Development of a novel metastable composite material

The development of a new family of mouldable metastable composite materials has been demonstrated. Their special quality is derived from the ability to maintain the matrix as a supercooled liquid or gel whose solidification can be triggered mechanically, as desired, by a user. This article describes some aspects of the development work. In particular, the following are explained: the choice of matrix material; the use of additives to enhance the properties of the matrix; and the selection of reinforcement fibre. As part of the work, some mechanical testing was performed on several variations of a matrix-fibre pair and, to demonstrate the potential of such materials, some comparisons were made with a possible competitor material, a glass-reinforced urethane. It was shown that the metastable material could be formulated to provide mechanical properties that would make it suitable for applications such as orthopaedic casting, splinting and body armour, and in items of sports equipment, these being areas where its mouldability could be particularly desirable

A Novel Optical/digital Processing System for Pattern Recognition

This paper describes two processing algorithms that can be implemented optically: the Radon transform and angular correlation. These two algorithms can be combined in one optical processor to extract all the basic geometric and amplitude features from objects embedded in video imagery. We show that the internal amplitude structure of objects is recovered by the Radon transform, which is a well-known result, but, in addition, we show simulation results that calculate angular correlation, a simple but unique algorithm that extracts object boundaries from suitably threshold images from which length, width, area, aspect ratio, and orientation can be derived. In addition to circumventing scale and rotation distortions, these simulations indicate that the features derived from the angular correlation algorithm are relatively insensitive to tracking shifts and image noise. Some optical architecture concepts, including one based on micro-optical lenslet arrays, have been developed to implement these algorithms. Simulation test and evaluation using simple synthetic object data will be described, including results of a study that uses object boundaries (derivable from angular correlation) to classify simple objects using a neural network

The role of pecuniary and non-pecuniary factors in teacher turnover and mobility decisions

We investigate the determinants of teacher exits from and mobility within the Queensland state school system. In common with previous research we find that non-pecuniary factors, such as class size and location, affect movement decisions but our results suggest a significant role for pecuniary factors. In particular, higher wages reduce exits from the public sector, especially in the case of more experienced female teachers. Locality allowances paid to teachers in rural and remote schools, where non-pecuniary factors are less attractive, appear to have some success in attracting and retaining staff in these locations

Two stochastic models useful in petroleum exploration

A model of the petroleum exploration process that tests empirically the hypothesis that at an early stage in the exploration of a basin, the process behaves like sampling without replacement is proposed along with a model of the spatial distribution of petroleum reserviors that conforms to observed facts. In developing the model of discovery, the following topics are discussed: probabilitistic proportionality, likelihood function, and maximum likelihood estimation. In addition, the spatial model is described, which is defined as a stochastic process generating values of a sequence or random variables in a way that simulates the frequency distribution of areal extent, the geographic location, and shape of oil deposit

Reward and uncertainty in exploration programs

A set of variables which are crucial to the economic outcome of petroleum exploration are discussed. These are treated as random variables; the values they assume indicate the number of successes that occur in a drilling program and determine, for a particular discovery, the unit production cost and net economic return if that reservoir is developed. In specifying the joint probability law for those variables, extreme and probably unrealistic assumptions are made. In particular, the different random variables are assumed to be independently distributed. Using postulated probability functions and specified parameters, values are generated for selected random variables, such as reservoir size. From this set of values the economic magnitudes of interest, net return and unit production cost are computed. This constitutes a single trial, and the procedure is repeated many times. The resulting histograms approximate the probability density functions of the variables which describe the economic outcomes of an exploratory drilling program

The Calculus of M-estimation in R with geex

M-estimation, or estimating equation, methods are widely applicable for point estimation and asymptotic inference. In this paper, we present an R package that can find roots and compute the empirical sandwich variance estimator for any set of user-specified, unbiased estimating equations. Examples from the M-estimation primer by Stefanski and Boos (2002) demonstrate use of the software. The package also includes a framework for finite sample variance corrections and a website with an extensive collection of tutorials