Managerialism and the arts : a complete and utter bloody waste of time

Managerialism has been adopted with alacrity by Australian government agencies across multiple sectors. Studies of managerialism in concept and practice have been undertaken in some public sectors but not from a critical management perspective. This position paper examines the contribution of organization theory to an understanding of managerialism. Here we challenge the appropriateness and effectiveness of new managerialism generally, and for the arts in particular, through an analysis of conflict between an Artistic Director, General Manager and Board in a key Australian community arts organization. We call for further research into the appropriateness of management theory and practice for the arts; and seek better ways of managing our cultural capital.

Identification of moving obstacles with pyramidal Lucas Kanade optical flow and k means clustering

This paper describes the methodology for identifying moving obstacles by obtaining a reliable and a sparse optical flow from image sequences. Given a sequence of images, basically we can detect two-types of on road vehicles, vehicles traveling in the opposite direction and vehicles traveling in the same direction. For both types, distinct feature points can be detected by Shi and Tomasi corner detector algorithm. Then pyramidal Lucas Kanade method for optical flow calculation is used to match the sparse feature set of one frame on the consecutive frame. By applying k means clustering on four component feature vector, which are to be the coordinates of the feature point and the two components of the optical flow, we can easily calculate the centroids of the clusters and the objects can be easily tracked. The vehicles traveling in the opposite direction produce a diverging vector field, while vehicles traveling in the same direction produce a converging vector field

A spectroscopy of texts for effective clustering

For many clustering algorithms, such as k-means, EM, and CLOPE, there is usually a requirement to set some parameters. Often, these parameters directly or indirectly control the number of clusters to return. In the presence of different data characteristics and analysis contexts, it is often difficult for the user to estimate the number of clusters in the data set. This is especially true in text collections such as Web documents, images or biological data. The fundamental question this paper addresses is: ldquoHow can we effectively estimate the natural number of clusters in a given text collection?rdquo. We propose to use spectral analysis, which analyzes the eigenvalues (not eigenvectors) of the collection, as the solution to the above. We first present the relationship between a text collection and its underlying spectra. We then show how the answer to this question enhances the clustering process. Finally, we conclude with empirical results and related work.

Genetic variation in BEACON influences quantitative variation in metabolic syndrome-related phenotypes

The BEACON gene (also known as UBL5) was identified as differentially expressed between lean and obese Psammomys obesus, a polygenic animal model of obesity, type 2 diabetes, and dyslipidemia. The human homologue of BEACON is located on chromosome 19p, a region likely to contain genes affecting metabolic syndrome–related quantitative traits as established by linkage studies. To assess whether the human BEACON gene may be involved in influencing these traits, we exhaustively analyzed the complete gene for genetic variation in 40 unrelated individuals and identified four variants (three novel). The two more common variants were tested for association with a number of quantitative metabolic syndrome–related traits in two large cohorts of unrelated individuals. Significant associations were found between these variants and fat mass (P = 0.026), percentage of fat (P = 0.001), and waist-to-hip ratio (P = 0.031). The same variants were also associated with total cholesterol (P = 0.024), LDL cholesterol (P = 0.019), triglycerides (P = 0.006), and postglucose load insulin levels (P = 0.018). Multivariate analysis of these correlated phenotypes also yielded a highly significant association (P = 0.0004), suggesting that BEACON may influence phenotypic variation in metabolic syndrome–related traits.

Extending construction linkage measures by the consideration of the impact of capital

Construction linkage is a well-established research field. However, a significant limitation in previous linkage research is that the flow of capital goods is not addressed. Using the OECD input-output tables, this research first generates a new input-output model considering capital as an intermediate factor. Using the new model, the construction linkages are recalculated and investigated in order to evaluate further the role of construction in national economies. The findings verify that traditional construction linkages were extremely underestimated in previous research. Furthermore, the effect of capital on construction shows a declining trend over the examined period. After considering the effect of capital, most values and rankings of backward and forward linkages show a decreasing trend, which confirms the declining role of the construction sector with economic maturity.

Linkage measures of the real estate sector considering the effect of capital

A significant limitation in previous linkage relevant research is that the flow of capital goods is not addressed. Using the OECD input-output tables, this research first generates a new input-output model considering capital as an intermediate factor. Using the new model, the real estate linkages are re-calculated and investigated in order to evaluate appropriately the impact of the real estate sector on national economies. The findings verify that the linkages of the real estate sector were extremely underestimated in previous research. A correct linkage measure of the real estate sector can contribute to produce correct information corresponding to the sectors responsible for the economic growth during the period under study and provide substantial contributions towards guiding the appropriate strategies for future economic development.

Clustering massive high dimensional data with dynamic feature maps

This paper presents an algorithm based on the Growing Self Organizing Map (GSOM) called the High Dimensional Growing Self Organizing Map with Randomness (HDGSOMr) that can cluster massive high dimensional data efficiently. The original GSOM algorithm is altered to accommodate for the issues related to massive high dimensional data. These modifications are presented in detail with experimental results of a massive real-world dataset.

