Towards globo sapiens : transforming learners in higher education

Global and local studies show that the present growth-based approach to development is unsustainable. If we are serious about surviving the 21st century we will need graduates who are not simply 'globally portable' or even 'globally competent', but also wise global citizens, Globo sapiens. This book contributes to what educators need to know, do and be in order to support transformative learning. The book is based on work with large, socially and culturally diverse, first-year engineering students at an Australian university of technology. It shows that reflective journals, with appropriate planning and support, can be one pillar of a transformative pedagogy which can encourage significant and even transformative attitude change in relation to gender, culture and the environment. It also offers evidence of improved communication skills and other tangible changes to counter common criticisms that such work is "airy-fairy" and irrelevant. The author combines communication theory with critical futures thinking to provide layered understandings of how transformative learning affected students' thinking, learning and behaviour. So the book is both a case-study and a detailed response to the personal and professional challenges that educators all over the world will face as they try to guide students in sustainable directions. It will be useful to teachers in higher education, especially those interested in internationalisation of the curriculum, transformative learning and values change for sustainable futures.

Multifractal characterisation and analysis of complex networks

Complex networks have been studied extensively due to their relevance to many real-world systems such as the world-wide web, the internet, biological and social systems. During the past two decades, studies of such networks in different fields have produced many significant results concerning their structures, topological properties, and dynamics. Three well-known properties of complex networks are scale-free degree distribution, small-world effect and self-similarity. The search for additional meaningful properties and the relationships among these properties is an active area of current research. This thesis investigates a newer aspect of complex networks, namely their multifractality, which is an extension of the concept of selfsimilarity. The first part of the thesis aims to confirm that the study of properties of complex networks can be expanded to a wider field including more complex weighted networks. Those real networks that have been shown to possess the self-similarity property in the existing literature are all unweighted networks. We use the proteinprotein interaction (PPI) networks as a key example to show that their weighted networks inherit the self-similarity from the original unweighted networks. Firstly, we confirm that the random sequential box-covering algorithm is an effective tool to compute the fractal dimension of complex networks. This is demonstrated on the Homo sapiens and E. coli PPI networks as well as their skeletons. Our results verify that the fractal dimension of the skeleton is smaller than that of the original network due to the shortest distance between nodes is larger in the skeleton, hence for a fixed box-size more boxes will be needed to cover the skeleton. Then we adopt the iterative scoring method to generate weighted PPI networks of five species, namely Homo sapiens, E. coli, yeast, C. elegans and Arabidopsis Thaliana. By using the random sequential box-covering algorithm, we calculate the fractal dimensions for both the original unweighted PPI networks and the generated weighted networks. The results show that self-similarity is still present in generated weighted PPI networks. This implication will be useful for our treatment of the networks in the third part of the thesis. The second part of the thesis aims to explore the multifractal behavior of different complex networks. Fractals such as the Cantor set, the Koch curve and the Sierspinski gasket are homogeneous since these fractals consist of a geometrical figure which repeats on an ever-reduced scale. Fractal analysis is a useful method for their study. However, real-world fractals are not homogeneous; there is rarely an identical motif repeated on all scales. Their singularity may vary on different subsets; implying that these objects are multifractal. Multifractal analysis is a useful way to systematically characterize the spatial heterogeneity of both theoretical and experimental fractal patterns. However, the tools for multifractal analysis of objects in Euclidean space are not suitable for complex networks. In this thesis, we propose a new box covering algorithm for multifractal analysis of complex networks. This algorithm is demonstrated in the computation of the generalized fractal dimensions of some theoretical networks, namely scale-free networks, small-world networks, random networks, and a kind of real networks, namely PPI networks of different species. Our main finding is the existence of multifractality in scale-free networks and PPI networks, while the multifractal behaviour is not confirmed for small-world networks and random networks. As another application, we generate gene interactions networks for patients and healthy people using the correlation coefficients between microarrays of different genes. Our results confirm the existence of multifractality in gene interactions networks. This multifractal analysis then provides a potentially useful tool for gene clustering and identification. The third part of the thesis aims to investigate the topological properties of networks constructed from time series. Characterizing complicated dynamics from time series is a fundamental problem of continuing interest in a wide variety of fields. Recent works indicate that complex network theory can be a powerful tool to analyse time series. Many existing methods for transforming time series into complex networks share a common feature: they define the connectivity of a complex network by the mutual proximity of different parts (e.g., individual states, state vectors, or cycles) of a single trajectory. In this thesis, we propose a new method to construct networks of time series: we define nodes by vectors of a certain length in the time series, and weight of edges between any two nodes by the Euclidean distance between the corresponding two vectors. We apply this method to build networks for fractional Brownian motions, whose long-range dependence is characterised by their Hurst exponent. We verify the validity of this method by showing that time series with stronger correlation, hence larger Hurst exponent, tend to have smaller fractal dimension, hence smoother sample paths. We then construct networks via the technique of horizontal visibility graph (HVG), which has been widely used recently. We confirm a known linear relationship between the Hurst exponent of fractional Brownian motion and the fractal dimension of the corresponding HVG network. In the first application, we apply our newly developed box-covering algorithm to calculate the generalized fractal dimensions of the HVG networks of fractional Brownian motions as well as those for binomial cascades and five bacterial genomes. The results confirm the monoscaling of fractional Brownian motion and the multifractality of the rest. As an additional application, we discuss the resilience of networks constructed from time series via two different approaches: visibility graph and horizontal visibility graph. Our finding is that the degree distribution of VG networks of fractional Brownian motions is scale-free (i.e., having a power law) meaning that one needs to destroy a large percentage of nodes before the network collapses into isolated parts; while for HVG networks of fractional Brownian motions, the degree distribution has exponential tails, implying that HVG networks would not survive the same kind of attack.

Human lens lipids differ markedly from those of commonly used experimental animals

Electrospray ionisation tandem mass spectrometry has allowed the unambiguous identification and quantification of individual lens phospholipids in human and six animal models. Using this approach ca. 100 unique phospholipids have been characterised. Parallel analysis of the same lens extracts by a novel direct-insertion electron-ionization technique found the cholesterol content of human lenses to be significantly higher (ca. 6 times) than lenses from the other animals. The most abundant phospholipids in all the lenses examined were choline-containing phospholipids. In rat, mouse, sheep, cow, pig and chicken, these were present largely as phosphatidylcholines, in contrast 66% of the total phospholipid in Homo sapiens was sphingomyelin, with the most abundant being dihydrosphingomyelins, in particular SM(d18:0/16:0) and SM(d18:0/24:1). The abundant glycerophospholipids within human lenses were found to be predominantly phosphatidylethanolamines and phosphatidylserines with surprisingly high concentrations of ether-linked alkyl chains identified in both classes. This study is the first to identify the phospholipid class (head-group) and assign the constituent fatty acid(s) for each lipid molecule and to quantify individual lens phospholipids using internal standards. These data clearly indicate marked differences in the membrane lipid composition of the human lens compared to commonly used animal models and thus predict a significant variation in the membrane properties of human lens fibre cells compared to those of other animals. © 2008 Elsevier B.V. All rights reserved.