Measuring and decomposing changes in agricultural productivity, nitrogen use efficiency and cumulative exergy efficiency: Application to OECD agriculture

This paper uses an aggregate quantity space to decompose the temporal changes in nitrogen use efficiency and cumulative exergy use efficiency into changes of Moorsteen–Bjurek (MB) Total Factor Productivity (TFP) changes and changes in the aggregate nitrogen and cumulative exergy contents. Changes in productivity can be broken into technical change and changes in various efficiency measures such as technical efficiency, scale efficiency and residual mix efficiency. Changes in the aggregate nitrogen and cumulative exergy contents can be driven by changes in the quality of inputs and outputs and changes in the mixes of inputs and outputs. Also with cumulative exergy content analysis, changes in the efficiency in input production can increase or decrease the cumulative exergy transformity of agricultural production. The empirical study in 30 member countries of the Organisation for Economic Co-operation Development from 1990 to 2003 yielded some important findings. The production technology progressed but there were reductions in technical efficiency, scale efficiency and residual mix efficiency levels. This result suggests that the production frontier had shifted up but there existed lags in the responses of member countries to the technological change. Given TFP growth, improvements in nutrient use efficiency and cumulative exergy use efficiency were counteracted by reductions in the changes of the aggregate nitrogen contents ratio and aggregate cumulative exergy contents ratio. The empirical results also confirmed that different combinations of inputs and outputs as well as the quality of inputs and outputs could have more influence on the growth of nutrient and cumulative exergy use efficiency than factors that had driven productivity change. Keywords: Nutrient use efficiency; Cumulative exergy use efficiency; Thermodynamic efficiency change; Productivity growth; OECD agriculture; Sustainability

Mortality, fertility, and persistent income inequality

Many developing countries are plagued by persistent inequality in income distribution. While a growing body of economic-demographic literature emphasizes differential fertility channel, this paper investigates differential child mortality--differences in child mortality across income groups--as a critical link through which income inequality persists. Using an overlapping generations model in which both child mortality and fertility are endogenously determined by parental choice, this paper demonstrates that differential child mortality and its interaction with differential fertility may generate an "income inequality trap." The trap is characterized by higher child mortality and lower degree of skill formation among the poorer households. The model can also explain the behavior of aggregate fertility and mortality rates for countries at various stages of development, consonant with patterns of demographic transition. The results indicate that provision of public health that raises the productivity of private health spending may be an effective way to reduce income inequality

Improving the provision of learning assistance services in higher education

This study is motivated by the need to look continually for ways to improve Griffith University's learning assistance services so that they meet the changed needs of stakeholders and are at the same time cost-effective and efficient. This study uses the conceptual tools of cultural-historical activity theory and expansive visibilisation to investigaate the developmenet and transformation of learning assistance services at Griffith University, one of Australia's largest mult-campus universities.

Application of higher order spectra to identify epileptic EEG

Epilepsy is characterized by the spontaneous and seemingly unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic system that detects seizure onsets would allow patients or the people near them to take appropriate precautions, and could provide more insight into this phenomenon. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, we made a comparative study of the performance of Gaussian mixture model (GMM) and Support Vector Machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Results show that the selected HOS based features achieve 93.11% classification accuracy compared to 88.78% with features derived from the power spectrum for a GMM classifier. The SVM classifier achieves an improvement from 86.89% with features based on the power spectrum to 92.56% with features based on the bispectrum.

