Convergence analysis of multi-stage PIC detectors

This paper analyses the convergence behaviour of the parallel interference cancellation (PIC) detector in code division multiple access (CDMA) systems. Using the results from previous stability analysis of an iterated-map neural network, the paper derives a general condition from which the sufficient condition for convergence of the PIC detector with tentative decision functions that are monotonically increasing at a sublinear rate can be calculated. As examples, the paper derives the sufficient conditions for convergence of the PIC detector with the clip decision and the hyperbolic tangent decision functions. The paper also examines the convergence behaviour of the PIC detector with hyperbolic tangent decision function via computer simulation and compares it with the analytical results.

Steric control over molecular structure and supramolecular association exerted by tin- and ligand-bound groups in diorganotin carboxylates

Structural data (X-ray and solution and solid-state ^₁₁₉Sn NMR) show that skew-trapezoidal-bipyramidal diorganotin compounds of 2-quinaldate are invariably monomeric, owing to the steric bulk of the carboxylate ligand. In contrast, most of the analogous compounds of 2-picolinate (2-pic) can increase their coordination number by polymerization or the incorporation of solvent in their coordination sphere in the solid state. The exceptional compound is tBu₂Sn(2-pic)₂ (3), for which no increase in coordination number is apparent, a result that is correlated with the bulky tert-butyl groups. Thus, judicious choice of tin or ligand substituents can be exploited to dictate coordination number and/or the degree of supramolecular aggregation in the investigated systems.

Predicting consumer innovative behavior using alternative theories and likelihood measures: a longitudinal study

This paper reports on a longitudinal study of consumers, where two dominant theories that purport to predict innovative behavior are applied and compared directly, using a methodology suggested as ideal by past researchers. Predictions made prior to launch were then evaluated against multiple measures of purchase likelihood, and against actual adoption behavior up to 12 months after launch. The results of this study suggest that perceptions of the innovations characteristics (PIC) predicted the selfreported likelihood of adoption better than the Domain Specific Innovativeness (DSI) scale, a personality-based measure. Prediction of actual adoption was largely inaccurate and both theories massively over predicted adoption levels, however the DSI scale was slightly more accurate. The conclusions here are that no one theory could make adequate predictions of behavior, that purchase likelihood measures are a poor substitute for measuring actual behavior but that purchase probability scales should be used more often in adoption research.

Application of radial return mapping algorithm for finite strain elastoplasticity in a hybrid finite element and particle-in-cell model

The radial return mapping algorithm within the computational context of a hybrid Finite Element and Particle-In-Cell (FE/PIC) method is constructed to allow a fluid flow FE/PIC code to be applied solid mechanic problems with large displacements and large deformations. The FE/PIC method retains the robustness of an Eulerian mesh and enables tracking of material deformation by a set of Lagrangian particles or material points. In the FE/PIC approach the particle velocities are interpolated from nodal velocities and then the particle position is updated using a suitable integration scheme, such as the 4th order Runge-Kutta scheme[1]. The strain increments are obtained from gradients of the nodal velocities at the material point positions, which are then used to evaluate the stress increment and update history variables. To obtain the stress increment from the strain increment, the nonlinear constitutive equations are solved in an incremental iterative integration scheme based on a radial return mapping algorithm[2]. A plane stress extension of a rectangular shape J2 elastoplastic material with isotropic, kinematic and combined hardening is performed as an example and for validation of the enhanced FE/PIC method. It is shown that the method is suitable for analysis of problems in crystal plasticity and metal forming. The method is specifically suitable for simulation of neighbouring microstructural phases with different constitutive equations in a multiscale material modelling framework.

Multiscale particle-in-cell modelling for advanced high strength steels

Advanced High Strength Steels (AHSS) offer outstanding characteristics for efficient and economic use of steel. The unique features of AHSS are direct result of careful heat treatment that creates martensite in the steel microstructure. Martensite and carbon content in the microstructure greatly affects the mechanical properties of AHSS, underlining more importance on microstructural discontinuities and their multiphase characteristics. In this paper, we present the Multiscale Particle-In-Cell (MPIC) method for microstructural modelling of AHSS. A specific particle method [1] usually used in fluid mechanics is adapted and implemented in a parallel multiscale framework. This multiscale method is based on homogenisation theories; with Particle-In-Cell (PIC) method in both micro and macroscale, and offers several advantages in comparison to finite element (FE) based formulation. Application of this method to a benchmark uniaxial tension test is presented and compared with conventional FE solutions.

