Navigating a complex organizational landscape : leadership challenges and practices

This paper discusses exploratory research to identify the reported leadership challenges faced by leaders in the public sector in Australia and what specific leadership practices they engage in to deal with these challenges. Emerging is a sense that leadership in these complex work environments is not about controlling or mandating action but about engaging in conversation, building relationships and empowering staff to engage in innovative ways to solve complex problems. In addition leaders provide a strong sense of purpose and identity to guide behaviour and decisions to overcome being overwhelmed by the sheer volume of demands in a unpredictable and often unsupportive environment. Questions are raised as to the core competencies leaders need to develop to drive and underpin these leadership practices and the implications this has for the focus on future leadership development programmes. The possible direction of a future research programme will be put forward for further discussion.

How long is a tweet? Mapping dynamic conversation networks on Twitter using Gawk and Gephi

Twitter is now well established as the world’s second most important social media platform, after Facebook. Its 140-character updates are designed for brief messaging, and its network structures are kept relatively flat and simple: messages from users are either public and visible to all (even to unregistered visitors using the Twitter website), or private and visible only to approved ‘followers’ of the sender; there are no more complex definitions of degrees of connection (family, friends, friends of friends) as they are available in other social networks. Over time, Twitter users have developed simple, but effective mechanisms for working around these limitations: ‘#hashtags’, which enable the manual or automatic collation of all tweets containing the same #hashtag, as well allowing users to subscribe to content feeds that contain only those tweets which feature specific #hashtags; and ‘@replies’, which allow senders to direct public messages even to users whom they do not already follow. This paper documents a methodology for extracting public Twitter activity data around specific #hashtags, and for processing these data in order to analyse and visualize the @reply networks existing between participating users – both overall, as a static network, and over time, to highlight the dynamic structure of @reply conversations. Such visualizations enable us to highlight the shifting roles played by individual participants, as well as the response of the overall #hashtag community to new stimuli – such as the entry of new participants or the availability of new information. Over longer timeframes, it is also possible to identify different phases in the overall discussion, or the formation of distinct clusters of preferentially interacting participants.

Charting the system : the integrated master schedule as a multi-level and poly-temporal boundary object in complex projects

This paper investigates the use of visual artifacts to represent a complex adaptive system (CAS). The integrated master schedule (IMS) is one of those visuals widely used in complex projects for scheduling, budgeting, and project management. In this paper, we discuss how the IMS outperforms the traditional timelines and acts as a ‘multi-level and poly-temporal boundary object’ that visually represents the CAS. We report the findings of a case study project on the way the IMS mapped interactions, interdependencies, constraints and fractal patterns in a complex project. Finally, we discuss how the IMS was utilised as a complex boundary object by eliciting commitment and development of shared mental models, and facilitating negotiation through the layers of multiple interpretations from stakeholders.

A multi-level model of leadership in complex project management

Management (or perceived mismanagement) of large-scale, complex projects poses special problems and often results in spectacular failures, cost overruns, time blowouts and stakeholder dissatisfaction. While traditional project management responds with increasingly administrative constraints, we argue that leaders of such projects also need to display adaptive and enabling behaviours to foster adaptive processes, such as opportunity recognition, which requires an interaction of cognitive and affective processes of individual, project, and team leader attributes and behaviours. At the core of this model we propose is an interaction of cognitive flexibility, affect and emotional intelligence. The result of this interaction is enhanced leader opportunity recognition that, in turn, facilitates multilevel outcomes.

Person-environment fit: project leader-stakeholder relationships in a complex project environment

In this paper, we develop a conceptual model to explore the perceived complementary congruence between complex project leaders and the demands of the complex project environment to understand how leaders’ affective and behavioural performance at work might be impacted by this fit. We propose that complex project leaders high in emotional intelligence and cognitive flexibility should report a higher level of fit between themselves and the complex project environment. This abilities-demands measure of fit should then relate to affective and behavioural performance outcomes, such that leaders who perceive a higher level of fit should establish and maintain more effective, higher quality project stakeholder relationships than leaders who perceive a lower level of fit.

