay model through available short time-series measurement. Our proposed method generalizes the well-known EKF algorithm by imposing physical constraints on the system states. Note that the state constraints are encountered very often in practice that give rise to considerable difficulties in system analysis and design. The main purpose of this paper is to handle the dynamic modeling problem with state constraints by combining the extended Kalman filtering and constrained optimization algorithms via the maximization probability method. More specifically, a recently developed SPSO algorithm is used to cope with the constrained optimization problem by converting it into an unconstrained optimization one through adding a penalty term to the objective function. The proposed algorithm is then employed to simultaneously identify the parameters and states of a lateral flow immunoassay model. It is shown that the proposed algorithm gives much improved performance over the traditional EKF method.This work was supported in part by the International Science and Technology Cooperation Project of China under Grant 2009DFA32050, Natural Science Foundation of China under Grants 61104041, International Science and Technology Cooperation Project of Fujian Province of China under Grant 2009I0016

Inference of nonlinear state-space models for sandwich-type lateral flow immunoassay using extended Kalman filtering

Copyright [2011] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this paper, a mathematical model for sandwichtype lateral flow immunoassay is developed via short available time series. A nonlinear dynamic stochastic model is considered that consists of the biochemical reaction system equations and the observation equation. After specifying the model structure, we apply the extend Kalman filter (EKF) algorithm for identifying both the states and parameters of the nonlinear state-space model. It is shown that the EKF algorithm can accurately identify the parameters and also predict the system states in the nonlinear dynamic stochastic model through an iterative procedure by using a small number of observations. The identified mathematical model provides a powerful tool for testing the system hypotheses and also inspecting the effects from various design parameters in a both rapid and inexpensive way. Furthermore, by means of the established model, the dynamic changes of the concentration of antigens and antibodies can be predicted, thereby making it possible for us to analyze, optimize and design the properties of lateral flow immunoassay devices.This work was supported in part by the International Science and Technology Cooperation Project of China under Grant 2009DFA32050, Natural Science Foundation of Fujian Province of China under Grants 2009J01280 and 2009J01281

Lagrange Model for the Chiral Optical Properties of Stereometamaterials

We employ a general Lagrange model to describe the chiral optical properties of stereometamaterials. We derive the elliptical eigenstates of a twisted stacked split-ring resonator, taking phase retardation into account. Through this approach, we obtain a powerful Jones matrix formalism which can be used to calculate the polarization rotation, ellipticity, and circular dichroism of transmitted waves through stereometamaterials at any incident polarization. Our experimental measurements agree well with our model.Comment: 10 pages, 3 figures, Theory and experimen

Giant Anisotropic Magnetoresistance in a Quantum Anomalous Hall Insulator

When a three-dimensional (3D) ferromagnetic topological insulator thin film is magnetized out-of-plane, conduction ideally occurs through dissipationless, one-dimensional (1D) chiral states that are characterized by a quantized, zero-field Hall conductance. The recent realization of this phenomenon - the quantum anomalous Hall effect - provides a conceptually new platform for studies of edge-state transport, distinct from the more extensively studied integer and fractional quantum Hall effects that arise from Landau level formation. An important question arises in this context: how do these 1D edge states evolve as the magnetization is changed from out-of-plane to in-plane? We examine this question by studying the field-tilt driven crossover from predominantly edge state transport to diffusive transport in Cr-doped (Bi,Sb)2Te3 thin films, as the system transitions from a quantum anomalous Hall insulator to a gapless, ferromagnetic topological insulator. The crossover manifests itself in a giant, electrically tunable anisotropic magnetoresistance that we explain using the Landauer-Buttiker formalism. Our methodology provides a powerful means of quantifying edge state contributions to transport in temperature and chemical potential regimes far from perfect quantization

Local spin polarisation of electrons in Rashba semiconductor nanowires: effects of the bound state

The local spin polarisation (LSP) of electrons in two typical semiconductor nanowires under the modulation of Rashba spin-orbit interaction (SOI) is investigated theoretically. The influence of both the SOI- and structure-induced bound states on the LSP is taken into account via the spin-resolved lattice Green function method. It is discovered that high spin-density islands with alternative signs of polarisation are formed inside the nanowires due to the interaction between the bound states and the Rashba effective magnetic field. Further study shows that the spin-density islands caused by the structure-induced bound state exhibit a strong robustness against disorder. These findings may provide an efficient way to create local magnetic moments and store information in semiconductors.Comment: 8 pages, 3 figure

Hurst parameter analysis of radio pulsar timing noise

We present an analysis of timing residual (noise) of 54 pulsars obtained from 25-m radio telescope at Urumqi Observatory with a time span of 5~8 years, dealing with statistics of the Hurst parameter. The majority of these pulsars were selected to have timing noise that look like white noise rather than smooth curves. The results are compared with artificial series of different constant pairwise covariances. Despite the noise like appearance, many timing residual series showed Hurst parameters significantly deviated from that of independent series. We concluded that Hurst parameter may be capable of detecting dependence in timing residual and of distinguishing chaotic behavior from random processes.Comment: 7 pages, 3 figures, 2 tables, Submitted to MNRA