Strategic people management of coal mining firms in Central Queensland

Purpose – This paper aims to propose a conceptual framework to explore the link between strategic human resource management (SHRM) and firm performance of the coal mining companies in Central Queensland (CQ), Australia.

Design/methodology/approach – The paper reviews literature relating to the process and issues of transforming human resource practices and industrial relations of the coal industry in Australia for the past decade. Theoretical development and empirical studies on the SHRM-performance linkage are discussed. Based on the literature review, the paper develops an integrated model for testing the relationship between SHRM and firm performance in the context of CQ's coalmines and proposes a number of research propositions.

Findings – Three perceivable outcomes are likely derived from application of this framework in the field. First, a testing of the linkage between strategic HRM and firm performance in the coal industry, using an integrated approach, would complement the empirical deficiency of treatments on the prior SHRM models. Second, data at firm level could be collected to develop a better understanding of how the adoption of strategic HRM practices in coal companies can affect firm performance. Third, the extent of flexibility practices, use of contractors and associated management practices could be identified.

Originality/value – The coal industry is central to economic development of regional Queensland. The industry contributes substantially to GDP via employment, investment and product export. An exploration of the impact of SHRM on the coal industry will likely result in identifying some best practices that could be potentially adopted in the wider business community to foster regional economic development in Australia and worldwide.

Mining multi-modal crime patterns at different levels of granularity using hierarchical clustering

The appearance of patterns could be found in different modalities of a domain, where the different modalities refer to the data sources that constitute different aspects of a domain. Particularly, the domain of our discussion refers to crime and the different modalities refer to the different data sources such as offender data, weapon data, etc. in crime domain. In addition, patterns also exist in different levels of granularity for each modality. In order to have a thorough understanding a domain, it is important to reveal the hidden patterns through the data explorations at different levels of granularity and for each modality. Therefore, this paper presents a new model for identifying patterns that exist in different levels of granularity for different modes of crime data. A hierarchical clustering approach - growing self organising maps (GSOM) has been deployed. Furthermore, the model is enhanced with experiments that exhibit the significance of exploring data at different granularities.

A hermeneutic study of pattern mining as a knowledge creation process : exploring multimedia design practices

In this paper, we discuss a special case of knowledge creation via pattern mining that was studied using a hermeneutic approach. The reported study explores the nature of knowledge creation by domain practitioners who do not communicate directly. The focus of this paper extends the traditional view of a knowledge creation process beyond organisational boundaries. The proposed knowledge creation framework explains the facilitated process of knowledge creation by its qualification, combination, socialisation, externalisation, internalisation and introspection, thus allowing the transformation of individual experience and knowledge into formalised shareable domain knowledge.

Advanced graph mining methods for protein analysis

As one of the primary substances in a living organism, protein defines the character of each cell by interacting with the cellular environment to promote the cell’s growth and function [1]. Previous studies on proteomics indicate that the functions of different proteins could be assigned based upon protein structures [2,3]. The knowledge on protein structures gives us an overview of protein fold space and is helpful for the understanding of the evolutionary principles behind structure. By observing the architectures and topologies of the protein families, biological processes can be investigated more directly with much higher resolution and finer detail. For this reason, the analysis of protein, its structure and the interaction with the other materials is emerging as an important problem in bioinformatics. However, the determination of protein structures is experimentally expensive and time consuming, this makes scientists largely dependent on sequence rather than more general structure to infer the function of the protein at the present time. For this reason, data mining technology is introduced into this area to provide more efficient data processing and knowledge discovery approaches.

Unlike many data mining applications which lack available data, the protein structure determination problem and its interaction study, on the contrary, could utilize a vast amount of biologically relevant information on protein and its interaction, such as the protein data bank (PDB) [4], the structural classification of proteins (SCOP) databases [5], CATH databases [6], UniProt [7], and others. The difficulty of predicting protein structures, specially its 3D structures, and the interactions between proteins as shown in Figure 6.1, lies in the computational complexity of the data. Although a large number of approaches have been developed to determine the protein structures such as ab initio modelling [8], homology modelling [9] and threading [10], more efficient and reliable methods are still greatly needed.

In this chapter, we will introduce a state-of-the-art data mining technique, graph mining, which is good at defining and discovering interesting structural patterns in graphical data sets, and take advantage of its expressive power to study protein structures, including protein structure prediction and comparison, and protein-protein interaction (PPI). The current graph pattern mining methods will be described, and typical algorithms will be presented, together with their applications in the protein structure analysis.

The rest of the chapter is organized as follows: Section 6.2 will give a brief introduction of the fundamental knowledge of protein, the publicly accessible protein data resources and the current research status of protein analysis; in Section 6.3, we will pay attention to one of the state-of-the-art data mining methods, graph mining; then Section 6.4 surveys several existing work for protein structure analysis using advanced graph mining methods in the recent decade; finally, in Section 6.5, a conclusion with potential further work will be summarized.

Mining characteristic relations bind to RNA secondary structures

The identification of RNA secondary structures has been among the most exciting recent developments in biology and medical science. It has been recognized that there is an abundance of functional structures with frameshifting, regulation of translation, and splicing functions. However, the inherent signal for secondary structures is weak and generally not straightforward due to complex interleaving substrings. This makes it difficult to explore their potential functions from various structure data. Our approach, based on a collection of predicted RNA secondary structures, allows us to efficiently capture interesting characteristic relations in RNA and bring out the top-ranked rules for specified association groups. Our results not only point to a number of interesting associations and include a brief biological interpretation to them. It assists biologists in sorting out the most significant characteristic structure patterns and predicting structurefunction relationships in RNA.