Creative industry clusters in Shanghai : a success story?

In 2005 the Shanghai municipal government adopted the notion of ‘creative industries’ as part of their economic development strategy. At the same time, they officially recognized a number of ‘Creative Industry Clusters’ (CIC) in the city; over the next five years these official clusters grew to over ninety in number. The active promotion of CIC by the local state can thus been seen as central to its adoption of the creative industries agenda, in turn part of its aspiration to become a modern, global metropolis. In the first part of this paper we look at the emergence of the creative industry agenda in China, making some general observations about the need to place such policy transfer in its specific context. We suggest how this agenda might be understood in the national context of China’s economic and cultural policy development. In the second we give a critical account of the development of the creative industries agenda in Shanghai and its relationship to that for CIC. We argue that this agenda had more to do with real estate development than the promotion of a ‘creative milieu’ or ‘ecosystem’, and we also give some reasons as to why this was the case. In the third section we provide some new evidence to suggest the increasing disjunction between CIC and such a wider ‘creative milieu’. In the final section we suggest some new ways in which these CIC might be approached by local government in Shanghai .

designing creative clusters : some indicators

This paper explores how the design of creative clusters as a key strategy in promoting the urban creative economy has played out in Shanghai. Creative Clusters in Europe and North America context have emerged ‘organically’. They developed spontaneously in those cities which went through a period of post-industrial decline. Creative Industries grew up in these cities as part of a new urban economy in the wake of old manufacturing industries. Artists and creative entrepreneurs moved into vacant warehouses and factories and began the trend of ‘creative clusters’. Such clusters facilitate the transfer of tacit knowledge through informal learning, the efficient sourcing of skills and information, competition, collaboration and learning, inter-cluster trading and networking. This new urban phenomenon was soon targeted by local economic development policy in charge of re-generating and re-structuralizing industrial activities in cities. Rising interest from real estate and local economic development has led to more and more planned creative clusters. In the aim of catching up with the world’s creative cities, Shanghai has planned over 100 creative clusters since 2005. Along with these officially designed creative clusters, there are organically emerged creative clusters that are much smaller in scale and much more informal in terms of the management. And they emerged originally in old residential areas just outside the CBD and expand to include French concession the most sort after residential area at the edge of CBD. More recently, office buildings within CBD are made available for creative usages. From fringe to CBD, these organic creative clusters provide crucial evidences for the design of creative clusters. This paper will be organized in 2 parts. In the first part, I will present a case study of 8 ‘official’ clusters (title granted by local govenrment) in Shanghai through which I am hoping to develop some key indicators of the success/failure of creative clusters as well as link them with their physical, social and operational efficacies. In the second part, a variety of ‘alternative’ clusters (organicly formed clusters most of which are not recongnized by the government) supplies with us the possibilities of rethinking the so-called ‘cluster development strategy’ in terms of what kind of spaces are appropriate for use by clusters? Who should manage them and in what format? And ultimately what are their relationship with the rest of the city should be defined?

Fuzzy methods for analysis of microarrays and networks

Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities.

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.

Chaos or complex systems? Identifying factors influencing the success of international and NESB graduate research students in Engineering and Information Technology Fields

The paper explores the results an on-going research project to identify factors influencing the success of international and non-English speaking background (NESB) gradúate students in the fields of Engineering and IT at three Australian universities: the Queensland University of Technology (QUT), the University of Western Australia (UWA), and Curtin University (CU). While the larger study explores the influence of factors from both sides of the supervision equation (e.g., students and supervisors), this paper focusses primarily on the results of an online survey involving 227 international and/or NESB graduate students in the areas of Engineering and IT at the three universities. The study reveals cross-cultural differences in perceptions of student and supervisor roles, as well as differences in the understanding of the requirements of graduate study within the Australian Higher Education context. We argue that in order to assist international and NESB research students to overcome such culturally embedded challenges, it is important to develop a model which recognizes the complex interactions of factors from both sides of the supervision relationship, in order to understand this cohort‟s unique pedagogical needs and develop intercultural sensitivity within postgraduate research supervision.