Multiphase material modelling by multiscale particle-in-cell method

A particle-based method for multiscale modeling of multiphase materials such as Dual Phase (DP) and Transformation Induced Plasticity (TRIP) steels has been developed. The multiscale Particle-In-Cell (PIC) method benefits from the many advantages of the FEM and mesh-free methods, and to bridge the micro and macro scales through homogenization. The conventional mesh-based modeling methods fail to give reasonable and accurate predictions for materials with complex microstructures. Alternatively in the multiscale PIC method, the Lagrangian particles moving in an Eulerian grid represent the material deformation at both the micro and macro scales. The uniaxial tension test of two phase and three-phase materials was simulated and compared with FE based simulations. The predictions using multiscale PIC method showed that accuracy of field variables could be improved by up to 7%. This can lead to more accurate forming and springback predictions for materials with important multiphase microstructural effects.

Development and characterisation of 20 microsatellite loci isolated from the large bent-wing bat, Miniopterus schreibersii (Chiroptera: Miniopteridae) and their cross-taxa utility in the family Miniopteridae

The large bent-wing bat, Miniopterus schreibersii (Kuhl 1819), has a long history of taxonomic uncertainty and many populations are known to be in a state of decline. Microsatellite loci were developed for the taxonomic and population genetic assessment of the Australian complex of this species. Of the 33 primer sets designed for this research, seven (21%) were deemed suitably polymorphic for population-level analyses of the Australian taxa, with five (71%) of these loci revealing moderate to high levels of polymorphism (PIC = 0.56 to 0.91). The cross-taxa utility of the M. schreibersii microsatellite markers was assessed in the microbat (Chiroptera) family Miniopteridae. Sub-species and species covering the Miniopteridae’s global distribution (with the exception of the Middle East) were selected, numbering 25 taxa in total. Amplification was successful for 26 loci, of which 20 (77%) were polymorphic. High cross-taxa utility of markers was observed with amplification achieved for all taxa for between four (20%) and 20 (100%) loci, and polymorphism was considered moderate to high (PIC = 0.47–0.91) for 12 (60%) of these loci. The high cross-taxa utility of the microsatellites reported herein reveal versatile and cost-effective molecular markers, contributing an important genetic resource for the research and conservation of Miniopteridae species worldwide.

Missing the lesbian and the missing lesbian: a study of the forgotten lesbian in 1970s Australian cinema

This paper addresses the (largely forgotten) presence of lesbians, in 1970s Australian film. It explores the romantic friendships canvassed in 'Picnic at Hanging Rock', (Peter Weir, 1975) and 'The Getting of Wisdom', (Bruce Beresford, 1977) alongside the lesbian in other canonical 1970s works, like the election comedy, 'Don’s Party' (Beresford, 1976) the bio-pic 'Dawn!', and unique tele-features such as 'The Alternative' (Paul Eddy 1978). This paper investigates the way these texts are read and received. The 1970s was an important decade in Australia's filmic history, (affectionately dubbed Australia's film ‘Renaissance’) and this paper examine the lesbian readings that are and aren’t there; and that are and are not avowed.

Prediction interval-based control of nonlinear systems using neural networks

Prediction interval (PI) is a promising tool for quantifying uncertainties associated with point predictions. Despite its informativeness, the design and deployment of PI-based controller for complex systems is very rare. As a pioneering work, this paper proposes a framework for design and implementation of PI-based controller (PIC) for nonlinear systems. Neural network (NN)-based inverse model within internal model control structure is used to develop the PIC. Firstly, a PI-based model is developed to construct PIs for the system output. This model is then used as an online estimator for PIs. The PIs from this model are fed to the NN inverse model along with other traditional inputs to generate the control signal. The performance of the proposed PIC is examined for two case studies. This includes a nonlinear batch polymerization reactor and a numerical nonlinear plant. Simulation results demonstrated that the proposed PIC tracking performance is better than the traditional NN-based controller.

Prediction interval-based neural network controller for nonlinear processes

Prediction interval (PI) has been extensively used to predict the forecasts for nonlinear systems as PI-based forecast is superior over point-forecast to quantify the uncertainties and disturbances associated with the real processes. In addition, PIs bear more information than point-forecasts, such as forecast accuracy. The aim of this paper is to integrate the concept of informative PIs in the control applications to improve the tracking performance of the nonlinear controllers. In the present work, a PI-based controller (PIC) is proposed to control the nonlinear processes. Neural network (NN) inverse model is used as a controller in the proposed method. Firstly, a PI-based model is developed to construct PIs for every sample or time instance. The PIs are then fed to the NN inverse model along with other effective process inputs and outputs. The PI-based NN inverse model predicts the plant input to get the desired plant output. The performance of the proposed PIC controller is examined for a nonlinear process. Simulation results indicate that the tracking performance of the PIC is highly acceptable and better than the traditional NN inverse model-based controller.