Lymphedema after complete axillary node dissection for melanoma: assessment using a new, objective definition

Objectives: The objectives of this study were to define appropriate criteria for assessing the presence of lymphedema, and to report the prevalence and risk factors for development of upper limb lymphedema after level I-III axillary dissection for melanoma.
Summary Background Data: The lack of a consistent and reliable objective definition for lymphedema remains a significant barrier to appreciating its prevalence after axillary dissection for melanoma (or breast carcinoma).
Methods: Lymphedema was assessed in 107 patients (82 male, 25 female) who had previously undergone complete level I-III axillary dissection. Of the 107 patients, 17 had also received postoperative axillary radiotherapy. Arm volume was measured using a water displacement technique. Change in volume of the arm on the side of the dissection was referenced to the volume of the other (control) arm. Volume measurements were corrected for the effect of handedness using corrections derived from a control group. Classification and regression tree (CART) analysis was used to determine a threshold fractional arm volume increase above which volume changes were considered to indicate lymphedema.
Results: Based on the CART analysis results, lymphedema was defined as an increase in arm volume greater than 16% of the volume of the control arm. Using this definition, lymphedema prevalence for patients in the present study was 10% after complete level I-III axillary dissection for melanoma and 53% after additional axillary radiotherapy. Radiotherapy and wound complications were independent risk factors for the development of lymphedema.
Conclusions: A suggested objective definition for arm lymphedema after axillary dissection is an arm volume increase of greater than 16% of the volume of the control arm.

A clustering based hybrid system for mass spectrometry data analysis

Recently, much attention has been given to the mass spectrometry (MS) technology based disease classification, diagnosis, and protein-based biomarker identification. Similar to microarray based investigation, proteomic data generated by such kind of high-throughput experiments are often with high feature-to-sample ratio. Moreover, biological information and pattern are compounded with data noise, redundancy and outliers. Thus, the development of algorithms and procedures for the analysis and interpretation of such kind of data is of paramount importance. In this paper, we propose a hybrid system for analyzing such high dimensional data. The proposed method uses the k-mean clustering algorithm based feature extraction and selection procedure to bridge the filter selection and wrapper selection methods. The potential informative mass/charge (m/z) markers selected by filters are subject to the k-mean clustering algorithm for correlation and redundancy reduction, and a multi-objective Genetic Algorithm selector is then employed to identify discriminative m/z markers generated by k-mean clustering algorithm. Experimental results obtained by using the proposed method indicate that it is suitable for m/z biomarker selection and MS based sample classification.

Data pre-processing for more effective gene clustering

The high-throughput experimental data from the new gene microarray technology has spurred numerous efforts to find effective ways of processing microarray data for revealing real biological relationships among genes. This work proposes an innovative data pre-processing approach to identify noise data in the data sets and eliminate or reduce the impact of the noise data on gene clustering, With the proposed algorithm, the pre-processed data sets make the clustering results stable across clustering algorithms with different similarity metrics, the important information of genes and features is kept, and the clustering quality is improved. The primary evaluation on real microarray data sets has shown the effectiveness of the proposed algorithm.

Getting the complete picture : combining parental and child data to identify the barriers to social inclusion for children living in low socio-economic areas

Background: Childhood mental health problems are prevalent in Australian children (14–20%). Social exclusion is a risk factor for mental health problems, whereas being socially included can have protective effects. This study aims to identify the barriers to social inclusion for children aged 9–12 years living in low socio-economic status (SES) areas, using both child-report and parent-report interviews.

Methods: Australian-born English-speaking parents and children aged 9–12 years were sampled from a low SES area to participate in semi-structured interviews. Parents and children were asked questions around three prominent themes of social exclusion; exclusion from school, social activities and social networks.

Results: Many children experienced social exclusion at school, from social activities or within social networks. Overall, nine key barriers to social inclusion were identified through parent and child interviews, such as inability to attend school camps and participate in school activities, bullying and being left out, time and transport constraints, financial constraints and safety and traffic concerns. Parents and children often identified different barriers.

Discussion: There are several barriers to social inclusion for children living in low SES communities, many of which can be used to facilitate mental health promotion programmes. Given that parents and children may report different barriers, it is important to seek both perspectives.

Conclusion: This study strengthens the evidence base for the investments and action required to bring about the conditions for social inclusion for children living in low SES communities.

The mechanism of metadynamic softening in austenite after complete dynamic recrystallization

A novel mechanism of post-dynamic softening during annealing of a fully dynamically recrystallized (DRX) austenitic Ni–30Fe alloy is proposed. The initial softening stage involves rapid growth of the dynamically formed nuclei and migration of the mobile boundaries. The sub-boundaries within DRX grains progressively disintegrate through dislocation climb and dislocation annihilation, which ultimately leads to the formation of dislocation-free grains, and the grain boundary migration gradually becomes slower. As a result, the DRX texture largely remains preserved throughout the annealing process.

A rough set approach for selecting clustering attribute

A few of clustering techniques for categorical data exist to group objects having similar characteristics. Some are able to handle uncertainty in the clustering process while others have stability issues. However, the performance of these techniques is an issue due to low accuracy and high computational complexity. This paper proposes a new technique called maximum dependency attributes (MDA) for selecting clustering attribute. The proposed approach is based on rough set theory by taking into account the dependency of attributes of the database. We analyze and compare the performance of MDA technique with the bi-clustering, total roughness (TR) and min–min roughness (MMR) techniques based on four test cases. The results establish the better performance of the proposed approach.

An input-output-based linkage measurement framework

This study proposed an input-output-based linkage measurement framework, which is a multi-level hierarchy, omni-direction decision-making model and takes the impact of capital into account. This framework had resolved the critical deficiencies and inherent limitations of the existing methods and was used to explore the real estate and construction linkages.