Cyclin-dependent kinase-mediated phosphorylation of RBP1 and pRb promotes their dissociation to mediate release of the SAP30·mSin3·HDAC transcriptional repressor complex

Eukaryotic cell cycle progression is mediated by phosphorylation of protein substrates by cyclin-dependent kinases (CDKs). A critical substrate of CDKs is the product of the retinoblastoma tumor suppressor gene, pRb, which inhibits G1-S phase cell cycle progression by binding and repressing E2F transcription factors. CDK-mediated phosphorylation of pRb alleviates this inhibitory effect to promote G1-S phase cell cycle progression. pRb represses transcription by binding to the E2F transactivation domain and recruiting the mSin3·histone deacetylase (HDAC) transcriptional repressor complex via the retinoblastoma-binding protein 1 (RBP1). RBP1 binds to the pocket region of pRb via an LXCXE motif and to the SAP30 subunit of the mSin3·HDAC complex and, thus, acts as a bridging protein in this multisubunit complex. In the present study we identified RBP1 as a novel CDK substrate. RBP1 is phosphorylated by CDK2 on serines 864 and 1007, which are N- and C-terminal to the LXCXE motif, respectively. CDK2-mediated phosphorylation of RBP1 or pRb destabilizes their interaction in vitro, with concurrent phosphorylation of both proteins leading to their dissociation. Consistent with these findings, RBP1 phosphorylation is increased during progression from G 1 into S-phase, with a concurrent decrease in its association with pRb in MCF-7 breast cancer cells. These studies provide new mechanistic insights into CDK-mediated regulation of the pRb tumor suppressor during cell cycle progression, demonstrating that CDK-mediated phosphorylation of both RBP1 and pRb induces their dissociation to mediate release of the mSin3·HDAC transcriptional repressor complex from pRb to alleviate transcriptional repression of E2F.

The reliability, accuracy and minimal detectable difference of a multi-segment kinematic model of the foot-shoe complex

Kinematic models are commonly used to quantify foot and ankle kinematics, yet no marker sets or models have been proven reliable or accurate when wearing shoes. Further, the minimal detectable difference of a developed model is often not reported. We present a kinematic model that is reliable, accurate and sensitive to describe the kinematics of the foot–shoe complex and lower leg during walking gait. In order to achieve this, a new marker set was established, consisting of 25 markers applied on the shoe and skin surface, which informed a four segment kinematic model of the foot–shoe complex and lower leg. Three independent experiments were conducted to determine the reliability, accuracy and minimal detectable difference of the marker set and model. Inter-rater reliability of marker placement on the shoe was proven to be good to excellent (ICC = 0.75–0.98) indicating that markers could be applied reliably between raters. Intra-rater reliability was better for the experienced rater (ICC = 0.68–0.99) than the inexperienced rater (ICC = 0.38–0.97). The accuracy of marker placement along each axis was <6.7 mm for all markers studied. Minimal detectable difference (MDD90) thresholds were defined for each joint; tibiocalcaneal joint – MDD90 = 2.17–9.36°, tarsometatarsal joint – MDD90 = 1.03–9.29° and the metatarsophalangeal joint – MDD90 = 1.75–9.12°. These thresholds proposed are specific for the description of shod motion, and can be used in future research designed at comparing between different footwear.

Complex events in business processes

Flow-oriented process modeling languages have a long tradition in the area of Business Process Management and are widely used for capturing activities with their behavioral and data dependencies. Individual events were introduced for triggering process instantiation and activities. However, real-world business cases drive the need for also covering complex event patterns as they are known in the field of Complex Event Processing. Therefore, this paper puts forward a catalog of requirements for handling complex events in process models, which can be used as reference framework for assessing process definition languages and systems. An assessment of BPEL and BPMN is provided.