Pattern recognition using invariants defined from higher order spectra : 2-D image inputs

A new algorithm for extracting features from images for object recognition is described. The algorithm uses higher order spectra to provide desirable invariance properties, to provide noise immunity, and to incorporate nonlinearity into the feature extraction procedure thereby allowing the use of simple classifiers. An image can be reduced to a set of 1D functions via the Radon transform, or alternatively, the Fourier transform of each 1D projection can be obtained from a radial slice of the 2D Fourier transform of the image according to the Fourier slice theorem. A triple product of Fourier coefficients, referred to as the deterministic bispectrum, is computed for each 1D function and is integrated along radial lines in bifrequency space. Phases of the integrated bispectra are shown to be translation- and scale-invariant. Rotation invariance is achieved by a regrouping of these invariants at a constant radius followed by a second stage of invariant extraction. Rotation invariance is thus converted to translation invariance in the second step. Results using synthetic and actual images show that isolated, compact clusters are formed in feature space. These clusters are linearly separable, indicating that the nonlinearity required in the mapping from the input space to the classification space is incorporated well into the feature extraction stage. The use of higher order spectra results in good noise immunity, as verified with synthetic and real images. Classification of images using the higher order spectra-based algorithm compares favorably to classification using the method of moment invariants

Shape discrimination using invariants defined from higher-order spectra

An approach to pattern recognition using invariant parameters based on higher-order spectra is presented. In particular, bispectral invariants are used to classify one-dimensional shapes. The bispectrum, which is translation invariant, is integrated along straight lines passing through the origin in bifrequency space. The phase of the integrated bispectrum is shown to be scale- and amplification-invariant. A minimal set of these invariants is selected as the feature vector for pattern classification. Pattern recognition using higher-order spectral invariants is fast, suited for parallel implementation, and works for signals corrupted by Gaussian noise. The classification technique is shown to distinguish two similar but different bolts given their one-dimensional profiles

A general procedure for the derivation of principal domains of higher-order spectra

A general procedure to determine the principal domain (i.e., nonredundant region of computation) of any higher-order spectrum is presented, using the bispectrum as an example. The procedure is then applied to derive the principal domain of the trispectrum of a real-valued, stationary time series. These results are easily extended to compute the principal domains of other higher-order spectra

Position, rotation, and scale invariant recognition of images using higher-order spectra

A new approach to recognition of images using invariant features based on higher-order spectra is presented. Higher-order spectra are translation invariant because translation produces linear phase shifts which cancel. Scale and amplification invariance are satisfied by the phase of the integral of a higher-order spectrum along a radial line in higher-order frequency space because the contour of integration maps onto itself and both the real and imaginary parts are affected equally by the transformation. Rotation invariance is introduced by deriving invariants from the Radon transform of the image and using the cyclic-shift invariance property of the discrete Fourier transform magnitude. Results on synthetic and actual images show isolated, compact clusters in feature space and high classification accuracies

Theorising a model for teaching and assessing reflective learning in higher education

The importance of reflection in higher education, and across disciplinary fields is widely recognised; it is generally included in university graduate attributes, professional standards and program objectives. Furthermore, reflection is commonly embedded into assessment requirements in higher education subjects, often without necessary scaffolding or clear expectations for students. Despite the rhetoric around the importance of reflection for ongoing learning, there is scant literature on any systematic, developmental approach to teaching reflective learning across higher education programs/courses. Given that professional or academic reflection is not intuitive, and requires specific pedagogic intervention to do well, a program/course-wide approach is essential. This paper draws on current literature to theorise a new, transferable and customisable model for teaching and assessing reflective learning across higher education, which foregrounds and explains the pedagogic field of higher education as a multi-dimensional space. We argue that explicit and strategic pedagogic intervention, supported by dynamic resources, is necessary for successful, broad-scale approaches to reflection in higher education.

Conceptualising and teaching discursive and performative reflection in higher education

The importance of reflection in higher education, and across disciplinary fields is widely recognised. It is generally embedded in university graduate attributes, professional standards and course objectives. Furthermore, reflection is commonly included in assessment requirements in higher education subjects, often without necessary scaffolding or clear expectations for students. It is essential that academic staff have substantive knowledge and clear expectations about the aims of reflective activities, the most effective mode of representation, and appropriate teaching strategies to support students in deep, critical reflection. The paper argues the case for reflection to be represented in different modes, using discursive (language) or performative (symbolic practice) forms of expression according to disciplinary context and individual communicative strengths. It introduces key discursive and expressive elements that constitute different modes of representation in reflective tasks. This functional analysis of textual elements provides explicit knowledge for teaching and assessing multiple modes of reflection in higher education.