A visual mining system for theme development evolution analysis of scientific literature

Theme development evolution analysis of literature is a significant tool to help the scientific scholars find and study the frontier problems more efficiently. This paper designs and develops a visual mining system for theme development evolution analysis to deal with the large number of literature information. The analysis of related themes based on sub-themes, together with the dynamic threshold strategy are adopted for improving the accuracy of system. Experiments results prove that correlations of themes obtained from the system are accurate and achieve better practical effect in comparison with that of our early work.

Study on student score based on data mining

Education is a complex systematic engineering, which is the guarantee of training high-quality talent, helping society make full use of educational outcomes and promote the healthy development of education. In the education, the students' score is a very important quantitative evaluation indicator, which can objectively reflect the effects of educational system and is an important basis to make lots of scientific decisions. This paper uses clustering algorithm and decision tree to comprehensively analyze the students' score, and obtains useful results. It can be observed that the results are valuable for the teaching and management.

Prediction of wool knitwear pilling propensity using data mining techniques

This thesis examined the application of data mining techniques to the issue of predicting pilling propensity of wool knitwear. Using real industrial data, a pilling propensity prediction tool with embedded trained support vector machines is developed to provide high accuracy prediction to wool knitwear even before the yarn is spun!

From training to artisanal practice : rethinking choreographic relationships in modern dance

In the first part of the twentieth century early modern dancers created both a new art form and the forms of group social organisation that were its condition of possibility. This paper critically examines the balletic and disciplinary ‘training’ model of dancer formation and proposes that the assumption of training in dance can obscure other ways of understanding dance-making relationships and other values in early modern dance. An ‘artisanal’ mode of production and knowledge transmission based on a non-binary relationship between ‘master’ and apprentice and occurring in a quasi-domestic and personalised space of some intimacy is proposed as a more pertinent way to think the enabling conditions of modern dance creation.

Electrowetting measurements with mercury showing mercury/mica interfacial energy depends on charging

We demonstrate that the interfacial energy between mercury and mica is a function of charge on the mercury surface, decreasing with increasing positive charge. The contact angle of mercury on mica has been measured as a function of potential applied to the mercury, which forms the working electrode of a cell containing either KC1 or NaF electrolyte solution. At high negative applied potentials, a stable aqueous film exists between the mercury and mica surface. As potential is made less negative, the film collapses and mercury partial1 wets the mica at a critical potential, close to the electrocapillary maximum. Upon increasing the potential further (making the Hg surface more and more positive), the contact angle measured within the mercury continually decreases. Electrowetting with mercury is not unexpected since its interfacial tension with the aqueous phase is known to be a function of applied potential. However, the observed decrease goes against the trend expected from the Young equation if only this effect is considered. To explain the data we must allow the mercury/mica interfacial tension also to vary with applied potential. This variation indicates that the mercury surface is positively charged by contact with mica, consistent with known contact electrification between these two materials. The inherent charges at the mercury interfaces with mica and electrolyte solution result in contact angle changes of some tens of degrees with a change in applied potential of half a volt orders of magnitude less than the potentials required to effect comparable changes in other electrowetting systems.

Self-assembled monolayers on mercury probed with a modified surface force apparatus

Self-assembled monolayers (SAMs) of three thiol compounds formed on mercury are investigated by a combination of cyclic voltammetry, electrocapillary curves, and a novel method of measuring electrical doublelayer properties. The last method involves a modified surface force apparatus in which a flat mica surface is pressed down toward a fixed mercury drop held beneath it, while both are immersed in aqueous electrolyte solution. Optical interference measurements are made of the mica-mercury separation as a function of electrical potential applied to the mercury, which yields information on the double-layer interaction between the two surfaces. Mercury is decorated by SAMs of 11-mercapto-1-undecanoic acid, which is shown to bring negative charge to the mercury/aqueous interface due to dissociation of the carboxylic acid groups; 11-mercapto-1- undecanol, which although it is uncharged changes the dipole potential of the interface; and 1-undecanethiol, which likewise changes the dipole potential, but by a different amount. The difference between the changes in dipole potential (90 mV) can be related to the different terminal groups of these two SAMs, -CH3 compared to -OH, that are in contact with the aqueous phase.

Measurement of aqueous film thickness between charged mercury and mica surfaces : a direct experimental probe of the Poisson-Boltzmann distribution

A stable aqueous electrolyte film is formed between a mercury drop and a flat mica surface due to electrical double-layer repulsion when a negative potential is applied to the mercury. Film thickness has been measured as a function of applied potential while keeping the film pressure constant. By making measurements in this way, it is possible to map the data directly according to the Poisson-Boltzmann equation. An excellent fit to the data is obtained, providing direct evidence for this classical equation and its use as the basis of the Gouy-Chapman model of the diffuse double layer in electrolyte solutions.

Data mining techniques for the assessment of factors contributing to the damage of residential houses in Australia

This paper reports on the preparation and management processes of inconsistent data on damage on residential houses in Victoria, Australia. There are no existing specific and fully relevant databases readily available except for the incomplete paper-based and electronic-based reports. Therefore, the extracting of information from the reports is complicated and time consuming in order to extract and include all the necessary information needed for analysis of damage on residential houses founded on expansive soils. Data mining is adopted to develop a database. Statistical methods and Artificial Intelligence methods are used to quantify the quality of data. The paper concludes that the development of such database could enable BHC to evaluate the usefulness of the reports prepared on the reported damage properties for further analysis.