Successes and stresses : A case study on relations between international higher degree students in Australia and their universities.

The paper examines the situation of postgraduate international students studying in Australia, mostly at doctoral level; a group widely seen as sought-after by Australian universities and employers, though also exposed to difficulties in aspects like learning culture, language and temporary employment. The investigation follows a novel path, as an exercise in practice-led research on issues involved in Higher Degree supervision. It is in fact an exercise within an advanced program of professional development for HD research supervisors. It begins by deploying a journalistic method, to obtain and present information. This has entailed the publishing of two feature articles about the lives of scholars for Subtropic, a campus based online magazine in Brisbane, www.subtropic.com.au. The next step is a review of a set of supervisions, citing issues raised in individual cases. Parallels can be seen between the two information-getting and analytical processes, with scope for contradictions. An exegetical statement deals with supervisory issues that have been exposed, and implications for learning, with recommendations for developing the quality of the experience of these students.

An exploration of shared services types in higher education

The emerging global Higher Education (HE) market challenges all universities to reconsider their mission, in order to function effectively and efficiently, and to be responsive to changing marketplace demands. Universities are considering cooperating or sharing in a wide range of areas, in order to achieve sought after cost savings and improvements in performance.Studies suggest that a wide range of services could potentially be shared across Higher Education Institutions (HEIs),offering several potential benefits. While there are many individual documented examples of how shared services have been applied in the Higher Education sector, there has been little synthesis, conceptualization or discussion (in general or in the HE sector) around the different types of possible shared services options and how they are structured. A deeper understanding of potential types of sharing arrangements will be of value to those universities considering shared services, implementing shared services, and managing shared services. Through analysis of 36 documented case studies of shared services in HE, this study sought to synthesize a typology of sharing arrangements. An inductive approach was used to identify the core differentiating dimensions. The findings present eight types of sharing arrangements that occur in the HE sector, with descriptions and examples from the case studies observed.

Distribution trade sector output and productivity performance : a case study of Singapore and Hong Kong 2001-2008

This paper employs the industry of origin approach to compare value added and productivity of Singapore and Hong Kong's Distribution Trade Sector for the period 2001-2008. The direct comparison between these two economies was motivated by the statements of the Singapore government: Its services sector, especially in Retail Trade, lags behind Hong Kong's productivity levels. The results show that since 2005, Singapore's Distribution performance in terms of labour productivity was below Hong Kong's level, which was largely due to poor performance in its Retail Trade sector arising from an influx of foreign workers. Results from total factor productivity (TFP) between these two economies also suggest that Hong Kong's better performance (since 2005) was largely due to its ability to employ more educated and trained workers with limited use of capital. The results suggest that polices that worked in Hong Kong may not work for Singapore because its population is more diverse which poses a challenge to policy-makers in raising its productivity level.

Pattern recognition using invariants defined from higher order spectra - one-dimensional inputs

A new approach to pattern recognition using invariant parameters based on higher order spectra is presented. In particular, invariant parameters derived from the bispectrum are used to classify one-dimensional shapes. The bispectrum, which is translation invariant, is integrated along straight lines passing through the origin in bifrequency space. The phase of the integrated bispectrum is shown to be scale and amplification invariant, as well. A minimal set of these invariants is selected as the feature vector for pattern classification, and a minimum distance classifier using a statistical distance measure is used to classify test patterns. The classification technique is shown to distinguish two similar, but different bolts given their one-dimensional profiles. Pattern recognition using higher order spectral invariants is fast, suited for parallel implementation, and has high immunity to additive Gaussian noise. Simulation results show very high classification accuracy, even for low signal-to-noise ratios.